ArticleAnnals of surgical oncology2024
Enhanced Artificial Intelligence Strategies in Renal Oncology: Iterative Optimization and Comparative Analysis of GPT 3.5 Versus 4.0.
Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Performance of large language models in urological decision support: a guideline-based comparative evaluation in urolithiasis.Urolithiasis · 2026Article
- Evaluating large language models in specialized myopia knowledge and clinical reasoning.Frontiers in medicine · 2026Article
- Systematic evaluation of deepseek in urolithiasis: from medical knowledge to clinical decision support.World journal of urology · 2025Article
- High Concordance Between GPT-4o and Multidisciplinary Tumor Board Decisions in Breast Cancer: A Retrospective Decision Support Analysis.Journal of medical systems · 2025Article
- Temporal evolution of large language models (LLMs) in oncology.Journal of translational medicine · 2025Article
- Clinical applications and limitations of large language models in nephrology: a systematic review.Clinical kidney journal · 2025Article
- Assessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Comparative Analysis of Large Language Model and Physician-Generated Responses in Bariatric Patient Inquiries: Assessing the Accuracy and Patient Satisfaction.Obesity surgery · 2025Article
- The Role of Artificial Intelligence (ChatGPT-4o) in Supporting Tumor Board Decisions.Journal of clinical medicine · 2025Article
- Article
- Comment on: "Benchmarking the performance of large language models in uveitis: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, Google Gemini, and Anthropic Claude3".Eye (London, England) · 2025Article
- Assessing the Current Limitations of Large Language Models in Advancing Health Care Education.JMIR formative research · 2025Article
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971-2024).Medical journal of the Islamic Republic of Iran · 2025Article
- Large language model use in clinical oncology.NPJ precision oncology · 2024Article
- The Diagnostic Ability of GPT-3.5 and GPT-4.0 in Surgery: Comparative Analysis.Journal of medical Internet research · 2024Article
- Large Language Models as Decision-support Tools for Adjuvant Therapy Planning in Early-stage Hormone Receptor-positive Breast Cancer.Cancer diagnosis & prognosisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThe rise of artificial intelligence (AI) in medicine has revealed the potential of ChatGPT as a pivotal tool in medical diagnosis and treatment. This study assesses the efficacy of ChatGPT versions 3.5 and 4.0 in addressing renal cell carcinoma (RCC) clinical inquiries. Notably, fine-tuning and iterative optimization of the model corrected ChatGPT's limitations in this area.
methodsIn our study, 80 RCC-related clinical questions from urology experts were posed three times to both ChatGPT 3.5 and ChatGPT 4.0, seeking binary (yes/no) responses. We then statistically analyzed the answers. Finally, we fine-tuned the GPT-3.5 Turbo model using these questions, and assessed its training outcomes.
resultsWe found that the average accuracy rates of answers provided by ChatGPT versions 3.5 and 4.0 were 67.08% and 77.50%, respectively. ChatGPT 4.0 outperformed ChatGPT 3.5, with a higher accuracy rate in responses (p < 0.05). By counting the number of correct responses to the 80 questions, we then found that although ChatGPT 4.0 performed better (p < 0.05), both versions were subject to instability in answering. Finally, by fine-tuning the GPT-3.5 Turbo model, we found that the correct rate of responses to these questions could be stabilized at 93.75%. Iterative optimization of the model can result in 100% response accuracy.
conclusionWe compared ChatGPT versions 3.5 and 4.0 in addressing clinical RCC questions, identifying their limitations. By applying the GPT-3.5 Turbo fine-tuned model iterative training method, we enhanced AI strategies in renal oncology. This approach is set to enhance ChatGPT's database and clinical guidance capabilities, optimizing AI in this field.
Indexed as
Identifiers
38472675What 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.