ArticleScientific reports2024
Response accuracy of ChatGPT 3.5 Copilot and Gemini in interpreting biochemical laboratory data a pilot study.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Quality and Readability Analysis of Artificial Intelligence-Generated Medical Information Related to Prostate Cancer: A Cross-Sectional Study of ChatGPT and DeepSeek.The world journal of men's health · 2026Article
- Evaluating large language model`s performance in answering principles of health course questions.Scientific reports · 2026Article
- Three large language models demonstrate competitive performance in Traditional Chinese Medicine national medical licensing examinations over two years.Scientific reports · 2026Article
- Assessing AI-Based Large Language Models (ChatGPT, Google Gemini, and DeepSeek) for Common Parent Questions about Autism: Acceptability, Readability, and Accuracy.The Psychiatric quarterly · 2025Article
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- The application and challenges of ChatGPT in laboratory medicine.Advances in laboratory medicine · 2025Review
- What artificial intelligence (AI) can tell us about Nasoalveolar Molding (NAM)?BMC oral health · 2025Article
- Clinical applications and limitations of large language models in nephrology: a systematic review.Clinical kidney journal · 2025Article
- Evaluation of Chatbots in the Emergency Management of Avulsion Injuries.Dental traumatology : official publication of International Association for Dental Traumatology · 2025Article
- Potential role of large language models and personalized medicine to innovate cardiac rehabilitation.World journal of clinical cases · 2025Article
- Comparative Analysis of Information Quality in Pediatric Otorhinolaryngology: Clinicians, Residents, and Large Language Models.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- Article
- Comparison of performance of artificial intelligence tools in answering emergency medicine question pool: ChatGPT 4.0, Google Gemini and Microsoft Copilot.Pakistan journal of medical sciences · 2025Article
- Accuracy of LLMs in medical education: evidence from a concordance test with medical teacher.BMC medical education · 2025Article
- The Role of Artificial Intelligence in Gynecologic Oncology Decision-Making: A Feasibility Study.Gynecologic and obstetric investigation · 2025Article
- Codeless Development of a Customized SMILE Nomogram Using a Large Language Model: A Practical Framework for Clinicians.Journal of ophthalmology · 2025Article
- Artificial intelligence in healthcare education: evaluating the accuracy of ChatGPT, Copilot, and Google Gemini in cardiovascular pharmacology.Frontiers in medicine · 2025Article
- Letter to the Editor of the Journal of Medical Systems: Regarding "Responses of Five Different Artificial Intelligence Chatbots to the Top Searched Queries About Erectile Dysfunction: A Comparative Analysis".Journal of medical systems · 2024Article
- Enhancing Patient Education on Cardiovascular Rehabilitation with Large Language Models.Missouri medicineArticle
- Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the release of ChatGPT at the end of 2022, a new era of thinking and technology use has begun. Artificial intelligence models (AIs) like Gemini (Bard), Copilot (Bing), and ChatGPT-3.5 have the potential to impact every aspect of our lives, including laboratory data interpretation. To assess the accuracy of ChatGPT-3.5, Copilot, and Gemini responses in evaluating biochemical data. Ten simulated patients' biochemical laboratory data, including serum urea, creatinine, glucose, cholesterol, triglycerides, low-density lipoprotein (LDL-c), and high-density lipoprotein (HDL-c), in addition to HbA1c, were interpreted by three AIs: Copilot, Gemini, and ChatGPT-3.5, followed by evaluation with three raters. The study was carried out using two approaches. The first encompassed all biochemical data. The second contained only kidney function data. The first approach indicated Copilot to have the highest level of accuracy, followed by Gemini and ChatGPT-3.5. Friedman and Dunn's post-hoc test revealed that Copilot had the highest mean rank; the pairwise comparisons revealed significant differences for Copilot vs. ChatGPT-3.5 (P = 0.002) and Gemini (P = 0.008). The second approach exhibited Copilot to have the highest accuracy of performance. The Friedman test with Dunn's post-hoc analysis showed Copilot to have the highest mean rank. The Wilcoxon Signed-Rank Test demonstrated an indistinguishable response (P = 0.5) of Copilot when all laboratory data were applied vs. the application of only kidney function data. Copilot is more accurate in interpreting biochemical data than Gemini and ChatGPT-3.5. Its consistent responses across different data subsets highlight its reliability in this context.
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