Evidence map›Paper›PMID 38602313›Full record

ArticleJMIR medical education2024

Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study.

Fumitoshi Fukuzawa, Yasutaka Yanagita, Daiki Yokokawa, Shun Uchida, Shiho Yamashita, Yu Li, Kiyoshi Shikino, Tomoko Tsukamoto, Kazutaka Noda, Takanori Uehara and 1 more

Abstract read
In one paragraph

Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

11 citing papers in PubMed.

  1. Article
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  10. PICOT questions and search strategies formulation: A novel approach using artificial intelligence automation.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025
    Article
  11. 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

11 authors.

Fumitoshi FukuzawaDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0009-0007-5853-387X
Yasutaka YanagitaDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0002-9213-8247
Daiki YokokawaDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0003-0944-8664
Shun UchidaUchida Internal Medicine Clinic, Saitama-shi, Japan.ORCID 0000-0003-3408-9763
Shiho YamashitaDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0009-0008-5694-7419
Yu LiDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0002-7978-9627
Kiyoshi ShikinoDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0002-3721-3443
Tomoko TsukamotoDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0002-1350-203X
Kazutaka NodaDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0003-0017-5022
Takanori UeharaDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0001-5086-5799
Masatomi IkusakaDepartment of General Medicine, Chiba University Hospital, Chiba-shi, Japan.ORCID 0000-0002-9760-5596

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective: This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods: Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results: ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. Conclusions: Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.

Indexed as

Artificial IntelligenceMedicineHumansLaboratoriesMental ProcessesPhysical ExaminationaccuracyadolescentadolescentsAIAI diagnosisAI in medicineartificial intelligenceChatGPTchildchildrendiagnostic accuracydigital healthelderelderlyfemaleinvestigativelaboratory investigationlaboratory investigationsmalemedical diagnosismedical historymHealthmobile healtholder adultolder adultsolder peopleolder personpatient historyphysical examinationphysical examinationspublic healthteenteenagerteenagersteenstreatmentUnited Statesyouth

Identifiers

PMID38602313
PMCPMC11024399

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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.