Evidence map›Paper›PMID 38792666›Full record

ArticleLife (Basel, Switzerland)2024

Integrating AI in Lipedema Management: Assessing the Efficacy of GPT-4 as a Consultation Assistant.

Tim Leypold, Lara F Lingens, Justus P Beier, Anja M Boos

Registry-linked trialAbstract read
In one paragraph

Article in Life (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07485465 (The Role of Chat GPT in the Diagnosis and Treatment of Lymphedema), which is not on this map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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.

NCT07485465 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

The Role of Chat GPT in the Diagnosis and Treatment of Lymphedema

Typeobservational_patient_registrySponsorFatih Sultan Mehmet Training and Research HospitalRan2026 to 2026Enrolled25ConditionsLymphedema, Artificial Intelligence (AI)Armsa domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency
3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Spine patient history-taking: a two phase study of a prompt-engineered large language model assistant.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Review
  9. 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

4 authors.

Tim LeypoldDepartment of Plastic Surgery, Hand Surgery-Burn Center, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany.ORCID 0000-0003-1985-8368
Lara F LingensDepartment of Plastic Surgery, Hand Surgery-Burn Center, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany.ORCID 0000-0003-1946-879X
Justus P BeierDepartment of Plastic Surgery, Hand Surgery-Burn Center, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany.ORCID 0000-0002-0179-1962
Anja M BoosDepartment of Plastic Surgery, Hand Surgery-Burn Center, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The role of artificial intelligence (AI) in healthcare is evolving, offering promising avenues for enhancing clinical decision making and patient management. Limited knowledge about lipedema often leads to patients being frequently misdiagnosed with conditions like lymphedema or obesity rather than correctly identifying lipedema. Furthermore, patients with lipedema often present with intricate and extensive medical histories, resulting in significant time consumption during consultations. AI could, therefore, improve the management of these patients. This research investigates the utilization of OpenAI's Generative Pre-Trained Transformer 4 (GPT-4), a sophisticated large language model (LLM), as an assistant in consultations for lipedema patients. Six simulated scenarios were designed to mirror typical patient consultations commonly encountered in a lipedema clinic. GPT-4 was tasked with conducting patient interviews to gather medical histories, presenting its findings, making preliminary diagnoses, and recommending further diagnostic and therapeutic actions. Advanced prompt engineering techniques were employed to refine the efficacy, relevance, and accuracy of GPT-4's responses. A panel of experts in lipedema treatment, using a Likert Scale, evaluated GPT-4's responses across six key criteria. Scoring ranged from 1 (lowest) to 5 (highest), with GPT-4 achieving an average score of 4.24, indicating good reliability and applicability in a clinical setting. This study is one of the initial forays into applying large language models like GPT-4 in specific clinical scenarios, such as lipedema consultations. It demonstrates the potential of AI in supporting clinical practices and emphasizes the continuing importance of human expertise in the medical field, despite ongoing technological advancements.

Indexed as

AIartificial intelligenceChatGPTGPT-4large language modelslipedemaLLMmedical historyplastic surgeryrecommendation

Identifiers

PMID38792666
PMCPMC11122530

What OpenQuestion holds

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Registered trials

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.