Evidence map›Paper›PMID 40983921›Full record

ArticleBMC primary care2025

Artificial intelligence in the prescription of acute medical treatments in primary healthcare - comparison of the performance of family physicians and ChatGPT.

Bárbara Lemos Pereira Simão, Carolina Moura Pereira, Mariana Jácome, Catarina Oliveira, Laura Martins Ferreira, José Miguel Paiva, Carlos Braga, Carlos Seiça Cardoso

Abstract readComparative Study
In one paragraph

Article in BMC primary care, 2025. 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.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. 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

8 authors.

Bárbara Lemos Pereira SimãoFaculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal. barbara12simao@gmail.com.ORCID 0009-0003-4384-6903
Carolina Moura PereiraUnidade de Saúde Familiar Norton de Matos, Coimbra, Portugal.ORCID 0000-0003-3636-0414
Mariana JácomeUnidade de Saúde Familiar Condeixa, Coimbra, Portugal.ORCID 0000-0001-8957-1990
Catarina OliveiraUnidade de Saúde Familiar Condeixa, Coimbra, Portugal.ORCID 0000-0002-9367-7533
Laura Martins FerreiraUnidade de Saúde Familiar Briosa, Coimbra, Portugal.ORCID 0009-0007-3445-0885
José Miguel PaivaUnidade de Saúde Familiar Briosa, Coimbra, Portugal.ORCID 0009-0004-9267-6901
Carlos BragaUnidade de Saúde Familiar Norton de Matos, Coimbra, Portugal.ORCID 0009-0005-1180-9889
Carlos Seiça CardosoFaculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal.ORCID 0000-0001-9214-6995

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence (AI) is increasingly being recognized as a transformative force in healthcare, showing significant promise in supporting healthcare professionals. AI has many applications in healthcare, including providing real-time decision support, diagnosing diseases, and advancing personalized medicine. However, clinical trials and further research are needed to evaluate the practical effectiveness of AI in primary healthcare. OBJECTIVE OF THE STUDY: This study aims to assess the accuracy of ChatGPT, an AI-powered chatbot, in therapeutic decision-making during acute disease consultations in primary care and compare its performance to that of general family physicians. The goal was to determine how well ChatGPT could replicate the decisions made by physicians based on standard clinical guidelines. MATERIALS AND

methodsA cross-sectional study was conducted at three primary healthcare units in the Central Region of Portugal. The analysis involved three phases: (1) collecting data from healthcare professionals, (2) gathering therapeutic proposals from ChatGPT v3.5 based on physician-defined diagnoses, and (3) comparing the treatments proposed by both ChatGPT v3.5 and the physicians, using the Dynamed platform as the gold standard for correct prescriptions.

resultsOut of a total of 860 consultations, 138 were excluded due to non-compliance with the inclusion criteria. The analysis showed that the diagnostic accuracy of ChatGPT v3.5 and physicians co-occurred in 26.2% of cases. In 29.1% of cases, there was no agreement between the AI and the physicians' diagnoses. The therapeutic decisions made by ChatGPT v3.5 were correct in 55.6% of the cases, while physicians made correct decisions in 54.3% of the cases. The therapeutic decisions of ChatGPT v3.5 were incorrect in 5.2% of the cases, compared to 11% for physicians. Furthermore, the therapeutic proposals of ChatGPT v3.5 were 'approximate' to the correct treatment in 24% of the cases, while physicians had a 17.1% approximation rate.

conclusionThis study suggests that AI - specifically ChatGPT v3.5 - can match or even outperform physicians in terms of therapeutic decision accuracy, with a similar or slightly better success rate than human doctors. This highlights the potential for AI to act as an effective auxiliary tool rather than a replacement for healthcare professionals. AI is most effective when used in collaboration with healthcare professionals, augmenting their capabilities and improving overall healthcare delivery. Ultimately, AI can serve as a powerful aid to healthcare professionals, helping improve patient care and healthcare outcomes, particularly in primary care.

Indexed as

Artificial IntelligenceClinical Decision-MakingPhysicians, FamilyPractice Patterns, Physicians'Primary Health CareAcute DiseaseAdultCross-Sectional StudiesDecision Support Systems, ClinicalFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPortugalArtificial intelligenceChatGPTPrimary healthcareTreatment

Identifiers

PMID40983921
PMCPMC12455777

What OpenQuestion holds

Textmetadata
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.