Jul 2026· British Journal of Surgery· Vol 113· 0 citations
TL;DR
There is an urgent need for clear national and hospital-level policies to support safe, regulated integration of AI into clinical practice to support safe, regulated integration of AI into clinical practice.
Abstract
With the growing use of artificial intelligence (AI) tools, the health service executive (HSE) has begun formally integrating AI into national digital and clinical governance structures. Despite this, real world patterns of AI use among doctors remain poorly characterised.
We conducted a cross sectional, observational and descriptive study using an anonymous online questionnaire distributed to doctors working across Ireland. The survey captured demographic data, context of AI use and perceived influence on clinical decision making including benefits and concerns.
Eighty-nine doctors consented and completed the survey. Seventy-four (83%) reported using AI during clinical care with fifty-two (58%) using it at least weekly. AI was used most commonly for rapid clarification of unfamiliar topics (71%), interpreting complex polypharmacy (60%) and checking medication interactions and doses (51%).
Among those who use AI, chat GPT is most commonly used (82%). AI was reported to have a moderate to very strong influence formulating a differential diagnosis (49%), selecting investigations (45%) and in prescribing decisions (42%). Forty-eight (54%) reported identifying incorrect AI recommendations regularly. Information was most often verified by cross checking with colleagues (34%) or guidelines (27%).
Sixty-four (72%) doctors expressed concern that AI may provide clinically unsafe information and seventy-five (84%) were unsure about how AI use fits within clinical governance.
While clinicians recognise its efficiency and cognitive benefits, substantial concern remains regarding safety and regulation. These findings highlight the urgent need for clear national and hospital-level policies to support safe, regulated integration of AI into clinical practice.
Artificial Intelligence (AI) is rapidly transforming healthcare delivery worldwide. The adoption of AI among healthcare professionals in Nigeria, its patterns of use, and associated factors are emerging issues that need to be explored. This study aimed to assess the prevalence, patterns, and determinants of AI use among healthcare professionals in Lagos State, Nigeria. A descriptive cross-sectional study was conducted among 415 healthcare professionals across 14 public health facilities in Lagos, representing primary, secondary, and tertiary levels of care. Data were collected using a structured, self-administered questionnaire and analysed using descriptive statistics, chi-square tests, and binary logistic regression. Although only 19.8% had received formal AI training, 86.5% reported using AI and 51.3% used it specifically for professional duties. The most common professional application was diagnostic support, including clinical reasoning, interpretation of laboratory and radiological findings, and medication-interaction checks. AI use in professional duties was significantly associated with educational level (p = 0.042); postgraduate-qualified professionals were twice as likely to use AI as those with only basic healthcare degrees (OR = 2.07, p = 0.014). Key barriers included privacy concerns, algorithmic distrust, and limited formal training. Despite low formal training, use of AI-related digital tools was common among healthcare professionals in public health facilities in Lagos, primarily supporting diagnostic reasoning and clinical documentation. These findings highlight the need for structured AI literacy programs and institutional governance frameworks to support safe and equitable AI integration in Nigeria’s health system.
A. Chima-Oduko, B. Maduafokwa, K. Abdulraheem et al.· BMC Digital Health· 0 citations
OBJECTIVES
Artificial intelligence (AI) and machine learning applications are rapidly expanding across healthcare. Successful implementation of AI technologies in rheumatology will depend not only on technical performance but also on the perceptions and preparedness of end-users. This study evaluated the current opinions, expectations, and concerns of AI among healthcare professionals and researchers in rheumatology across the UK.
METHODS
A 19-item survey was designed and distributed through national and regional networks aimed at the rheumatology workforce between June 2025 - January 2026, targeted at consultant rheumatologists, doctors-in-training, allied health professionals, specialist nurses, and non-clinical researchers in rheumatology. The questions included respondent background data, current applications of AI in clinical care and research, opinions about AI in terms of perceived impact, concerns, educational needs and expected performance.
RESULTS
Of the 218 respondents, 39% to 40% reported daily or weekly use of AI in research and clinical practice respectively. The most common clinical uses were using LLMs to look up medical facts (45%), to improve grammar/spelling of clinical documentation (28%), generate differential diagnoses (22%) and use of ambient scribes (17%). 86% anticipated that AI would substantially impact clinical practice in five years or less. Administrative tasks (85%) and musculoskeletal imaging (63%) were perceived as the areas likely to experience the greatest impact from AI. Highlighted concerns included data security/privacy (70%), medical liability (70%), followed by lack of explainability (47%). One in four reported excellent confidence in using digital technology, with only 6% self-rating their AI knowledge as excellent. A strong interest in education about AI was expressed regarding several areas including the ethical and safe use of AI (66%), safe and efficient use of LLMs in clinical practice (64%), and ambient AI scribes (59%).
CONCLUSIONS
AI is already being used frequently in UK rheumatology practice and research, with most anticipating a considerable impact on clinical care within the next five years or less. However, despite enthusiasm for adoption, important concerns regarding data security, liability, and explainability remain, alongside low self-reported AI knowledge, highlighting the need for targeted education, robust governance, and safe clinical implementation strategies.
H. Canagarajah, Pratyasha Saha, Jordan Tsigarides et al.· Rheumatology· 0 citations
Abstract Background The rapid emergence of artificial intelligence (AI) has outpaced its formal adoption in health care organizations, contributing to the emergence of Shadow AI, defined here as the use of unauthorized AI tools by medical professionals. Under the European Union Medical Device Regulation, AI tools used for clinical purposes must undergo conformity assessment before use; general-purpose tools such as ChatGPT have not done so, rendering their clinical application unauthorized at the regulatory level. While Shadow AI offers potential efficiency gains and higher performance, it poses significant risks to data privacy, clinical safety, and regulatory compliance. Despite its growing prevalence, empirical research on the purposes for which physicians use Shadow AI remains scarce. Objective This study explores the purposes for which physicians describe using Shadow AI in their work. Methods We conducted a cross-sectional survey of physicians employed in Swedish health care organizations (N=357; response rate~64%). Data were collected between December 2023 and January 2024 via a verified online panel. We conducted a qualitative content analysis of free-text responses on the use of unauthorized AI tools. We applied theoretical lenses from the sociology of professions and paradox theory to interpret the empirical findings. Results Physicians use Shadow AI for several purposes, which we grouped into 4 categories: clinical work and decision-making, administrative work, research and professional development, and technological interest and curiosity. More specifically, Shadow AI is used as a colleague and second opinion for clinical decision support (eg, differential diagnoses and rare cases), administrative tasks such as patient communication and documentation, and research aimed at staying up to date and exploring developments in generative AI. Physicians described using these tools compensated for perceived gaps in institutional systems, reducing workload, and accessing knowledge considered difficult to obtain through conventional channels. The findings reveal a tension between physicians’ drive to improve their practice and the regulatory and organizational constraints that render such use unauthorized. Conclusions Shadow AI used by physicians presents both opportunities and risks for health care professionals and organizations. Shadow AI indicates gaps where formal hospital systems may fail to meet health care professionals’ needs and signals a way for physicians to strengthen their experience-based knowledge. It represents a renegotiation of professional boundaries, as physicians bypass institutional constraints to maintain professional efficacy. The findings highlight a paradox in which the same tools that pose regulatory and safety risks also address real gaps in clinical and administrative support, suggesting that governance approaches must account for this tension rather than relying on prohibition alone.
Lena Petersson, Luís Irgang, Ingela Mauritzon et al.· Journal of Medical Internet...· 0 citations
Objective: Artificial intelligence (AI) is rapidly transforming healthcare, offering opportunities to improve diagnostics, optimize workflows, and reduce medical errors. Effective integration requires not only technological innovation but also clinician engagement and education. This study explores the perceptions, knowledge, and experiences of Spanish medical professionals and students regarding AI in healthcare.
Methods: A cross-sectional online survey was conducted between October and December 2024, yielding 167 valid responses from diverse medical backgrounds.
Results: Participants reported higher familiarity with general technology (mean = 6.6/10) than with AI (5.0) or AI in medicine (4.2). Younger respondents demonstrated greater AI literacy but less experience with medical software. Gender disparities were evident, with males reporting significantly higher knowledge and engagement across all domains. Although 95.8% of participants recognized ChatGPT, familiarity with medical AI tools was minimal. Respondents expressed limited awareness of AI regulation (mean = 2.2/10) and uncertainty about physicians’ roles in policy-making, despite broad support for ethical oversight.
Conclusions: These findings reveal significant educational, generational, and gender gaps that may hinder AI adoption in clinical practice. Strengthening interdisciplinary collaboration, promoting inclusive AI education, and involving clinicians in regulatory processes are essential to ensure responsible, equitable, and effective integration of AI in healthcare.
Unknown authors· Journal of Scientific Innova...· 0 citations
Background Artificial intelligence (AI) can enhance diagnostics, treatment, and workflow efficiency. However, successful integration into clinical practice depends on users' acceptance. Objective To investigate benefits, barriers, and challenges of AI applications among anaesthesia and intensive care professionals. Design International online survey. Main outcome measures The survey included items on familiarity and experiences with AI applications, perceived benefits, concerns, and demographic variables. Descriptive analyses, fisher exact tests, χ²-tests, odds ratios, and Spearman rank correlations were used to explore associations between responses and demographics. Results The survey was distributed by the European Society of Anaesthesiology and Intensive Care in 2023. A total of 510 respondents completed the entire survey, primarily from Europe (78%) and Asia (14.5%), and the majority were board-certified anaesthesiologists (86.3%). 86.5% of the respondents were aware of AI applications, but only 36.8% reported regular encounters. Familiarity was higher among males and intensive care specialists. 94.5% expressed interest in AI training, particularly younger and less experienced professionals. 94.7% expressed willingness to use AI applications, citing benefits such as improved decision-making (92.7%), complication anticipation (88.6%), and workload reduction (80.1%). Younger and female respondents were more optimistic about AI's benefits. Key concerns included lack of explainability (68.4%), over-reliance on AI (80.8%), and medico-legal uncertainties (58.4%). Scepticism was attributed to insufficient validation studies and fears of inaccurate outputs, particularly among experienced clinicians. Conclusions This international survey shows cautious optimism among anaesthesia and intensive care professionals regarding AI applications. Adoption in clinical practice requires tailored training that accounts also for demographic-specific concerns, robust validation, and clear ethical and legal frameworks.
R. Theilen, Tim Kramer, M. Scharffenberg et al.· Journal of clinical monitori...· 0 citations
A list of eight best practices was created to assist developers with designing AI systems in a way that would reduce the overall risk of harm for users attempting to use their AI for mental health cases.
Joshua Frankenfield, Briana M. Sobel, Barbara Chaparro· Proceedings of the Internati...· 0 citations