Evidence map›Paper›PMID 41115171›Full record

SynthesisBritish medical bulletin2025

AI chatbots versus human healthcare professionals: a systematic review and meta-analysis of empathy in patient care.

Alastair Howcroft, Amber Bennett-Weston, Ahmad Khan, Joseff Griffiths, Simon Gay, Jeremy Howick

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in British medical bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

37 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study.International journal of qualitative studies on health and well-being · 2026
    Article
  6. Article
  7. Article
  8. Audit of Patient Communications Following Transoral Robotic Surgery.World journal of otorhinolaryngology - head and neck surgery · 2026
    Article
  9. Article
  10. Article
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  12. Article
  13. Review
  14. The Empathy Paradox: AI in Clinical Diagnosis.Journal of evaluation in clinical practice · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

6 authors.

Alastair HowcroftSchool of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, Nottinghamshire NG8 1BB, United Kingdom.ORCID 0009-0000-8445-3880
Amber Bennett-WestonStoneygate Centre for Empathic Healthcare, Leicester Medical School, University of Leicester, George Davies Centre, Lancaster Road, Leicester, Leicestershire LE1 7HA, United Kingdom.ORCID 0000-0001-5981-9393
Ahmad KhanLeicester Medical School, University of Leicester, George Davies Centre, Lancaster Road, Leicester, Leicestershire LE1 7HA, United Kingdom.ORCID 0009-0002-4314-5747
Joseff GriffithsSchool of Medicine, University of Nottingham, Queen's Medical Centre, Derby Road, Nottingham, Nottinghamshire NG7 2UH, United Kingdom.
Simon GayLeicester Medical School, University of Leicester, George Davies Centre, Lancaster Road, Leicester, Leicestershire LE1 7HA, United Kingdom.ORCID 0000-0002-1743-9824
Jeremy HowickStoneygate Centre for Empathic Healthcare, Leicester Medical School, University of Leicester, George Davies Centre, Lancaster Road, Leicester, Leicestershire LE1 7HA, United Kingdom.ORCID 0000-0003-0280-7206

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmpathy is widely recognized for improving patient outcomes, including reduced pain and anxiety and improved satisfaction, and its absence can cause harm. Meanwhile, use of artificial intelligence (AI)-based chatbots in healthcare is rapidly expanding, with one in five general practitioners using generative AI to assist with tasks such as writing letters. Some studies suggest AI chatbots can outperform human healthcare professionals (HCPs) in empathy, though findings are mixed and lack synthesis. SOURCES OF DATA: We searched multiple databases for studies comparing AI chatbots using large language models with human HCPs on empathy measures. We assessed risk of bias with ROBINS-I and synthesized findings using random-effects meta-analysis where feasible, whilst avoiding double counting. AREAS OF AGREEMENT: We identified 15 studies (2023-2024). Thirteen studies reported statistically significantly higher empathy ratings for AI, with only two studies situated in dermatology favouring human responses. Of the 15 studies, 13 provided extractable data and were suitable for pooling. Meta-analysis of those 13 studies, all utilising ChatGPT-3.5/4, showed a standardized mean difference of 0.87 (95% CI, 0.54-1.20) favouring AI (P < .00001), roughly equivalent to a two-point increase on a 10-point scale. AREAS OF CONTROVERSY: Studies relied on text-based assessments that overlook non-verbal cues and evaluated empathy through proxy raters. GROWING POINTS: Our findings indicate that, in text-only scenarios, AI chatbots are frequently perceived as more empathic than human HCPs. AREAS TIMELY FOR DEVELOPING RESEARCH: Future research should validate these findings with direct patient evaluations and assess whether emerging voice-enabled AI systems can deliver similar empathic advantages.

Indexed as

Artificial IntelligenceEmpathyHealth PersonnelPatient CareGenerative Artificial IntelligenceHumansartificial intelligencedigital healthempathymeta-analysispatient-centred carephysician-patient relationssystematic review

Identifiers

PMID41115171
PMCPMC12536877

What OpenQuestion holds

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Registered trials

None linked

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.