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AI & the Jobs Market: Which Professions Will Vanish by 2030 ...
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AI & the Jobs Market: Which Professions Will Vanish by 2030 and How to Thrive in the New Reality?
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https://codegym.cc
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[] |
AI might eliminate 85 million jobs in just a year but create 97 million new ones. By 2030, 375 million people will be forced to change careers due to the ...
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The summary:
The Current State of AI Technologies Development
AI Evolution Timeline
2020 , COVID-19, the University of Oxford develops Curial — a rapid AI test for emergency rooms. The AI proves its efficiency in handling extreme situations. Meanwhile, OpenAI releases GPT-3 with 175 billion parameters for humanlike text generation, making a breakthrough in NLP.
, COVID-19, the University of Oxford develops Curial — a rapid AI test for emergency rooms. The AI proves its efficiency in handling extreme situations. Meanwhile, OpenAI releases GPT-3 with 175 billion parameters for humanlike text generation, making a breakthrough in NLP. 2022 — OpenAI launches ChatGPT, and five days after the launch, the app acquired over 1 million users. Thus, the greatest exposure to using AI on a daily basis begins.
The Numbers Behind the Top AI Tools
24 Billion: the number of online visits generated by the top 50 AI tools in 1 year
the number of online visits generated by the top 50 AI tools in 1 year 14 Billion: the number of visits generated by ChatGPT alone during that period
the number of visits generated by ChatGPT alone during that period 5.5 Billion: the number of visits generated by the US, followed by India (2.1 billion) and Indonesia (1.4 billion)
The Increasing Number of Open Source AI
AI Adoption Across Industries: Which Ones Are Ahead
Making The Headlines
Airbnb acquires an AI startup for close to $200 million to build the ‘ultimate travel agent”,
Google’s AI weather forecaster beats a global standard and gives more accurate forecasts than Europe’s leading weather center,
Industries That Prioritize The Implementation of AI in Their Operations
The retail sector leads in AI implementation, with 72% of retailers using AI. 80% of retail executives expect to adopt AI automation by 2025,
68% of healthcare organizations use AI technologies,
63% of IT and telecom companies utilize AI,
44% of automotive organizations implement AI. By 2035, 75% of vehicles will be equipped with AI technology,
25% of travel and hospitality companies already use chatbots and other AI technologies.
Impact Of AI On Jobs And Employment: Who’s at Risk
Predictions about the impact of AI on the global job market in 2024-2025
By 2025, AI will eliminate 85 million jobs. Instead, it’ll create 97 million new ones,
AI and ML might replace 16% of US jobs by 2025,
Content creators and entry-level professionals across different industries believe their jobs are at risk because of AI.
What's next? Key AI Predictions Up to 2030
Intelligent robots might take the place of 30% of the global workforce.
375 million professionals may change careers by 2030.
Nearly a third of UK jobs could be replaced by AI in the 2030s.
Professions That Are Already at Risk.
Entry-level data analysts and researchers. AI can overtake manual and repetitive data entry tasks, minimizing human errors and allowing specialists to focus on strategic tasks.
AI can overtake manual and repetitive data entry tasks, minimizing human errors and allowing specialists to focus on strategic tasks. Customer service specialists. AI-driven tools, such as chatbots, automated phone systems, and knowledge-based systems (F.A.Qs, interactive troubleshooting guides), have successfully replaced the L1 support in service companies.
AI-driven tools, such as chatbots, automated phone systems, and knowledge-based systems (F.A.Qs, interactive troubleshooting guides), have successfully replaced the L1 support in service companies. Entry-level finance and accounting specialists. Potentially automated tasks include data collection, data entry, categorization, reconciliation, and invoicing.
Potentially automated tasks include data collection, data entry, categorization, reconciliation, and invoicing. Entry-level legal workers. This scope of tasks includes document automation and contract review.
This scope of tasks includes document automation and contract review. Travel agents. As automation tools become more advanced, many booking services are removed from the agents’ hands.
The Future of AI-Influenced Job Market: Which Professions Will Be Beneficial
Enhanced productivity in daily tasks: forget about manual tasks and focus on strategic and complex tasks.
forget about manual tasks and focus on strategic and complex tasks. More means of expression for creation: use AI for drafting and brainstorming.
use AI for drafting and brainstorming. New job sectors and career perspectives: AI-based digital assistants, smart devices, and robotic systems require specialists in development and support.
AI-based digital assistants, smart devices, and robotic systems require specialists in development and support. Easier upskilling: Implementing AI in businesses requires lifelong learning. At the same time, utilizing AI to gain new skills makes the education process far more efficient.
Implementing AI in businesses requires lifelong learning. At the same time, utilizing AI to gain new skills makes the education process far more efficient. Elevation of human expertise: there are plenty of professions that require creativity, empathy, strategic thinking, and human judgment that are less susceptible to being replaced by AI.
New professions that will emerge thanks to AI
Prompt engineers, who are skilled at the development and refinement of prompts for apps like ChatGPT to achieve the desired output,
who are skilled at the development and refinement of prompts for apps like ChatGPT to achieve the desired output, AI sales and marketing specialists who know the AI capabilities, can explain the benefits and “market” the AI solutions to businesses and consumers,
can explain the benefits and “market” the AI solutions to businesses and consumers, AI content editors and fact-checkers will be responsible for reviewing AI-generated content to ensure it is accurate, aligns with the company’s tone of voice, and free from biases.
will be responsible for reviewing AI-generated content to ensure it is accurate, aligns with the company’s tone of voice, and free from biases. Etc.
According to data from the World Economic Forum report, by 2030, 6-9% of people will be employed in jobs that do not exist today because of the impact of AI and automation. Now is the best time to embrace those changes and make the decision for your future career.
Industries in which automation will create the most job openings
Wrap-up
Shortly, developers will be in the highest demand.
How long does it take to become proficient in software development? You can make it by 2025
What are the best programming languages for software and AI development?
AI might eliminate 85 million jobs in just a year but create 97 million new ones. By 2030, 375 million people will be forced to change careers due to the implementation of AI in business processes across all industries. If you’re unsure how to feel about these facts — panic or rejoice because of the new opportunities — let’s examine today the transformation of the jobs market due to AI development. We’ll review the current state of AI & business synergy and get to the point where we decide on the most and the least promising careers.Aside from tech enthusiasts who’d taken an interest in AI a long time ago, most people barely paid attention to this field until the introduction of ChatGPT and the increasing information bubble about the threats of AI taking our jobs. However, AI has been around for quite some time and has been widely used in research, education, and business processes for many years already:So, what has changed recently? Let’s pinpoint two significant milestones:Companies that develop AI products attract massive investments, while tech giants reallocate their budgets to in-house AI development, starting the “arms race” in the age of AI.Here are just a few numbers for you to understand how huge the AI trend is today:From multitool like ChatGPT to highly specialized products like Grammarly for improving your writing and Respeecher for speech processing, AI companies are emerging all over the globe:What’s more important is that since 2020, there’s been a spike in AI development that is beneficial for business processes and encourages the easy integration of AI into any kind of enterprise.According to GitHub, the number of AI-related GitHub projects has significantly increased, growing from 1,536 in 2011 to 347,934 in 2022. See that spike between 2019 and 2020? Yeah, that’s when the accelerated changes in the perception of AI's influence on the business and the jobs market have begun:Based on the recent news, it may seem that AI is indeed everywhere and inevitably merging into business and governmental action plans:The UK’s Department for Transport wants flying taxis to take off inside the country as soon as 2026, with driverless versions coming several years later.So, are the forecasters, travel agents, and taxi drivers the only ones “threatened” by AI in the near future? Do they need to feel pressured at all? To predict these kinds of changes, it’s wise to consider industry trends and the level of economic development in various countries.As for now, the leading industries in AI adoption are retail, IT&Telecom, healthcare, automotive industry, and travel & hospitality:However, cross-industry data proves that the increasing adoption of AI is just a matter of time for almost any field. Executives see the potential of utilizing these technologies to transform their business processes. A third of businesses already use AI, and 9 of 10 companies believe that AI can gain them an edge over competitors. Nearly 4 out of 5 companies prioritized AI in business strategy.All in all, AI influence is inevitable in any business field. Which brings us to the main question: How will it affect human professionals? Will AI replace jobs?First and most importantly, advanced economies are prone to transform businesses with the help of AI faster than emerging and low-income countries:This means the automation trend may affect 38% of US jobs, 35% in Germany, 30% in the UK, and 21% in Japan.AI has a reputation as a productivity booster. However, some roles may be at risk of total automation.As you can see, AI may be a threat only for mundane and manual tasks. And, of course, its development is far from the human level of critical thinking and empathy. If you’re feeling anxious, don’t. Here’s a bonus: the list of 119 Jobs That AI Won't Replace Let’s reiterate the critical thought: AI is not a threat. It’s a resource that gives us access to unlimited opportunities, such as:According to global research from the World Economic Forum , half of organizations believe that AI will create job growth in the next five years. While many jobs are tech-related, non-techies can benefit from AI implementation. A few examples of non-tech emerging roles include:What is the safest option in a rapidly changing, tech-oriented world heavily relying on AI technologies? You know the answer. Become one of those who create new solutions, automate processes, and implement and support new technologies. The number of jobs in Artificial Intelligence and Information Technology (IT) will definitely increase. Data engineers, data scientists, computer vision engineers, and IT professionals specializing in technology development and automation will stay in hot demand by 2030.While AI creates new jobs for non-techies, the greatest share of irreplaceable future careers lies in the tech industry.With AI tools, a developer can create a product that automates the work of dozens, even hundreds, of people. Of course, those who create AI solutions will be the most desired specialists among the top companies.Java, Python, and C++. Since you’re conveniently reading this on a Java learning platform, let’s give Java a shot!
| 2030-07-14T00:00:00 |
2030/07/14
|
https://codegym.cc/groups/posts/25745-ai--the-jobs-market-which-professions-will-vanish-by-2030-and-how-to-thrive-in-the-new-reality
|
[
{
"date": "2024/04/09",
"position": 71,
"query": "AI employment"
},
{
"date": "2024/04/09",
"position": 14,
"query": "AI job creation vs elimination"
},
{
"date": "2024/04/09",
"position": 9,
"query": "AI labor market trends"
}
] |
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] |
AI Will Wipe Out White-Collar Jobs by 2027
|
The End of White Collar Work
|
https://www.wbn.digital
|
[] |
AI experts at Anthropic warn that nearly all white collar jobs could be automated by 2027. With little time left, companies and individuals must urgently ...
|
By Elke Porter | WBN Ai | June 9, 2025
Subscription to WBN and being a Writer is FREE!
In a striking new video from AI Copium, researcher Sholto Douglas of Anthropic predicts that by 2027–2028, AI systems will be capable of automating nearly every white-collar job. And it’s not just a prediction—it’s a warning echoed by other leading voices in the AI space, including Trenton Bricken (Anthropic), David Blondon (Data Stage), and futurist Peter Diamandis.
🔍 Key Takeaways:
White-collar automation is inevitable and imminent. Sholto Douglas claims that with existing models and enough data, we can already automate the majority of white-collar tasks—even if AI development stopped progressing.
Sholto Douglas claims that with existing models and enough data, we can already automate the majority of white-collar tasks—even if AI development stopped progressing. We don’t even need new AI models. According to Douglas and Bricken, current systems, with the right infrastructure and datasets, can handle most tasks done by analysts, consultants, marketers, HR, and even software engineers.
According to Douglas and Bricken, current systems, with the right infrastructure and datasets, can handle most tasks done by analysts, consultants, marketers, HR, and even software engineers. It’s not just technically possible—it’s economically inevitable. Companies will be financially incentivized to adopt AI to stay competitive. Those that don’t will risk being left behind.
Companies will be to adopt AI to stay competitive. Those that don’t will risk being left behind. Job displacement will begin fast and soon. Expect major disruption between 2026 and 2028 . Many workers, including those in Silicon Valley, are already feeling the effects, with layoffs accelerating.
Expect major disruption between . Many workers, including those in Silicon Valley, are already feeling the effects, with layoffs accelerating. Corporations are focused on profit, not people. Engineers who helped build these AI systems are being replaced by them. This cycle is expected to spread across industries.
Engineers who helped build these AI systems are being replaced by them. This cycle is expected to spread across industries. Most people, governments, and companies are unprepared. Experts urge immediate action: employers must train their workers to become AI users now—or risk making them obsolete.
Experts urge immediate action: employers must train their workers to become AI users now—or risk making them obsolete. Unanswered questions remain. What happens when consumers no longer have jobs or income to support the economy? The video notes that even if the economics don't add up in the long term, short-term profit motives will still drive adoption.
⚠️ Final Message:
The video concludes with a sense of urgency: this future is not decades away—it’s a few years out. While AI can improve lives and unlock abundance in medicine and robotics, without preparation, it will first cause mass disruption.
🎙️ Credit:
This summary is based on “AI Copium – Intro”, a video commentary on the future of artificial intelligence and work, featuring Sholto Douglas and Trenton Bricken of Anthropic, with insights from Peter Diamandis and David Blondon.
Why Does It Matter?
The prediction that AI could automate nearly all white-collar jobs by 2027–2030 matters because it signals a rapid and massive shift in the global workforce that few are prepared for. This could lead to widespread job displacement, economic disruption, rising inequality, and social unrest if proactive steps aren't taken. It’s a critical moment to rethink education, upskilling, and public policy to ensure AI enhances human potential rather than replaces it entirely.
🎙️ Bottom Line:
This isn’t just a temporary downturn — it’s a fundamental reset of the workforce. The future belongs to those who adapt.
Connect with Elke at Westcoast German Media or on LinkedIn: Elke Porter or contact her on WhatsApp: +1 604 828 8788
TAGS: #AI Job Disruption #Future Of Work #White Collar AI #AI Revolution #Anthropic Insights #Automation 2027 #WBN News Vancouver #Elke Porter
| 2027-08-07T00:00:00 |
2027/08/07
|
https://www.wbn.digital/ai-will-wipe-out-white-collar-jobs-by-2027-and-no-ones-ready/
|
[
{
"date": "2025/06/09",
"position": 61,
"query": "artificial intelligence blue collar workers"
},
{
"date": "2025/06/09",
"position": 38,
"query": "artificial intelligence white collar workers"
}
] |
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] |
Enhancing AI literacy in undergraduate pre-medical education ...
|
Enhancing AI literacy in undergraduate pre-medical education through student associations: an educational intervention - BMC Medical Education
|
https://bmcmededuc.biomedcentral.com
|
[
"Hopson",
"Department Of Physics",
"Astronomy",
"Brigham Young University",
"Provo",
"Mildon",
"Hassard",
"Kubalek",
"Laverty",
"Urie"
] |
However, a gap exists in formal AI education for pre-medical students. This study evaluates the effectiveness of the AI in Medicine Association ...
|
Study design
This study employed a quasi-experimental, pretest-posttest control group design to evaluate the educational impact of a student-led AI curriculum. Participants were assigned to either an intervention group, which completed the curriculum through the AI in Medicine Association (AIM), or a control group, which did not receive the intervention. Outcomes were assessed through knowledge gains in AI and pathology-related content.
Participants and recruitment
Participants were undergraduate students at BYU pursuing pre-medical or other pre-health career paths. Cohort participants were recruited via flyers, campus bulletins, and an in-person booth at BYU’s student center. Control group participants were recruited through in-person outreach in large, pre-med-focused courses. Inclusion criteria required current enrollment in pre-medical coursework and interest in healthcare careers. Students with prior formal AI training beyond the scope of the AIM curriculum were excluded.
Intervention
The AIM curriculum consisted of four weekly educational sessions incorporating principles of active learning and multimodal instruction, as recommended in the literature [10,11,12,13]. The curriculum integrated foundational AI concepts, ethical frameworks, hands-on engagement with histological image analysis, and exposure to current AI research. Specific topics included:
1. Introduction to AI in healthcare. 2. Ethical implications and responsible use of AI. 3. Data preprocessing techniques. 4. Model development, training, and evaluation.
Instructional materials were designed by AIM student officers and reviewed by a senior faculty member to ensure academic rigor and pedagogical clarity. A key project involved identifying cancerous tissue and histological features in whole-slide prostate images, fostering practical engagement with AI in pathology [20].
Assessment design
The primary assessment tool consisted of a structured survey administered both pre- and post-intervention. It began with six demographic/background questions (e.g., prior AI exposure, graduation year, attitudes toward AI) followed by ten knowledge items divided into two domains:
AI and Data Science (5 questions).
Pathology and AIM Projects (5 questions).
Item formats included multiple-choice and short-answer questions. A complete version of the assessment is available in Supplementary Materials.
Data collection
Both the intervention and control groups completed the same assessment before and after a four-week period. Assessments were conducted in supervised or distraction-free environments. Only cohort participants who completed all four AIM sessions were eligible for the posttest. Control participants received their posttest four weeks after the pretest, with no exposure to the intervention.
To reduce response bias, participants were assured that their responses would remain anonymous, were not graded for performance, and could omit questions if desired. Identifying information was removed from scores once collected, and only used to contact students if needed, to link pre- and post-tests to the same student, and to collect demographic information that was not collected in the survey such as the sex of the participant. Participation was voluntary, and all students were informed they could withdraw at any time. Those who opted out of the study were still allowed to engage fully in AIM activities. Data were collected over two academic semesters and included 15 intervention participants and 16 controls.
Grading and scoring
Responses were graded by a faculty member using a structured rubric to ensure consistency and minimize bias. Partial credit was awarded for partially correct answers, with specific criteria delineated in the rubric (see Supplementary Materials).
Statistical analysis
Descriptive statistics (means, standard deviations, medians, frequencies, and percentages) were used to summarize demographic variables and baseline characteristics. Between-group comparisons at baseline were assessed using chi-square tests (categorical variables) and independent t-tests or Mann-Whitney U tests (continuous variables).
To assess knowledge gains, within-group changes in pretest and posttest scores were analyzed using paired t-tests (or Wilcoxon signed-rank tests if data were non-normally distributed). Between-group comparisons of posttest scores were analyzed using independent t-tests or Mann-Whitney U tests.
Effect sizes were calculated using Cohen’s d. Additionally, an analysis of covariance was conducted to compare posttest scores between groups while controlling for pretest scores and potential confounders (e.g., prior AI experience, pre-med status).
Exploratory analyses included Pearson or Spearman correlations between prior AI experience and knowledge gains. A multiple regression model was used to identify predictors of improvement, with group assignment, prior AI experience, and baseline attitudes toward AI entered as independent variables.
Data visualization
To visualize score distributions and changes, violin plots were generated for pre- and posttest scores by group. Bar plots with error bars depicted mean score changes and standard deviations across AI, pathology, and combined domains. Scatter plots explored associations between prior experience or attitudes and test performance. All figures were generated using Python packages seaborn and matplotlib.
Ethical considerations
The study received approval from the Brigham Young University Institutional Review Board. All participants provided informed consent, and data confidentiality was maintained via secure storage and anonymized analysis (see Supplementary Material).
| 2025-12-14T00:00:00 |
2025/12/14
|
https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-025-07556-2
|
[
{
"date": "2022/12/01",
"position": 86,
"query": "AI education"
},
{
"date": "2023/03/01",
"position": 83,
"query": "AI education"
},
{
"date": "2023/06/01",
"position": 84,
"query": "AI education"
},
{
"date": "2023/09/01",
"position": 85,
"query": "AI education"
},
{
"date": "2023/11/01",
"position": 86,
"query": "AI education"
},
{
"date": "2024/01/01",
"position": 89,
"query": "AI education"
},
{
"date": "2024/03/01",
"position": 87,
"query": "AI education"
},
{
"date": "2024/05/01",
"position": 90,
"query": "AI education"
},
{
"date": "2024/07/01",
"position": 88,
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},
{
"date": "2024/10/01",
"position": 88,
"query": "AI education"
},
{
"date": "2024/11/01",
"position": 87,
"query": "AI education"
}
] |
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] |
Implementing Artificial Intelligence in Critical Care Medicine
|
Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22 - Critical Care
|
https://ccforum.biomedcentral.com
|
[
"Cecconi",
"Humanitas University",
"Milan",
"Irccs Humanitas Research Hospital",
"Greco",
"Shickel",
"Intelligent Clinical Care Center",
"University Of Florida",
"Gainesville",
"Department Of Medicine"
] |
A social contract for AI in healthcare should define clear roles and responsibilities for all stakeholders—clinicians, patients, developers, ...
|
Artificial intelligence (AI) is rapidly entering critical care, where it holds the potential to improve diagnostic accuracy and prognostication, streamline intensive care unit (ICU) workflows, and enable personalized care. [1, 2] Without a structured approach to implementation, evaluation, and control, this transformation may be hindered or possibly lead to patient harm and unintended consequences.
Despite the need to support overwhelmed ICUs facing staff shortages, increasing case complexity, and rising costs, most AI tools remain poorly validated and untested in real settings. [3, 45]
To address this gap, we issue a call to action for the critical care community: the integration of AI into the ICU must follow a pragmatic, clinically informed, and risk-aware framework. [6,7,8] As a result of a multidisciplinary consensus process with a panel of intensivists, AI researchers, data scientists and experts, this paper offers concrete recommendations to guide the safe, effective, and meaningful adoption of AI into critical care.
Methods
The consensus presented in this manuscript emerged through expert discussions, rather than formal grading or voting on evidence, in recognition that AI in critical care is a rapidly evolving field where many critical questions remain unanswered. Participants were selected by the consensus chairs (MC, AB, FT, and JLV) based on their recognized contributions to AI in critical care to ensure representation from both clinical end-users and AI developers. Discussions were iterative with deliberate engagement across domains, refining recommendations through critical examination of real-world challenges, current research, and regulatory landscapes.
While not purely based on traditional evidence grading, this manuscript reflects a rigorous, expert-driven synthesis of key barriers and opportunities for AI in critical care, aiming to bridge existing knowledge gaps and provide actionable guidance in a rapidly evolving field. To guide physicians in this complex and rapidly evolving arena [9], some of the current taxonomy and classifications are reported in Fig. 1.
Fig. 1 Taxonomy of AI in critical care Full size image
Main barriers and challenges for AI integration in critical care
The main barriers to AI implementation in critical care determined by the expert consensus are presented in this section. These unresolved and evolving challenges have prompted us to develop a series of recommendations to physicians and other healthcare workers, patients, and societal stakeholders, emphasizing the principles we believe should guide the advancement of AI in healthcare. Challenges and principles are divided into four main areas, 1) human-centric AI; 2) Recommendation for clinician training on AI use; 3) standardization of data models and networks and 4) AI governance. These are summarized in Fig. 2 and discussed in more detail in the next paragraphs.
Fig. 2 Recommendations, according to development of standards for networking, data sharing and research, ethical challenges, regulations and societal challenges, and clinical practice Full size image
The development and maintenance of AI applications in medicine require enormous computational power, infrastructure, funding and technical expertise. Consequently, AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems [10, 11]. The rapid diffusion of new AI models contrasts sharply with the evidence-based culture of medicine. This raises concerns about the deployment of insufficiently validated clinical models. [12]
Moreover, many models are developed using datasets that underrepresent vulnerable populations, leading to algorithmic bias. [13] AI models may lack both temporal validity (when applied to new data in a different time) and geographic validity (when applied across different institutions or regions). Variability in temporal or geographical disease patterns including demographics, healthcare infrastructure, and the design of Electronic Health Records (EHR) further complicates generalizability.
Finally, the use of AI raises ethical concerns, including trust in algorithmic recommendations and the risk of weakening the human connection at the core of medical practice, which is the millenary relation between physicians and patients. [14]
Recommendations
Here we report recommendations, divided in four domains. Figure 3 reports a summary of five representative AI use cases in critical care—ranging from waveform analysis to personalized clinician training—mapped across these four domains.
Fig. 3 Summary of five representative AI use cases in critical care—ranging from waveform analysis to personalized clinician training—mapped across these 4 domains Full size image
Strive for human-centric and ethical AI utilization in healthcare
Alongside its significant potential benefit, the risk of AI misuse cannot be underestimated. AI algorithms may be harmful when prematurely deployed without adequate control [9, 15,16,17]. In addition to the regulatory frameworks that have been established to maintain control (presented in Sect."Governance and regulation for AI in Critical Care") [18, 19] we advocate for clinicians to be involved in this process and provide guidance.
Develop human-centric AI in healthcare
AI development in medicine and healthcare should maintain a human-centric perspective, promote empathetic care, and increase the time allocated to patient-physician communication and interaction. For example, the use of AI to replace humans in time-consuming or bureaucratic tasks such as documentation and transfers of care [20,21,22]. It could craft clinical notes, ensuring critical information is accurately captured in health records while reducing administrative burdens [23].
Establish social contract for AI use in healthcare
There is a significant concern that AI may exacerbate societal healthcare disparities [24]. When considering AI’s potential influence on physicians'choices and behaviour, the possibility of including or reinforcing biases should be examined rigorously to avoid perpetuating existing health inequities and unfair data-driven associations [24]. It is thus vital to involve patients and societal representatives in discussions regarding the vision of the next healthcare era, its operations, goals, and limits of action [25]. The desirable aim would be to establish a social contract for AI in healthcare, to ensure the accountability and transparency of AI in healthcare. A social contract for AI in healthcare should define clear roles and responsibilities for all stakeholders—clinicians, patients, developers, regulators, and administrators. This includes clinicians being equipped to critically evaluate AI tools, developers ensuring transparency, safety, and clinical relevance, and regulators enforcing performance, equity, and post-deployment monitoring standards. We advocate for hospitals to establish formal oversight mechanisms, such as dedicated AI committees, to ensure the safe implementation of AI systems. Such structures would help formalize shared accountability and ensure that AI deployment remains aligned with the core values of fairness, safety, and human-centred care.
Prioritize human oversight and ethical governance in clinical AI
Since the Hippocratic oath, patient care has been based on the doctor-patient connection where clinicians bear the ethical responsibility to maximize patient benefit while minimizing harm. As AI technologies are increasingly integrated into healthcare, their responsibility must also extend to overseeing its development and application. In the ICU, where treatment decisions balance between individual patient preferences and societal consideration, healthcare professionals must lead this transition [26]. As intensivists, we should maintain governance of this process, ensuring ethical principles and scientific rigor guide the development of frameworks to measure fairness, assess bias, and establish acceptable thresholds for AI uncertainty [6,7,8].
While AI models are rapidly emerging, most are being developed outside the medical community. To better align AI development with clinical ethics, we propose the incorporation of multidisciplinary boards comprising clinicians, patients, ethicists, and technological experts, who should be responsible for systematically reviewing algorithmic behaviour in critical care, assessing the risks of bias, and promoting transparency in decision-making processes. In this context, AI development offers an opportunity to rethink and advance ethical principles in patient care.
Recommendations for clinician training on AI use
Develop and assess the Human-AI interface
Despite some promising results [27, 28], the clinical application of AI remains limited [29,30,31]. The first step toward integration is to understand how clinicians interact with AI and to design systems that complement, rather than disrupt, clinical reasoning [32]. This translates into the need for specific research on the human-AI interface, where a key area of focus is identifying the most effective cognitive interface between clinicians and AI systems. On one side, physicians may place excessive trust on AI model results, possibly overlooking crucial information. For example, in sepsis detection an AI algorithm might miss an atypical presentation or a tropical infectious disease due to limitations in its training data; if clinicians overly trust the algorithm’s negative output, they may delay initiating a necessary antibiotic. On the other, the behaviour of clinicians can influence AI responses in unintended ways. To better reflect this interaction, the concept of synergy between human and AI has been proposed in the last years, emphasizing that AI supports rather than replaces human clinicians [33]. This collaboration has been described in two forms: human-AI augmentation (when human–AI interface enhances clinical performance compared to human alone) and human-AI synergy (where the combined performance exceeds that of both the human and the AI individually) [34]. To support the introduction of AI in clinical practice in intensive care, we propose starting with the concept of human-AI augmentation, which is more inclusive and better established according to medical literature [34]. A straightforward example of the latter is the development of interpretable, real-time dashboards that synthetize complex multidimensional data into visual formats, thereby enhancing clinicians’ situational awareness without overwhelming them.
Improve disease characterization with AI
Traditional procedures for classifying patients and labelling diseases and syndromes based on a few simple criteria are the basis of medical education, but they may fail to grasp the complexity of underlying pathology and lead to suboptimal care. In critical care, where patient conditions are complex and rapidly evolving, AI-driven phenotyping plays a crucial role by leveraging vast amounts of genetic, radiological, biomarker, and physiological data. AI-based phenotyping methods can be broadly categorized into two approaches.
One approach involves unsupervised clustering, in which patients are grouped based on shared features or patterns without prior labelling. Seymour et al. demonstrated how machine learning can stratify septic patients into clinically meaningful subgroups using high-dimensional data, which can subsequently inform risk assessment and prognosis [35]. Another promising possibility is the use of supervised or semi-supervised clustering techniques, which incorporate known outcomes or partial labelling to enhance the phenotyping of patient subgroups [36].
The second approach falls under the causal inference framework, where phenotyping is conducted with the specific objective of identifying subgroups that benefit from a particular intervention due to a causal association. This method aims to enhance personalized treatment by identifying how treatment effects vary among groups, ensuring that therapies are targeted toward patients most likely to benefit. For example, machine learning has been used to stratify critically ill patients based on their response to specific therapeutic interventions, potentially improving clinical outcomes [37]. In a large ICU cohort of patients with traumatic brain injury (TBI), unsupervised clustering identified six distinct subgroups, based on combined neurological and metabolic profiles. [38]
These approaches hold significant potential for advancing acute and critical care by ensuring that AI-driven phenotyping is not only descriptive, but also actionable. Before integrating these methodologies into clinical workflows, we need to make sure clinicians can accept the paradigm shift between broad syndromes and specific sub-phenotypes, ultimately supporting the transition toward personalized medicine [35, 39,40,41].
Ensure AI training for responsible use of AI in healthcare
In addition to clinical practice, undergraduate medical education is also directly influenced by AI transformation [42] as future workers need to be equipped to understand and use these technologies. Providing training and knowledge from the start of their education requires that all clinicians understand data science and AI's fundamental concepts, methods, and limitations, which should be included in medical degree core curriculum. This will allow clinicians to use and assess AI critically, identify biases and limitations, and make well-informed decisions, which may ultimately benefit the medical profession's identity crisis and provide new careers in data analysis and AI research [42].
In addition to undergraduate education, it is essential to train experienced physicians, nurses, and other allied health professional [43]. The effects of AI on academic education are deep and outside the scope of the current manuscript. One promising example is the use of AI to support personalized, AI-driven training for clinicians—both in clinical education and in understanding AI-related concepts [44]. Tools such as chatbots, adaptive simulation platforms, and intelligent tutoring systems can adapt content to students’ learning needs in real time, offering a tailored education. This may be applied to both clinical training and training in AI domains.
Accepting uncertainty in medical decision-making
Uncertainty is an intrinsic part of clinical decision-making, with which clinicians are familiar and are trained to navigate it through experience and intuition. However, AI models introduce a new type of uncertainty, which can undermine clinicians'trust, especially when models function as opaque “black boxes” [45,46,47]. This increases cognitive distance between model and clinical judgment, as clinicians don’t know how to interpret it. To bridge this gap, explainable AI (XAI) has emerged, providing tools to make model predictions more interpretable and, ideally, more trustworthy to reduce perceived uncertainty [48].
Yet, we argue that interpretability alone is not enough [48].To accelerate AI adoption and trust, we advocate that physicians must be trained to interpret outputs under uncertainty—using frameworks like plausibility, consistency with known biology, and alignment with consolidated clinical reasoning—rather than expecting full explainability [49].
Standardize and share data while maintaining patient privacy
In this section we present key infrastructures for AI deployment in critical care [50]. Their costs should be seen as investment in patient outcomes, processes efficiency, and reduced operational costs. Retaining data ownership within healthcare institutions, and recognizing patients and providers as stakeholders, allows them to benefit from the value their data creates. On the contrary, without safeguards clinical data risk becoming proprietary products of private companies—which are resold to their source institutions rather than serving as a resource for their own development—for instance, through the development and licensing of synthetic datasets [51].
Standardize data to promote reproducible AI models
Standardized data collection is essential for creating generalizable and reproducible AI models and fostering interoperability between different centres and systems. A key challenge in acute and critical care is the variability in data sources, including EHRs, multi-omics data (genomics, transcriptomics, proteomics, and metabolomics), medical imaging (radiology, pathology, and ultrasound), and unstructured free-text data from clinical notes and reports. These diverse data modalities are crucial for developing AI-driven decision-support tools, yet their integration is complex due to differences in structure, format, and quality across healthcare institutions.
For instance, the detection of organ dysfunction in the ICU, hemodynamic monitoring collected by different devices, respiratory parameters from ventilators by different manufacturers, and variations in local policies and regulations all impact EHR data quality, structure, and consistency across different centres and clinical trials.
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which embeds standard vocabularies such as LOINC and SNOMED CT, continues to gain popularity as a framework for structuring healthcare data, enabling cross-centre data exchange and model interoperability [52,53,54]. Similarly, Fast Healthcare Interoperability Resources (FHIR) offers a flexible, standardized information exchange solution, facilitating real-time accessibility of structured data [55].
Hospitals, device and EHR companies must contribute to the adoption of recognized standards to make sure interoperability is not a barrier to AI implementation.
Beyond structured data, AI has the potential to enhance data standardization by automatically tagging and labelling data sources, tracking provenance, and harmonizing data formats across institutions. Leveraging AI for these tasks can help mitigate data inconsistencies, thereby improving the reliability and scalability of AI-driven clinical applications.
Prioritize data safety, security, and patient privacy
Data safety, security and privacy are all needed for the application of AI in critical care. Data safety refers to the protection of data from accidental loss or system failure, while data security is related with defensive strategies for malicious attacks including hacking, ransomware, or unauthorized data access [56]. In modern hospitals, data safety and security will soon become as essential as wall oxygen in operating rooms [57, 58]. A corrupted or hacked clinical dataset during hospital care could be as catastrophic as losing electricity, medications, or oxygen. Finally, data privacy focuses on the safeguard of personally information, ensuring that patient data is stored and accessed in compliance with legal standards [56].
Implementing AI that prioritizes these three pillars will be critical for resilient digital infrastructure in healthcare. A possible option for the medical community is to support open-source modes to increase transparency and reduce dependence on proprietary algorithms, and possibly enable better control of safety and privacy issues within the distributed systems [59]. However, sustaining open-source innovation requires appropriate incentives, such as public or dedicated research funding, academic recognition, and regulatory support to ensure high-quality development and long-term viability [60]. Without such strategies, the role of open-source models will be reduced, with the risk of ceding a larger part of control of clinical decision-making to commercial algorithms.
Develop rigorous AI research methodology
We believe AI research should be held to the same methodological standards of other areas of medical research. Achieving this will require greater accountability from peer reviewers and scientific journals to ensure rigor, transparency, and clinical relevance.
Furthermore, advancing AI in ICU research requires a transformation in the necessary underlying infrastructure, particularly when considering high-frequency data collection and the integration of complex, multimodal patient information, detailed in the sections below. In this context, the gap in data resolution between highly monitored environments such as ICUs and standard wards become apparent. The ICU provides a high level of data granularity due to high resolution monitoring systems, capable of capturing the rapid changes in a patient's physiological status [61]. Consequently, the integration of this new source of high-volume, rapidly changing physiological data into medical research and clinical practice could give rise to “physiolomics”, a proposed term to describe this domain, that could become as crucial as genomics, proteomics and other “-omics” fields in advancing personalized medicine.
AI will change how clinical research is performed, improving evidence-based medicine and conducting randomized clinical trials (RCTs) [62]. Instead of using large, heterogeneous trial populations, AI might help researchers design and enrol tailored patient subgroups for precise RCTs [63, 64]. These precision methods could solve the problem of negative critical care trials related to inhomogeneities in the population and significant confounding effects. AI could thus improve RCTs by allowing the enrolment of very subtle subgroups of patients with hundreds of specific inclusion criteria over dozens of centres, a task impossible to perform by humans in real-time practice, improving trial efficiency in enrolling enriched populations [65,66,67]. In the TBI example cited, conducting an RCT on the six AI-identified endotypes—such as patients with moderate GCS but severe metabolic derangement—would be unfeasible without AI stratification [38]. This underscores AI’s potential to enable precision trial designs in critical care.
There are multiple domains for interaction between AI and RCT, though a comprehensive review is beyond the scope of this paper. These include trial emulation to identify patient populations that may benefit most from an intervention, screening for the most promising drugs for interventions, detecting heterogeneity of treatment effects, and automated screening to improve the efficiency and cost of clinical trials.
Ensuring that AI models are clinically effective, reproducible, and generalizable requires adherence to rigorous methodological standards, particularly in critical care where patient heterogeneity, real-time decision-making, and high-frequency data collection pose unique challenges. Several established reporting and validation frameworks already provide guidance for improving AI research in ICU settings. While these frameworks are not specific to the ICU environment, we believe these should be rapidly disseminated into the critical care community through dedicated initiatives, courses and scientific societies.
For predictive models, the TRIPOD-AI extension of the TRIPOD guidelines focuses on transparent reporting for clinical prediction models with specific emphasis on calibration, internal and external validation, and fairness [68]. PROBAST-AI framework complements this by offering a structured tool to assess risk of bias and applicability in prediction model studies [69]. CONSORT-AI extends the CONSORT framework to include AI-specific elements such as algorithm transparency and reproducibility for interventional trials with AI [70], while STARD-AI provides a framework for reporting AI-based diagnostic accuracy studies [71]. Together, these guidelines encompass several issues related to transparency, reproducibility, fairness, external validation, and human oversight—principles that must be considered foundational for any trustworthy AI research in healthcare. Despite the availability of these frameworks, many ICU studies involving AI methods still fail to meet these standards, leading to concerns about inadequate external validation and generalizability [68, 72, 73].
Beyond prediction models, critical care-specific guidelines proposed in recent literature offer targeted recommendations for evaluating AI tools in ICU environments, particularly regarding data heterogeneity, patient safety, and integration with clinical workflows. Moving forward, AI research in critical care must align with these established frameworks and adopt higher methodological standards, such as pre-registered AI trials, prospective validation in diverse ICU populations, and standardized benchmarks for algorithmic performance.
Encourage collaborative AI models
Centralizing data collection from multiple ICUs, or federating them into structured networks, enhances external validity and reliability by enabling a scale of data volume that would be unattainable for individual institutions alone [74]. ICUs are at the forefront of data sharing efforts, offering several publicly available datasets for use by the research community [75]. There are several strategies to build collaborative databases. Networking refers to collaborative research consortia [76] that align protocols and pool clinical research data across institutions. Federated learning, by contrast, involves a decentralized approach where data are stored locally and only models or weights are shared between centres [77]. Finally, centralized approaches, such as the Epic Cosmos initiative, leverage de-identified data collected from EHR and stored on a central server providing access to large patient populations for research and quality improvement purposes across the healthcare system [78]. Federated learning is gaining traction in Europe, where data privacy regulations have a more risk-averse approach to AI development, thus favouring decentralized models [79]. In contrast, centralized learning approaches like Epic Cosmos are more common in the United States, where there is a more risk-tolerant environment which favours large-scale data aggregation.
In parallel, the use of synthetic data is emerging as a complementary strategy to enable data sharing while preserving patient privacy. Synthetic datasets are artificially generated to reflect the characteristics of real patient data and can be used to train and test models without exposing sensitive information [80]. The availability of large-scale data, may also support the creation of digital twins. Digital twins, or virtual simulations that mirror an individual’s biological and clinical state and rely on high-volume, high-fidelity datasets, may allow for predictive modelling and virtual testing of interventions before bedside application and improve safety of interventions.
The ICU community should advocate for the diffusion of further initiatives to extended collaborative AI models at national and international level.
Governance and regulation for AI in Critical Care
Despite growing regulatory efforts, AI regulation remains one of the greatest hurdles to clinical implementation, particularly in high-stakes environments like critical care, as regulatory governance, surveillance, and evaluation of model performance are not only conceptually difficult, but also require a large operational effort across diverse healthcare settings. The recent European Union AI Act introduced a risk-based regulatory framework, classifying medical AI as high-risk and requiring stringent compliance with transparency, human oversight, and post-market monitoring [18]. While these regulatory efforts provide foundational guidance, critical care AI presents unique challenges requiring specialized oversight.
By integrating regulatory, professional, and institutional oversight, AI governance in critical care can move beyond theoretical discussions toward actionable policies that balance technological innovation with patient safety [73, 81, 82].
Grant collaboration between public and private sector
Given the complexity and significant economic, human, and computational resources needed to develop a large generative AI model, physicians and regulators should promote partnerships among healthcare institutions, technology companies, and governmental bodies to support the research, development, and deployment of AI-enabled care solutions [83]. Beyond regulatory agencies, professional societies and institutional governance structures must assume a more active role. Organizations such as Society of Critical Care Medicine (SCCM), European Society of Intensive Care Medicine (ESICM), and regulatory bodies like the European Medical Agency (EMA) should establish specific clinical practice guidelines for AI in critical care, including standards for model validation, clinician–AI collaboration, and accountability. Regulatory bodies should operate at both national and supranational levels, with transparent governance involving multidisciplinary representation—including clinicians, data scientists, ethicists, and patient advocates—to ensure decisions are both evidence-based and ethically grounded. To avoid postponing innovation indefinitely, regulation should be adaptive and proportionate, focusing on risk-based oversight and continuous post-deployment monitoring rather than rigid pre-market restrictions. Furthermore, implementing mandatory reporting requirements for AI performance and creating hospital-based AI safety committees could offer a structured, practical framework to safeguard the ongoing reliability and safety of clinical AI applications.
Address AI divide to improve health equality
The adoption of AI may vary significantly across various geographic regions, influenced by technological capacities, (i.e. disparities in access to software or hardware resources), and differences in investments and priorities between countries. This “AI divide” can separate those with high access to AI from those with limited or no access, exacerbating social and economic inequalities.
The EU commission has been proposed to act as an umbrella to coordinate EU wide strategies to reduce the AI divide between European countries, implementing coordination and supporting programmes of activities [84]. The use of specific programmes, such as Marie-Curie training networks, is mentioned here to strengthen the human capital on AI while developing infrastructures and implementing common guidelines and approaches across countries.
A recent document from the United Nations also addresses the digital divide across different economic sectors, recommending education, international cooperation, and technological development for an equitable AI resource and infrastructure allocation [85].
Accordingly, the medical community in each country should lobby at both national level and international level through society and WHO for international collaborations, such as through the development of specific grants and research initiatives. Intensivist should require supranational approaches to standardized data collection and require policies for AI technology and data analysis. Governments, UN, WHO, and scientific society should be the target of this coordinated effort.
Continuous evaluation of dynamic models and post-marketing surveillance
A major limitation in current regulation is the lack of established pathways for dynamic AI models. AI systems in critical care are inherently dynamic, evolving as they incorporate new real-world data, while most FDA approvals rely on static evaluation. In contrast, the EU AI Act emphasizes continuous risk assessment [18]. This approach should be expanded globally to enable real-time auditing, validation, and governance of AI-driven decision support tools in intensive care units, as well as applying to post-market surveillance. The EU AI Act mandates ongoing surveillance of high-risk AI systems, a principle that we advocate to be adopted internationally to mitigate the risks of AI degradation and bias drift in ICU environments. In practice, this requires AI commercial entities to provide post-marketing surveillance plans and to report serious incidents within a predefined time window (15 days or less) [18]. Companies should also maintain this monitoring as the AI systems evolve over time. The implementation of these surveillance systems should include standardized monitoring protocols, embedded incident reporting tools within clinical workflows, participation in performance registries, and regular audits. These mechanisms are overseen by national Market Surveillance Authorities (MSAs), supported by EU-wide guidance and upcoming templates to ensure consistent and enforceable oversight of clinical AI systems.
Require adequate regulations for AI deployment in clinical practice
Deploying AI within complex clinical environments like the ICU, acute wards, or even regular wards presents a complex challenge [86].
We underline three aspects for adequate regulation: first, a rigorous regulatory process for evaluation of safety and efficacy before clinical application of AI products. A second aspect is related with continuous post-market evaluation, which should be mandatory and conducted according to other types of medical devices [18].
The third important aspect is liability, identifying who should be held accountable if an AI decision or a human decision based on AI leads to harm. This relates with the necessity for adequate insurance policies. We urge regulatory bodies in each country to provide regulations on these issues, which are fundamental for AI diffusion.
We also recommend that both patients and clinicians request that regulatory bodies in each country update current legislation and regulatory pathways, including clear rules for insurance policies to anticipate and reduce the risk for case laws.
| 2025-12-14T00:00:00 |
2025/12/14
|
https://ccforum.biomedcentral.com/articles/10.1186/s13054-025-05532-2
|
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Evaluating accountability, transparency, and bias in AI-assisted ...
|
Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK - BMC Medical Ethics
|
https://bmcmedethics.biomedcentral.com
|
[
"Nouis",
"Saoudi Ce",
"Biochemistry Department",
"Worcester Royal Hospital",
"Worcester",
"Master Of Business Management",
"Aston University",
"Birmingham",
"Uren",
"Aston Business School"
] |
Forty participants—including clinicians, healthcare administrators, and AI developers—took part in semi-structured interviews or focus groups.
|
Aim, design, and setting
The main aim of this study was to explore how healthcare professionals interpret, implement, and evaluate AI tools in clinical decision-making, focusing on ethical considerations such as accountability, transparency, and potential bias. Because this work was confined to a single NHS Trust context, we do not claim generalizability beyond that local setting. A qualitative design was adopted, grounded in phenomenological principles [10] that prioritize the subjective experiences and personal interpretations of participants. Phenomenology was deemed especially relevant given the multifaceted nature of AI integration in healthcare, where personal perceptions can reveal challenges, benefits, and ethical dilemmas not captured by purely quantitative measures. By emphasizing lived experiences, this approach allowed us to examine how participants understood and negotiated the opacity of AI systems, the sharing of responsibility for patient outcomes, and the potential for biased decision-making.
The study was conducted across multiple hospitals within one NHS Trust in the West Midlands, United Kingdom, each displaying varying levels of AI adoption. Some had integrated advanced imaging analytics into everyday practice, while others had more limited, pilot-stage AI initiatives. This range ensured that participants encompassed both early and later adopters, as well as those at different levels of enthusiasm or skepticism about AI-driven tools. We emphasize that our findings reflect the local experiences of staff at these sites and are not intended to be applied universally.
Participant characteristics
We first used purposive sampling to capture a breadth of clinical roles, then applied snowball sampling to reach IT specialists and AI developers who were less visible in staff directories. Inclusion criteria required that individuals be employed within the hospital setting for at least one year, have direct or indirect exposure to AI-supported clinical systems, and voluntarily consent to participate. Exclusion criteria eliminated those without any exposure to AI or those unable to grant informed consent for any reason.
From these efforts, approximately 40 participants were recruited, comprising clinicians (such as doctors, nurses, and biomedical scientists), AI developers, IT specialists, and healthcare administrators. Fifteen participants identified as experienced clinicians with a history of working closely with AI-based tools, ten were AI experts or IT professionals involved in designing or maintaining AI systems, ten were administrators responsible for managing AI related activities, and five were clinicians who were relatively new to AI use. Out of the total 40 participants, 25 opted to participate in one-on-one interviews, while 15 took part in focus group discussions. This distribution ensured both a depth of individual reflections and the potential for interactive dialogue around shared AI-related challenges and successes.
Demographics
In this qualitative study, 40 participants were recruited, comprising 28 clinicians, 6 AI developers, and 6 departmental administrators. Clinicians spanned various roles—clinical scientists, biomedical scientists, laboratory technicians, radiology specialists, nurses, and doctors—ensuring a wide spectrum of expertise and exposure to AI-driven tools. The decision to recruit 40 participants was guided by the principle of data saturation, whereby interviews continued until no new insights emerged. All participants were selected based on the direct or indirect influence of AI in their daily work. For instance, clinicians described using AI to streamline lab diagnostics, flag anomalies in patient imaging, or manage triage systems. AI developers refined algorithms, integrated them into electronic health records, and maintained predictive models for patient risk assessments, while departmental administrators oversaw the integration of AI into hospital workflows, focusing on policy compliance, staff training, and ethical considerations.
By including individuals across these diverse roles and real-world AI applications, the study captured a broad perspective on the integration, challenges, and ethical implications of AI in clinical decision-making. Table 1 provides an overview of the demographic characteristics, including gender, clinical experience, years of AI-assisted systems use, and specialty.
Table 1 Demographic characteristics (n = 40) Full size table
All participant quotations in this paper have been lightly edited for brevity and clarity. Minor grammatical refinements and the removal of extraneous filler words were made to ensure readability without compromising the substance or intent of the original remarks. To maintain transparency about these editorial choices, a selection of unedited, verbatim quotes is included in the appendix, allowing readers to observe participants’ spontaneous thinking and the ethical dilemmas they encountered—particularly around accountability, transparency, and bias in AI supported clinical decision-making.
Data collection procedures
To capture the depth and breadth of participants’ experiences, semi-structured interviews were held with 25 participants, each session running for approximately 45 to 60 min. Some participants—15 in total—opted to join focus group discussions, each lasting around 60 min with groups of 5 to 6 people. In the individual interviews, participants often provided detailed, personal accounts of how AI affected their decision-making and ethical responsibilities; in contrast, the focus group format facilitated collective insights and sometimes revealed differing viewpoints about the same AI tools or processes. The interview guide was designed by the lead author in collaboration with the co-author, drawing on preliminary literature and pilot-tested with two senior clinicians. It covered topics such as perceived benefits of AI, potential workflow disruptions, issues of algorithmic opacity, and questions of accountability when AI-driven recommendations diverge from human clinical judgment.
All interviews and focus groups were audio-recorded after obtaining verbal consent from participants, with the recordings transcribed verbatim to create an accurate textual dataset. Transcripts were anonymized, with unique codes assigned to each participant, thereby removing references to personal identifiers, hospital names, or departmental specifics. Digital transcripts were securely stored on a password-protected computer system accessible only to the core study team.
No formal comparative interventions were introduced as part of this study. Rather, participants were encouraged to reflect on their existing experiences with any AI tools or processes present in their workplace, including both established systems and pilot-stage initiatives. While some hospitals were exploring AI to enhance diagnostic speed and accuracy, others were focusing on back-office operational tools, such as automated scheduling or real-time resource monitoring. These naturally occurring variations in AI use allowed for a wide scope of perspectives on ethical and practical hurdles.
Data analysis
Data analysis followed the thematic analysis framework outlined by Braun and Clarke [24], which involves a structured, multi-phase process of coding, reviewing, and defining themes. Taking a deductive stance, we built an a-priori code book comprising five sensitising concepts: economic impact, efficiency, clinical impact, accountability & transparency, and bias derived from our study aims and the AI-ethics literature. Two researchers independently coded initial transcripts to generate an overarching codebook. Discrepancies were resolved through discussions designed to refine coding definitions, thereby ensuring consistency across the dataset. Once the codebook was deemed sufficiently stable, it was applied to the remaining transcripts. This iterative, cyclical process allowed for refinement of themes as new data emerged, enabling the study to capture multifaceted experiences of AI integration ranging from optimism about efficiency gains to concern over biases in training data or “black box” recommendations that lack explainability. While material relevant to all five sensitising concepts was identified, participants spoke most extensively about accountability, transparency, and bias; these three areas therefore receive particular emphasis in the Results.
Because some participants spoke in focus groups while others did so in private interviews, the analysis also considered the potential influence of group dynamics versus individual reflection. Focus group interactions sometimes triggered spontaneous debate or collective consensus on certain issues, whereas one-on-one interviews allowed for more personal, detailed narratives. Throughout the analysis, MAXQDA 24 software facilitated the systematic organization and retrieval of coded data. The emergent themes included questions about who bears responsibility for decisions in an AI-augmented environment, how transparency or opacity of AI outputs affects clinical trust and patient communication, and whether any known biases (such as underperformance in minority patient populations) had manifested in participants’ day-to-day practice. Data collection and analysis were undertaken concurrently, allowing the research team to adapt the interview guide as new focal areas, such as interpretability or user training, became increasingly salient in participants’ accounts.
Ethical approval and considerations
Ethical approval for this study was granted by Aston University under its guidelines for non-invasive social research. The study involved interviews solely with healthcare professionals, and no identifiable patient data were collected, nor were any clinical interventions conducted. Additionally, approval from the Trust’s Caldicott Guardian was obtained to ensure adherence to national data confidentiality standards. All participants received an information sheet outlining the study’s aims, the voluntary nature of their involvement, and their right to withdraw at any time. Written informed consent was obtained from each participant prior to the interviews or focus groups. All data were anonymized, and no patient-related information was collected or stored. This study followed ethical guidelines to protect participants’ privacy and confidentiality, in line with the principles of the Declaration of Helsinki.
All recruitment and data collection procedures were carried out in coordination with local hospital administrators to avoid disruption to normal operations and to ensure fully voluntary staff participation. Participants received an information sheet outlining the study’s aims, the voluntary nature of involvement, the right to withdraw at any time, and the confidentiality measures protecting personal details. Written informed consent was obtained from each participant before any interview or focus group began. The participants were employees of the NHS Trust, and no patients were involved or patient data analyzed in the study. Interview transcripts were anonymized using unique participant codes. All electronic data were maintained on encrypted, password-protected systems, accessible only to the core research team. The resulting interviews and focus groups provided valuable insights into healthcare professionals’ perspectives on AI implementation and ethics, thereby advancing discussions on how to responsibly and effectively integrate AI-driven technologies into clinical practice.
| 2025-12-14T00:00:00 |
2025/12/14
|
https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-025-01243-z
|
[
{
"date": "2023/01/01",
"position": 94,
"query": "AI healthcare"
},
{
"date": "2023/02/01",
"position": 95,
"query": "AI healthcare"
},
{
"date": "2023/07/01",
"position": 96,
"query": "AI healthcare"
},
{
"date": "2023/08/01",
"position": 94,
"query": "AI healthcare"
},
{
"date": "2023/11/01",
"position": 94,
"query": "AI healthcare"
},
{
"date": "2023/12/01",
"position": 89,
"query": "AI healthcare"
},
{
"date": "2024/01/01",
"position": 95,
"query": "AI healthcare"
},
{
"date": "2024/03/01",
"position": 93,
"query": "AI healthcare"
},
{
"date": "2024/09/01",
"position": 94,
"query": "AI healthcare"
},
{
"date": "2024/11/01",
"position": 94,
"query": "AI healthcare"
}
] |
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] |
Global Trends in Education: Artificial Intelligence ...
| "Global Trends in Education: Artificial Intelligence, Postplagiarism, and Future-focused Learning fo(...TRUNCATED) |
https://edintegrity.biomedcentral.com
| ["Eaton","Sarah Elaine","Werklund School Of Education","University Of Calgary","Calgary","Sarah Elai(...TRUNCATED) | "by SE Eaton · 2025 · Cited by 4 — In this distinguished research lecture, Dr. Sarah Elaine Eato(...TRUNCATED) | "Good guys versus bad guys\n\nStudents have been cheating for as long as there have been examination(...TRUNCATED) | 2025-12-14T00:00:00 |
2025/12/14
|
https://edintegrity.biomedcentral.com/articles/10.1007/s40979-025-00187-6
|
[
{
"date": "2025/03/01",
"position": 6,
"query": "artificial intelligence education"
}
] | [0.0982566848397255,-0.04180486500263214,-0.010146071203052998,0.07231936603784561,0.007628624793142(...TRUNCATED) |
AI and the economic divide: How Artificial Intelligence could widen ...
| "AI and the economic divide: How Artificial Intelligence could widen the divide in the U.S. - EPJ Da(...TRUNCATED) |
https://epjdatascience.springeropen.com
| ["Septiandri","Nokia Bell Labs","Cambridge","Constantinides","Cyens Centre Of Excellence","Nicosia",(...TRUNCATED) | "These dynamics suggest that AI could amplify existing divides, hitting hardest in areas where econo(...TRUNCATED) | "4.1 Most- and least-impacted geographical areas\n\nJust as with the occupation analysis, the AII me(...TRUNCATED) | 2025-12-14T00:00:00 |
2025/12/14
|
https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-025-00547-9
| [{"date":"2025/04/17","position":45,"query":"AI economic disruption"},{"date":"2025/04/17","position(...TRUNCATED) | [-0.06184086576104164,0.02441847324371338,-0.005743398331105709,0.03698892891407013,0.01618265733122(...TRUNCATED) |
Radiology AI and sustainability paradox: environmental, economic ...
| "Radiology AI and sustainability paradox: environmental, economic, and social dimensions - Insights (...TRUNCATED) |
https://insightsimaging.springeropen.com
| ["Kocak","Department Of Radiology","University Of Health Sciences","Basaksehir Cam","Sakura City Hos(...TRUNCATED) | "AI systems, particularly deep learning models, require substantial computational resources, leading(...TRUNCATED) | "Environmental impact and challenges\n\nThe process of training an AI model, particularly those base(...TRUNCATED) | 2025-12-14T00:00:00 |
2025/12/14
|
https://insightsimaging.springeropen.com/articles/10.1186/s13244-025-01962-2
|
[
{
"date": "2025/04/17",
"position": 91,
"query": "AI economic disruption"
}
] | [-0.09297597408294678,0.08827494084835052,0.002889176132157445,-0.0016323028830811381,0.009010312147(...TRUNCATED) |
Generative AI and its disruptive challenge to journalism
|
Generative AI and its disruptive challenge to journalism: an institutional analysis
|
https://link.springer.com
| ["Lewis","Seth C.","Sclewis Uoregon.Edu","University Of Oregon","Eugene","United States","Guzman","A(...TRUNCATED) | "by SC Lewis · 2025 · Cited by 3 — This conceptual article examines the transformative impact of(...TRUNCATED) | "It has long been anticipated, with various degrees of hope, hype, and hysteria, that artificial int(...TRUNCATED) | 2025-12-14T00:00:00 |
2025/12/14
|
https://link.springer.com/article/10.1007/s44382-025-00008-x
| [{"date":"2025/06/01","position":23,"query":"AI journalism"},{"date":"2025/06/01","position":17,"que(...TRUNCATED) | [0.05142361670732498,-0.03176164627075195,-0.005740470718592405,0.022136904299259186,0.0244743414223(...TRUNCATED) |
Healthcare workers' readiness for artificial intelligence and ...
| "Healthcare workers' readiness for artificial intelligence and organizational change: a quantitative(...TRUNCATED) |
https://bmchealthservres.biomedcentral.com
| ["Boyacı","Fikret Biyal Central Research Laboratory","Cerrahpasa Medical Faculty","Istanbul Univers(...TRUNCATED) | "The aim of the study is to measure the readiness levels of medical artificial intelligence and the (...TRUNCATED) | "Technology offers new solutions to improve the quality of healthcare and facilitate access [1]. The(...TRUNCATED) | 2025-12-07T00:00:00 |
2025/12/07
|
https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-025-12846-y
| [{"date":"2025/06/08","position":21,"query":"artificial intelligence healthcare workers"},{"date":"2(...TRUNCATED) | [-0.056635815650224686,0.025890657678246498,-0.0011209171498194337,0.0047196513041853905,-0.02616035(...TRUNCATED) |
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