Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 13 appraisals
Journal of medical Internet research
Telehealth Scale and Artificial Intelligence Adoption Tiers Across Clinical and Operational Domains in US Hospitals: Cross-Sectional Study
BACKGROUND: Telehealth expansion and artificial intelligence (AI) adoption are often described as parallel dimensions of health system digital transformation. However, whether telehealth scale is associated with hospital AI adoption and whether this relationship varies across hospital settings remain unclear. OBJECTIVE: This study examined the association of telehealth scale with clinical and operational AI adoption tiers in US hospitals and assessed whether these patterns differed by telehealth reporting behavior and geography. METHODS: This cross-sectional study included 6173 US acute care hospitals using linked 2024 American Hospital Association Annual Survey and Information Technology Supplement data and 2023 Healthcare Cost Report Information System data. Telehealth scale was parameterized using log-transformed telehealth volume, a telehealth nonreporting indicator, and a reported-zero telehealth indicator. Clinical and operational AI adoption tiers were derived from hospital-reported AI capability items and classified into 3 tiers. Both outcomes were modeled using multioutput gradient-boosted tree classifiers, and model behavior was interpreted using Shapley additive explanations, partial dependence plots, and stratified analyses by the Core-Based Statistical Area category. RESULTS: Telehealth volume was the strongest predictor of both clinical and operational AI adoption tiers and had a larger contribution to the clinical AI model. Telehealth nonreporting was common, occurring in 57% (3521/6173) of hospitals, and was concentrated among hospitals in the lowest clinical AI adoption tier, accounting for 91.4% (3145/3441) of hospitals with no reported clinical AI adoption. Higher telehealth volume was associated with a steep increase in predicted clinical AI adoption tiers at lower telehealth volumes, followed by a plateau at higher volumes. At similar telehealth volumes, rural hospitals showed weaker telehealth-attributed contributions to predicted clinical AI adoption tiers than metropolitan hospitals. Supplementary analyses suggested that telehealth reporting status and telehealth intensity reflected related but distinct structural processes. CONCLUSIONS: Telehealth scale was strongly associated with hospital AI adoption tiers, especially clinical AI adoption tiers. These findings suggest that telehealth capacity may serve as a practical hospital-level marker of broader digital readiness for AI adoption, but the cross-sectional design does not establish whether telehealth expansion precedes or causes AI adoption. Hospitals with telehealth nonreporting and rural hospitals may face additional structural barriers that limit the translation of digital capacity into AI maturity. Policies to reduce inequities in hospital AI adoption may therefore need to pair telehealth expansion with implementation support, interoperability capacity, and organizational resources.
14 June 2026
Read appraisal →Journal of the American College of Surgeons
Governance Framework for Safe and Ethical Implementation of Artificial Intelligence in Surgery: A Modified Delphi Consensus
BACKGROUND: Artificial intelligence (AI)-enabled clinical decision support systems (CDSS) demonstrate performance comparable or superior to human experts in certain tasks. However, their integration into surgical practice faces a significant implementation gap, alongside ethical, privacy, and legal concerns. Clear governance frameworks are needed to guide their responsible adoption in surgery, to prevent inconsistent application, care quality variation, and exacerbation of algorithmic bias. Here, we establish a systematic, evidence-based, and consensus-driven framework to guide the ethical, effective, and sustainable adoption of AI-enabled CDSS in surgery. STUDY DESIGN: A systematic literature review was conducted of PubMed, Cochrane Library, Medline, and Embase databases until 2024 to identify key governance themes. The themes informed the generation of candidate items, which were then refined through a multiround expert panel consensus process using a modified Delphi approach to produce the final framework. RESULTS: Thematic analysis of 80 full-text articles meeting inclusion criteria identified 4 overarching themes for AI governance: (1) technical prerequisites and model design; (2) clinical implementation and human factors; (3) ethics, safety, and trustworthiness; and (4) bias, fairness, and equity. Panel consensus evaluation resulted in the development of a 19-item framework. CONCLUSIONS: The consensus-driven framework presented here provides foundational guidance essential for navigating the complexities of implementing AI-enabled CDSS safely and ethically in surgery. Addressing the considerations outlined across these 4 core themes can facilitate the responsible adoption of AI, accelerating the transition toward an advanced, data-driven surgical practice while mitigating potential risks.
13 June 2026
Read appraisal →JMIR nursing
Simulation-Based Training for Nursing Students to Improve Patient Safety: Systematic Review
BACKGROUND: Patient safety is a fundamental pillar of health care quality. Simulation-based training provides a controlled environment for nursing students to develop safety competencies and error-recognition skills before clinical practice. OBJECTIVE: This systematic review aimed to describe and characterize the simulation-based education features and modalities used to address patient safety outcomes in undergraduate nursing students, identifying the strategies that contribute to improvements in safety-related competencies. METHODS: A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines across PubMed, Web of Science, Scopus, CINAHL, Cochrane, and Lilacs (2019-2024). Inclusion criteria focused on original studies involving undergraduate nursing students and simulation interventions measuring patient safety outcomes. Studies in languages other than English, Spanish, or Portuguese were excluded. Two reviewers independently performed study selection and data extraction. Methodological quality was assessed using Joanna Briggs Institute tools, applying a 60% quality threshold for inclusion. Results were synthesized through a narrative approach. RESULTS: A total of 20 studies from 12 countries were included. The methodological quality was high (n=14) and moderate (n=6). Findings revealed that high-fidelity simulation and virtual reality are the primary strategies used. Simulation proved effective in enhancing both technical skills (medication administration accuracy) and nontechnical skills (communication via SBAR [Situation, Background, Assessment, Recommendation] and ISBAR [Identification, Situation, Background, Assessment, Recommendation] tools, teamwork, and adverse event reporting). Key strategies contributing to safety included repetitive practice and interprofessional simulation, which significantly improved error detection and clinical judgment. CONCLUSIONS: Simulation is an essential pedagogical strategy for preparing nursing students to deliver safe care. Practical implications include the need to integrate structured simulation into nursing curricula to bridge the theory-practice gap. Future research should prioritize longitudinal designs to assess the retention of these safety skills in clinical settings and develop standardized metrics for measuring patient safety outcomes.
28 May 2026
Read appraisal →Journal of medical Internet research
Patients' Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study
BACKGROUND: Rapid developments in artificial intelligence (AI) will enable its widespread use in radiological diagnostics in the near future. Patients will then be confronted with findings generated with the help of AI. Understanding patients' perspectives on the use of this technology is one of the key factors for its successful implementation. OBJECTIVE: This qualitative study aimed to gain insight into patients' reasoning about the opportunities and risks of using AI in radiological diagnosis, to identify prerequisites for its acceptance, and to identify aspects that can promote trust in AI diagnoses, especially in scenarios of high personal concern. METHODS: A total of 7 focus groups were conducted with 34 patients (n=15, 44% female participants) aged between 23 and 85 years (mean 49.06, SD 17.08 y), recruited using purposive sampling strategies. Each focus group was audiotaped, transcribed, and analyzed using the method of structured qualitative content analysis. RESULTS: Study findings show that patients are open to the use of AI in radiological diagnostics. The basic prerequisites for this are (1) scientific evidence of safe outcomes that are more accurate and faster than those without AI; (2) recognizable added value in patient care; (3) transparency in the use of AI and disclosure to the patient; (4) comprehensive, binding measures for quality assurance; and (5) the use of AI solely to support the physician. However, the results indicate that further criteria are important for patients to be willing to choose a radiologist who uses AI and to trust AI diagnoses. In situations where they are personally affected, patients fear that physicians will place too much trust in the AI result and that the physician-patient relationship will become dehumanized. Therefore, the physicians' abilities and functions that inspire trust from the patients' perspective must come into play. These include (1) an independent diagnosis by the physician that takes into account not only the clinical context but also the individuality of the patient, (2) a comprehensible explanation of the pros and cons of using AI for patients and clear communication of AI output, and (3) a humane and empathetic physician-patient relationship, which shows that the physician continues to feel responsible for the patient. CONCLUSIONS: The results of the study underscore that a high quality of the entire "sociotechnical" system is an essential prerequisite for patient acceptance of the use of AI in radiological diagnostics and for trust in AI diagnoses. The further development of AI performance must go hand in hand with the creation of framework conditions for its use that meet patients' expectations of the role of the physician and ensure a trust-building physician-patient relationship. The study provides valuable insights into how such integration of AI into radiological practice can be achieved.
26 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Factors associated with unplanned healthcare encounters in pediatric cancer patients.
INTRODUCTION: Pediatric cancer therapies can cause complications that drive unplanned healthcare encounters (e.g., emergency visits and unplanned admissions). Characterizing their frequency and risk factors is important for resource planning, family counseling, and targeted mitigation strategies. In a secondary analysis of data from a cluster-randomized controlled trial of symptom monitoring in pediatric cancer patients, the primary objective was to describe the characteristics of unplanned healthcare encounters. The secondary objective was to identify risk factors associated with unplanned healthcare encounters. METHODS: This was a sub-analysis of a cluster-randomized trial of 10 sites randomized to symptom screening and 10 sites randomized to usual care. We included English- or Spanish-speaking pediatric patients newly diagnosed with cancer who were 8-18 years of age receiving any cancer treatment. At symptom screening sites, enrolled participants were reminded to complete symptom screening with Symptom Screening in Pediatrics Tool (SSPedi), a validated symptom screening and assessment, three times weekly for 8 weeks. The healthcare team was notified for severely bothersome symptoms. Emergency department visits, and unplanned clinic visits and admissions were determined from families and charts. Encounter documentation was reviewed to determine if the unplanned encounter was related to SSPedi symptoms. RESULTS: Among the 444 participants from 20 participating sites, there were 652 unplanned encounters and the most common were emergency room visits (n = 269) followed by unplanned clinic visits (n = 143) and unplanned admissions (n = 240). Pain was the primary reason for emergency room visits (59, 13.3%), unplanned clinic visits (52, 11.7%), and unplanned admissions (19, 4.3%). Nausea/vomiting was the next most common symptom for emergency room visits (40, 9.0%), unplanned clinic visits (4, 0.9%), and unplanned admissions (14, 3.2%). In multivariable analysis, leukemia diagnosis (P = 0.0003), site randomization (P = 0.018), and increasing physician full-time equivalents per 100 new cancer diagnoses (P = 0.032) were associated with more unplanned encounters. CONCLUSIONS: Unplanned healthcare utilization is common among newly diagnosed pediatric cancer patients. The association between unplanned healthcare utilization and standardized symptom screening and increasing physical full-time equivalents warrant further investigation. Key aspects to the implementation of routine symptom screening among pediatric patients with cancer will be resource allocation to prevent and manage common symptoms including those that may lead to unplanned healthcare visits.
22 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
A systematic review of blood pressure monitor validation studies
BACKGROUND: Good hypertension care requires accurate blood pressure (BP) measurement with BP monitors validated as accurate. The British and Irish Hypertension Society maintains a peer-reviewed list of validated monitors, independent of commercial interests. The list informs National Institute for Health and Care Excellence hypertension guidance and NHS monitor procurement. An updated universal standard protocol for BP device validation was introduced in 2018. AIM: Review the literature to establish which validation protocols are now being used; has this changed since publication of the universal protocol, and what proportion of recently published BP monitor validations fully meet protocol validation criteria? METHOD: Systematic review searches of Medline and Embase from 2018 to September 2025 identified BP monitor validation studies. Citations and full texts were screened independently by two authors. Included studies were reviewed independently by two authors against checklists of protocol criteria. Disagreements were resolved through discussion and/or arbitration by lead authors. RESULTS: Seven reviewers screened 1780 unique citations, reviewing 216 full texts to date. Nine reviewers have double-extracted data from 157 publications reporting 197 device validation studies. Overall, 104 (63%) used universal protocols. While 184 (93%) validations were reported as successful by authors, only 146 (74%) fulfilled all protocol criteria on review. Thus, we considered 51 (26%) published successful validations inadequate, due to non-fulfilment of key validation criteria and/or violations of BP measurement protocols such as incomplete BP range coverage. CONCLUSION: This review highlights inadequacies in current journal peer-review processes, justifying our approach to provide impartial guidance for health professionals, purchasers, and the public.
16 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Speed and quality of complex strategic decisions
Many strategic decisions involve both substantial complexity and time pressure, but the association between decision speed and decision quality of cognitively demanding strategic decisions is not well understood. This paper presents evidence on this question using a setting with exceptionally detailed and precise information about decision times and decision quality-it analyses move-by-move data from in-person professional chess tournaments. Decision quality is measured by comparing actual moves to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results show that faster decisions are associated with higher decision quality, even after accounting for computational complexity, distinctiveness between alternatives, and time pressure. Greater computational complexity and lower distinctiveness between move alternatives are associated with longer decision times, whereas greater time pressure is associated with shorter decision times. All three factors are associated with lower decision quality. We discuss the findings against the predictions of different decision models in which individuals sequentially acquire information about alternatives with uncertain valuations, extending theories originally developed in the context of nonstrategic decisions to a strategic environment.
15 May 2026
Read appraisal →Journal of medical Internet research
Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review
BACKGROUND: Artificial intelligence (AI) in the domain of health care is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. OBJECTIVE: This study aimed to explore barriers and facilitators influencing patients' acceptance of AI. METHODS: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Nine databases, including PubMed, Web of Science, and Embase, were comprehensively searched from inception to December 23, 2025. We included qualitative, quantitative, and mixed methods studies investigating adult patients' attitudes toward medical AI. Two researchers independently screened records, extracted data, and appraised methodological quality using the Mixed Methods Appraisal Tool. Following the Joanna Briggs Institute convergent integrated approach, data synthesis was guided by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Theoretical Domains Framework (TDF). Factors were mapped to behavior change techniques (BCTs) and evaluated for practical feasibility using the Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects/safety, and Equity criteria. RESULTS: A total of 61 studies met the inclusion criteria out of 7452 search results. Study designs included qualitative (n=20), quantitative (n=35), and mixed methods (n=6). Performance and effort expectancies were the primary determinants of acceptance. Major barriers comprised perceived operational complexity, lack of algorithmic trust, reduced interpersonal interaction, privacy vulnerabilities, and high costs. Facilitators included transparent data governance, interpretability of AI decisions, improved clinician-patient communication, and human-centered design. Education level and disease severity emerged as key moderating variables. Through UTAUT2-TDF mapping, we identified 25 distinct BCTs (6 high, 14 medium, and 5 low feasibility) and formulated 40 actionable intervention strategies. CONCLUSIONS: This study innovatively integrates the UTAUT2 and TDF frameworks to evaluate patient acceptance of medical AI. Unlike existing reviews that predominantly evaluate isolated psychosocial factors or purely technical attributes, this transtheoretical approach differentiates itself by merging technology adoption mechanisms directly with behavioral drivers. Consequently, it contributes to the field by systematically identifying multilevel factors influencing acceptance, including performance expectancy, effort expectancy, and ethical security, and translating these into 40 actionable BCTs. In real-world clinical practice, these findings provide a feasible, prioritized blueprint for clinicians and administrators to design patient-centered interventions, enhancing the clinical integration and long-term effectiveness of medical AI.
12 May 2026
Read appraisal →Neurology
Virtual vs In-Person Neurologic Ambulatory Care: A Case-Control Study of Subsequent Health Care Utilization
BACKGROUND AND OBJECTIVES: Implementation of telemedicine expanded options for outpatient neurology care. It remains uncertain which new neurology patients can be appropriately evaluated virtually. We compared subsequent health care utilization after virtual vs in-person new patient neurology visits across 3 academic medical centers. METHODS: We conducted a retrospective multicenter cohort study of adults with a new outpatient neurology visit from September 2020 through December 2021 using the Vizient Clinical Data Base and Clinical Practice Solutions Center databases. Virtual and in-person patients were matched 1:1 using propensity scores incorporating demographics, clinical characteristics, time period, and previous health care utilization. Outcomes were analyzed overall and stratified by neurologic chief complaint category and institution. We compared rates of subsequent neurologic clinic follow-up, emergency department (ED) visits, and hospitalizations after virtual and in-person encounters. Testing and all-cause ED visits/hospitalizations were also assessed. RESULTS: We identified 10,428 virtual and 36,767 in-person neurology new outpatient visits. After propensity score matching, 8,202 virtual visits were matched to 8,202 in-person visits. Neurology follow-up within 90 days did not differ between virtual and in-person visits (24.6% vs 23.7%, p = 0.18). Thirty-day neurology clinic follow-up was slightly lower after virtual visits, whereas follow-up at 6 months and 1 year was similar between groups. Neurologic ED visits and hospitalizations within 90 days were similar (0.9% vs 0.8%, p = 0.23 and 1.8% vs 1.7%, p = 0.47, respectively). All-cause ED visits and hospitalizations within 90 days were also comparable (1.8% vs 1.7%, p = 0.59 and 2.2% vs 1.8%, p = 0.13, respectively). Analyses by chief complaint found that 90-day follow-up was higher after in-person visits for dementia, whereas 30- and 90-day follow-up was higher after virtual visits for Parkinson disease and multiple sclerosis, and 90-day follow-up was higher after virtual visits for headache. Testing was more frequent after in-person visits for certain chief complaints. DISCUSSION: In this propensity score-matched multicenter cohort, new neurology patients seen virtually had similar downstream utilization as those seen in-person, including comparable 90-day follow-up and similar neurologic and all-cause ED visits and hospitalizations. Although follow-up varied modestly by chief complaint and testing was more frequent after some in-person visits, no major differences emerged overall.
25 Apr 2026
Read appraisal →Medical education online
Medical students' attitudes towards older persons - a systematic review and meta-analysis
OBJECTIVE: Ageism negatively affects older people's health, well-being, and quality of care. Identifying ageism and attitudes toward older adults may help identify medical students motivated to work with a growing older population. We conducted a systematic review of interventions to reduce ageism among medical students. METHODS: We conducted a systematic literature retrieval in Medline, the Cochrane Library, Epistemonikos and PubMed via Ovid, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, from database inception to August 23rd, 2024. We searched for studies examining medical students' attitudes towards older persons, including interventions to influence these attitudes. RESULTS: The systematic review included 21 studies. Study samples ranged between 29 and 415, with a pooled total of 3940 medical students, the majority conducted in the USA. The meta-analysis of students' attitudes before and after all interventions included a total of 2004 students, yielding a non-significant standardized mean difference (SMD) of 0, 12 (95% CI -0, 01 to 0, 26). The meta-analysis of students' attitudes before and after all interventions (35-37, 39, 41, 45, 48, 50), after removing UCLA-GAS and R-GAS, included a total of 1344 students with a standardized mean difference of 0, 23 (95% CI 0, 06 to 0, 40). Analysis of empathy-based teaching interventions included 1652 students, and showed a statistically significant effect, SMD 0, 18 (95% CI 0, 01 to 0, 34). Analysis of 352 students receiving traditional teaching methods yielded no effect on ageism, with an SMD -0, 13 (95% CI -0, 81 to 0, 54). High heterogeneity (I2 75-85%) affects our confidence in the effect estimates. CONCLUSION: Empathy-based interventions seem to improve medical students' attitudes towards older persons. Traditional teaching methods may increase negative attitudes. Considering the high heterogeneity, the results should be interpreted cautiously. Adding empathy-based components to medical curricula could combat ageism. More studies are needed to examine whether the results of studies from North America are valid globally.
23 Apr 2026
Read appraisal →Genetics
Mondo: integrating disease terminology across communities
Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities-including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology-a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene-disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.
5 Apr 2026
Read appraisal →Patient education and counseling
Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication
OBJECTIVE: To examine the influence of the emerging use of generative artificial intelligence (GenAI) within electronic health records and among the public on the patient-centeredness of communication in healthcare. METHOD: In this scoping review, we conducted a systematic search for peer-reviewed studies in PubMed and PsycInfo that empirically examined GenAI involvement in clinical communication. We then mapped study findings onto a well-established framework for patient-centered communication. RESULTS: Our search yielded 67 studies for analysis. Results suggest that integration of GenAI into healthcare communication has the potential to increase clinician efficiency in interacting with patients, to expand channels for patients to obtain information about their healthcare, and to enhance empathy in clinical communication. However, findings also indicate variability in the quality of information produced by GenAI, the potential for GenAI to recast the clinician as a technical supervisor rather than a humanistic care provider, and several issues of equity and privacy raised by engagement with GenAI. CONCLUSION: As GenAI becomes more prevalent in healthcare, rigorous examination of GenAI is needed to ensure that its development and implementation aids rather than hinders patient-centered communication. We conclude with an agenda for further research on GenAI grounded in the PCC framework underlying our review. PRACTICE IMPLICATIONS: Findings from this review highlight the current potential benefits and limitations of GenAI as a third party to clinical communication. Continued efforts toward developing and applying GenAI for effective healthcare communication should focus on protecting patients from potential drawbacks and maximizing nascent benefits for patient-centered communication.
4 Apr 2026
Read appraisal →International journal of nursing studies
The use of large language models in clinical documentation: A scoping review
BACKGROUND: Clinical documentation is essential for safe, high-quality care but has become increasingly complex, contributing to clinician burnout. Large language models offer potential to ease documentation by generating summaries, structuring data, and ensuring compliance. However, concerns remain regarding accuracy, bias, privacy, and regulatory risks. OBJECTIVE: To map current literature on large language models applications in clinical documentation, evaluating their benefits, limitations, and ethical considerations. INFORMATION SOURCES: Five electronic databases (i.e., PubMed, Scopus, CINAHL, Cochrane Library, and IEEE Xplore) covering peer-reviewed literature published in English between January 2009 and August 2025. METHODS: This scoping review followed Arksey and OMalleys framework and was reported in accordance with PRISMA-ScR guidelines. Screening, data extraction, and quality appraisal were conducted independently by multiple reviewers using Joanna Briggs Institute tools. Findings were synthesized using descriptive and narrative approaches. RESULTS: Forty-one studies met inclusion criteria, most originating from the United States. Large language models were primarily applied to clinical note generation, discharge summaries, and provider-patient encounter documentation. Key evaluation metrics included content accuracy, linguistic quality, and summarization performance. Large language models demonstrated potential to improve documentation efficiency and readability, with some studies reporting up to 40 % time savings. However, concerns about factual inaccuracies, hallucinations, and reduced performance in complex cases were common. Clinician perceptions were mixed. Some found notes generated by large language models helpful and well-structured, while others raised concerns about reliability, liability, and loss of clinical nuance. Ethical challenges included data privacy, security, and algorithmic bias, with varying levels of compliance across settings. CONCLUSIONS: Large language models hold significant promise for enhancing clinical documentation by improving efficiency, standardization, and clarity. However, their safe and effective use requires rigorous attention to accuracy, ethical safeguards, and clinician trust. Integration must support, rather than supplant, clinical reasoning and patient-centered care. Co-design with clinicians, real-world evaluation, and artificial intelligence literacy are essential to ensure that these technologies augment, not erode, professional judgment and care quality. REGISTRATION: Open Science Framework Registries (https://osf.io/m4h3q).
2 Apr 2026
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