ArticleSkin health and disease2024
Comparison of large language models in management advice for melanoma: Google's AI BARD, BingAI and ChatGPT.
Article in Skin health and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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Who cites it
15 citing papers in PubMed, 29 citations in OpenAlex.
- Use of generative large language models for patient education on common surgical conditions: a comparative analysis between ChatGPT and Google Gemini.Updates in surgery · 2026Article
- Clinical evaluation of large language model recommendations in melanoma: comparison with multidisciplinary tumor board decisions in a real-world cohort.Frontiers in oncology · 2026Article
- Assessment of patient information guides generated by LLMs for common cardiological procedures.Global cardiology science & practice · 2025Article
- Performance of single-agent and multi-agent language models in Spanish language medical competency exams.BMC medical education · 2025Article
- Microsoft Copilot Provides More Accurate and Reliable Information About Anterior Cruciate Ligament Injury and Repair Than ChatGPT and Google Gemini; However, No Resource Was Overall the Best.Arthroscopy, sports medicine, and rehabilitation · 2025Article
- Comparing answers of ChatGPT and Google Gemini to common questions on benign anal conditions.Techniques in coloproctology · 2025Article
- The Comparative Sufficiency of ChatGPT, Google Bard, and Bing AI in Answering Diagnosis, Treatment, and Prognosis Questions About Common Dermatological Diagnoses.JMIR dermatology · 2025Article
- Article
- There are significant differences among artificial intelligence large language models when answering scientific questions.Frontiers in artificial intelligence · 2025Article
- Exploring the Role of Large Language Models in Melanoma: A Systematic Review.Journal of clinical medicine · 2024Review
- Can AI Answer My Questions? Utilizing Artificial Intelligence in the Perioperative Assessment for Abdominoplasty Patients.Aesthetic plastic surgery · 2024Article
- Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review.Journal of clinical medicine · 2024Review
- A framework for human evaluation of large language models in healthcare derived from literature review.NPJ digital medicine · 2024Article
- Article
- Comparison of large language models in management advice for melanoma: Google's AI BARD, BingAI and ChatGPT.Skin health and disease · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are emerging artificial intelligence (AI) technology refining research and healthcare. Their use in medicine has seen numerous recent applications. One area where LLMs have shown particular promise is in the provision of medical information and guidance to practitioners. This study aims to assess three prominent LLMs-Google's AI BARD, BingAI and ChatGPT-4 in providing management advice for melanoma by comparing their responses to current clinical guidelines and existing literature. Five questions on melanoma pathology were prompted to three LLMs. A panel of three experienced Board-certified plastic surgeons evaluated the responses for reliability using reliability matrix (Flesch Reading Ease Score, the Flesch-Kincaid Grade Level and the Coleman-Liau Index), suitability (modified DISCERN score) and comparing them to existing guidelines.
Identifiers
What OpenQuestion holds
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.