ArticleJMIR medical education2024
ChatGPT in Medical Education: A Precursor for Automation Bias?
Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed.
- Integrating Large Language Models Into Trauma Education for Medical Students: Randomized Controlled Pilot Trial.JMIR medical education · 2026Trial
- Integrating ChatGPT into knowledge-retrieval tutorials in undergraduate medical education: a prospective evaluation of higher-order learning and feasibility.Medical education online · 2026Article
- Artificial intelligence in cardiovascular medicine: a practical guide for clinicians and researchers.European heart journal. Digital health · 2026Review
- Current Landscape of Generative AI Use as a Search Engine Among Resident Physicians: Cross-Sectional Study.JMIR AI · 2026Article
- Addressing Clinician-Educator Hesitancy Toward Artificial Intelligence Through a Peer-Led Instructional Design.Cureus · 2026Article
- Pediatric Autism Diagnosis Accuracy and Confidence: A Comparison of Experienced and Inexperienced Clinicians Making Decisions with and without AI Decision Support.Research square · 2026Article
- Mixed-methods study on GenAI Usage, dependence behaviors, and standardized application paths among Chinese medical students.NPJ digital medicine · 2026Article
- Conversational artificial intelligence in the operating room: an exploratory cross-sectional single-center survey among anesthesiology residents.BMC anesthesiology · 2026Observational
- Feasibility of Large Language Model-Based Standardized Virtual Patients to Support Clinical Decision-Making Training in Operative Dentistry: Mixed Methods Study.JMIR formative research · 2026Article
- The AI adoption paradox in Chinese medical education: a multi-institution cross-sectional study of usage patterns, critical literacy gaps, and adoption profiles among 3,194 undergraduate medical students.BMC medical education · 2026Article
- The Influencing Factors of Medical Postgraduates' Usage Intention Toward Artificial Intelligence-Generated Content Tools in Academic Research: Qualitative Analysis.Journal of medical Internet research · 2026Article
- Assessing the accuracy and educational value of ChatGPT-generated content for core topics in cardiology: a descriptive analysis at Selçuk University Cardiology Clinic.BMC medical education · 2026Article
- A systematic review of the limitations of large language models in generating healthcare content.PLOS digital health · 2026Article
- Validation of the Use of a Large Language Model for Detecting Sentiment in Student Course Evaluation.Family medicine · 2026Article
- Uptake of Large Language Models by London Medical Students: Exploratory Qualitative Interview Study.JMIR formative research · 2026Article
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- What and when: a Delphi study identifying AI competencies and learning objectives for medical students.Frontiers in digital health · 2026Article
- Patients' perception towards large language models in otorhinolaryngology, head and neck surgery: a single-centre survey.Frontiers in digital health · 2026Article
- From writing paragraphs to evaluating articles: academic vocabulary development in EFL health sciences students during the ChatGPT transition.Frontiers in medicine · 2026Article
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No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) in health care has the promise of providing accurate and efficient results. However, AI can also be a black box, where the logic behind its results is nonrational. There are concerns if these questionable results are used in patient care. As physicians have the duty to provide care based on their clinical judgment in addition to their patients' values and preferences, it is crucial that physicians validate the results from AI. Yet, there are some physicians who exhibit a phenomenon known as automation bias, where there is an assumption from the user that AI is always right. This is a dangerous mindset, as users exhibiting automation bias will not validate the results, given their trust in AI systems. Several factors impact a user's susceptibility to automation bias, such as inexperience or being born in the digital age. In this editorial, I argue that these factors and a lack of AI education in the medical school curriculum cause automation bias. I also explore the harms of automation bias and why prospective physicians need to be vigilant when using AI. Furthermore, it is important to consider what attitudes are being taught to students when introducing ChatGPT, which could be some students' first time using AI, prior to their use of AI in the clinical setting. Therefore, in attempts to avoid the problem of automation bias in the long-term, in addition to incorporating AI education into the curriculum, as is necessary, the use of ChatGPT in medical education should be limited to certain tasks. Otherwise, having no constraints on what ChatGPT should be used for could lead to automation bias.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.