Evidence map›Paper›PMID 38015298›Full record

ArticleJournal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology2023

Arthrosis diagnosis and treatment recommendations in clinical practice: an exploratory investigation with the generative AI model GPT-4.

Stefano Pagano, Sabrina Holzapfel, Tobias Kappenschneider, Matthias Meyer, Günther Maderbacher, Joachim Grifka, Dominik Emanuel Holzapfel

Abstract read
In one paragraph

Article in Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 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

19 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  17. Exploring the landscape of AI-assisted decision-making in head and neck cancer treatment: a comparative analysis of NCCN guidelines and ChatGPT responses.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
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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.

Stefano PaganoDepartment of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, Bad Abbach, Germany. stefano.pagano@ukr.de.ORCID http://orcid.org/0009-0006-6721-7564
Sabrina HolzapfelDepartment of Neonatology, University Children's Hospital Regensburg, Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany.
Tobias KappenschneiderDepartment of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, Bad Abbach, Germany.
Matthias MeyerDepartment of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, Bad Abbach, Germany.
Günther MaderbacherDepartment of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, Bad Abbach, Germany.
Joachim GrifkaDepartment of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, Bad Abbach, Germany.
Dominik Emanuel HolzapfelDepartment of Orthopaedic Surgery, University of Regensburg, Asklepios Klinikum, Bad Abbach, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe spread of artificial intelligence (AI) has led to transformative advancements in diverse sectors, including healthcare. Specifically, generative writing systems have shown potential in various applications, but their effectiveness in clinical settings has been barely investigated. In this context, we evaluated the proficiency of ChatGPT-4 in diagnosing gonarthrosis and coxarthrosis and recommending appropriate treatments compared with orthopaedic specialists.

methodsA retrospective review was conducted using anonymized medical records of 100 patients previously diagnosed with either knee or hip arthrosis. ChatGPT-4 was employed to analyse these historical records, formulating both a diagnosis and potential treatment suggestions. Subsequently, a comparative analysis was conducted to assess the concordance between the AI's conclusions and the original clinical decisions made by the physicians.

resultsIn diagnostic evaluations, ChatGPT-4 consistently aligned with the conclusions previously drawn by physicians. In terms of treatment recommendations, there was an 83% agreement between the AI and orthopaedic specialists. The therapeutic concordance was verified by the calculation of a Cohen's Kappa coefficient of 0.580 (p < 0.001). This indicates a moderate-to-good level of agreement. In recommendations pertaining to surgical treatment, the AI demonstrated a sensitivity and specificity of 78% and 80%, respectively. Multivariable logistic regression demonstrated that the variables reduced quality of life (OR 49.97, p < 0.001) and start-up pain (OR 12.54, p = 0.028) have an influence on ChatGPT-4's recommendation for a surgery.

conclusionThis study emphasises ChatGPT-4's notable potential in diagnosing conditions such as gonarthrosis and coxarthrosis and in aligning its treatment recommendations with those of orthopaedic specialists. However, it is crucial to acknowledge that AI tools such as ChatGPT-4 are not meant to replace the nuanced expertise and clinical judgment of seasoned orthopaedic surgeons, particularly in complex decision-making scenarios regarding treatment indications. Due to the exploratory nature of the study, further research with larger patient populations and more complex diagnoses is necessary to validate the findings and explore the broader potential of AI in healthcare. LEVEL OF EVIDENCE: Level III evidence.

Indexed as

Osteoarthritis, HipOsteoarthritis, KneeArtificial IntelligenceHumansKnee JointQuality of LifeArthrosisArtificial intelligenceChatGPT-4Large language modelOrthopaedicsTotal joint replacement

Identifiers

PMID38015298
PMCPMC10684473

What OpenQuestion holds

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LicenceCC BY
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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.