Evidence map›Paper›PMID 41013444›Full record

ArticleBMC medical informatics and decision making2025

Evaluating the performance of ChatGPT in clinical multidisciplinary treatment: a retrospective study.

Xueqi Wang, Jianhua Guo, Tao Zhang, Huajun Lu, Dandan Zhou, Haitao Zhang, Xuebin Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. ChatGPT in precision medicine.APL bioengineering · 2026
    Review
  3. Review
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

7 authors.

Xueqi WangDepartment of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No.150, Jimo Road, Pudong New Area, Shanghai, 200120, China.
Jianhua GuoDepartment of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No.150, Jimo Road, Pudong New Area, Shanghai, 200120, China.
Tao ZhangDepartment of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No.150, Jimo Road, Pudong New Area, Shanghai, 200120, China.
Huajun LuDepartment of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No.150, Jimo Road, Pudong New Area, Shanghai, 200120, China.
Dandan ZhouDepartment of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No.150, Jimo Road, Pudong New Area, Shanghai, 200120, China.
Haitao Zhang *Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No.150, Jimo Road, Pudong New Area, Shanghai, 200120, China. boy8672@126.com.
Xuebin Wang *Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No.150, Jimo Road, Pudong New Area, Shanghai, 200120, China. 1400094@tongji.edu.cn.ORCID 0000-0002-2806-2065

Funding

Key Discipline Support Initiative for Emergency and Critical Care Medicine within the Shanghai Health System 2023ZDFC0104
6 · The paper itself

Abstract

backgroundMultidisciplinary treatment (MDT) consultations are essential for managing complex patients. However, resource and time constraints can limit their quality. Large language models (LLMs) have shown potential in assisting clinical decision-making, but their performance in complex MDT scenarios remains unclear. This study aims to evaluate the quality of MDT recommendations generated by ChatGPT compared to those provided by physicians.

methodsClinical data from 64 patient cases were retrospectively included in the study. ChatGPT was asked to provide specific MDT recommendations. 2 experienced physicians evaluated and scored the responses in a blinded manner across 5 aspects: comprehensiveness, accuracy, feasibility, safety, and efficiency, each assessed by 2 questions.

resultsThe median overall score for ChatGPT was 41.0 out of 50.0, which was lower than the MDT physicians' median score of 43.5 (p = 0.001). Compared to the MDT physicians' responses, ChatGPT excelled in comprehensiveness (p < 0.001) but fell short in accuracy (p < 0.001), feasibility (p < 0.001), and efficiency (p = 0.003). Analysis of specific questions revealed that ChatGPT lacked the ability to reason through the etiologies of complex cases.

conclusionThis study indicates that ChatGPT has potential in clinical MDT applications, particularly in demonstrating more comprehensive consideration of clinical factors. However, ChatGPT still has deficiencies in accuracy, which could lead to incorrect healthcare decisions. Therefore, further development and clinical validation of LLMs are necessary. Recognizing the current limitations of LLMs, it is essential to use them with caution in clinical practice.

trial registrationNot applicable to the present retrospective study. For transparency, a related prospective extension is registered at ChiCTR (ChiCTR2400088563; registered on 21 August 2024).

Indexed as

Clinical Decision-MakingPatient Care TeamAdultAgedFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedRetrospective StudiesChatGPTClinical decision supportLarge language modelsMultidisciplinary treatmentRecommendation

Identifiers

PMID41013444
PMCPMC12465737

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.