Evidence map›Paper›PMID 42805438›Full record

ArticleClinical medicine (London, England)2026

Evaluating the use of artificial intelligence and human guidance to teach generic and specialist skills in breaking bad news: A mixed methods exploration.

Miriam E Armstrong, Philip Bright, Stephen Glen, David Marshall, Michael C Jones

Abstract read
In one paragraph

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. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Miriam E ArmstrongFederation of the Royal Colleges of Physicians of the United Kingdom, 11 St Andrews Place, Regent's Park, London NW1 4LE, United Kingdom. Electronic address: Miriam.Armstrong@thefederation.uk.
Philip BrightNHS England West Midlands Office, 23 Stephenson Street, Birmingham B2 4HQ, United Kingdom. Electronic address: philip.bright@nhs.net.
Stephen GlenFederation of the Royal Colleges of Physicians of the United Kingdom, 11 St Andrews Place, Regent's Park, London NW1 4LE, United Kingdom. Electronic address: stephen.glen@thefederation.uk.
David MarshallFederation of the Royal Colleges of Physicians of the United Kingdom, 11 St Andrews Place, Regent's Park, London NW1 4LE, United Kingdom. Electronic address: David.Marshall@thefederation.uk.
Michael C JonesFederation of the Royal Colleges of Physicians of the United Kingdom, 11 St Andrews Place, Regent's Park, London NW1 4LE, United Kingdom. Electronic address: Mike.Jones@thefederation.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveBreaking bad news (BBN) is specified in the General Medical Council's Generic Professional Capabilities framework, which sets out core skills to be developed alongside curricula-specified capabilities in postgraduate training. To date, a wide variety of approaches to teaching BBN have been used, however, the application of artificial intelligence (AI) to learning in this field remains in its early stages and requires further investigation. DESIGN, SETTING, PARTICIPANTS AND

interventionTwo approaches were compared in response to the question What is the best way to break bad news?: a human-facilitated discussion versus an AI-generated response using ChatGPT-4. Both approaches produced high-level guidance condensed into short statements for online mixed methods evaluation by experienced medical educators. OUTCOME MEASURES AND

results41 doctors from 17 specialties and eight lay representatives provided free-text commentary as part of the assessment. Overall findings were compared with popular patient-focused BBN protocols, such as SPIKES, with qualitative analysis providing more details of skills requirements for generic and specialist levels of practice by adding more depth and nuance via three themes: patient experience, professionalism and access to support. Both approaches matched well with patient-focused protocols albeit they also addressed doctors' debriefing after BBN.

conclusionsOverall, AI produced more comprehensive, highly rated guidance suitable for early careers, but qualitative evaluation highlighted the need for more specialised skills, their nature and application, plus differences in preferred style and content between approaches. The value of tailoring communication, via a personalised approach developed from real-life experience, was also confirmed.

Indexed as

Artificial intelligenceBreaking bad newsMixed methods: Qualitative analysisPostgraduate training

Identifiers

PMID42805438
PMCPMC13677254

What OpenQuestion holds

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Registered trials

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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.