ArticleClinical medicine (London, England)2026
Can artificial intelligence help with the development of generic clinical skills when breaking bad news? A quantitative evaluation.
Article in Clinical medicine (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for affective-domain development in healthcare professions education: a systematic review.Frontiers in medicine · 2026Pooled it
- Hypertension: A common, yet poorly managed condition. What can we do better?Clinical medicine (London, England) · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectiveArtificial intelligence (AI) is being increasingly applied in medical education, yet its role in developing complex communication skills, such as breaking bad news, is less well defined. The Joint Royal Colleges of Physicians Training Board (JRCPTB) explored whether AI-generated guidance could complement or enhance traditional human-facilitated teaching methods in this sensitive domain.
designTwo sets of guidance on breaking bad news were developed: one using a generative AI chatbot (ChatGPT) and the other through facilitated discussions among trainers and resident doctors. SETTING AND
participantsEach approach generated a series of summarised statements that were presented to members of the JRCPTB Specialty Advisory Committees (SACs) via an anonymous online consultation.
main outcome measuresRespondents rated their agreement with each statement using a five-point Likert scale and provided qualitative feedback. Demographic data were collected to assess variations in preferences.
resultsA total of 80 assessments were completed for the traditional approach and 75 for the AI approach, involving 69 doctors across 19 specialties and 11 lay representatives. Both approaches produced a common core of 11 statements with high agreement (>85%) alongside unique statements specific to each method. Overall, 61% of respondents preferred the AI-generated content, 22% preferred the traditional approach and 17% expressed no preference. Female respondents showed a statistically significant preference for the AI approach (p = 0.003) although small study numbers restrict generalisability. No differences were found based on age, ethnicity or training background.
conclusionsAI-generated content, when appropriately curated, can effectively support the teaching of sensitive communication skills, complementing traditional reflective learning methods. A hybrid model that integrates AI with human-facilitated discussions may offer a comprehensive and efficient approach to postgraduate medical education. Further research is warranted to ensure content quality, cultural and setting appropriateness, and to preserve trust in the supervisor-learner relationship.
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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.