DeepSeek: Another step forward in the diagnosis of oral lesions

Journal of Dental Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

---

Comparative Evaluation of Large Language Models for Medical Education: Performance Analysis in Urinary System Histology.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Large language models (LLMs) show potential for medical education, but their domain-specific capabilities need systematic evaluation. This study presents a comparative assessment of thirteen LLMs in urinary system histology education. Using multi-dimensional framework, we evaluated across two tasks: answering 65 validated multiple-choice questions (MCQs) and generating clinical scenarios with items. For MCQ performance, assessed accuracy along explanation quality through relevance comprehensiveness metrics. scenario generation, Quality, Complexity, Relevance, Correctness, Variety dimensions. Performance varied substantially tasks, ChatGPT-o1 achieving highest (96.31 ± 17.85%) Claude-3.5 demonstrating superior generation (91.4% maximum possible score). All significantly outperformed random guessing large effect sizes. Statistical analyses revealed significant differences consistency multiple attempts dimensional most showing higher Correctness than Quality scores generation. Term frequency analysis content imbalances all models, overemphasis certain anatomical structures complete omission others. Our findings demonstrate that while considerable promise reliable implementation requires matching specific to appropriate educational implementing verification mechanisms, recognizing current limitations pedagogically balanced content.

Language: Английский

---

Making shiny objects illuminating: the promise and challenges of large language models in U.S. health systems

Published: March 18, 2025

Language: Английский

---

Unveiling the potential of large language models in transforming chronic disease management: A mixed-method systematic review (Preprint)

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e70535 - e70535

Published: March 19, 2025

Chronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking. This review aims synthesize on feasibility, opportunities, and challenges LLMs across management spectrum, from prevention screening, diagnosis, treatment, long-term care. Following PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analysis) guidelines, 11 databases (Cochrane Central Register Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web Science Core China National Knowledge Internet, SinoMed) were searched April 17, 2024. Intervention simulation studies that examined in included. The methodological quality included was evaluated using rating rubric designed simulation-based research risk bias nonrandomized interventions tool quasi-experimental studies. Narrative analysis descriptive figures used study findings. Random-effects meta-analyses conducted assess pooled effect estimates feasibility management. A total 20 general-purpose (n=17) retrieval-augmented generation-enhanced (n=3) diseases, including cancer, cardiovascular metabolic disorders. demonstrated spectrum by generating relevant, comprehensible, accurate recommendations (pooled rate 71%, 95% CI 0.59-0.83; I2=88.32%) having higher accuracy rates compared (odds ratio 2.89, 1.83-4.58; I2=54.45%). facilitated equitable information access; increased patient awareness regarding ailments, preventive measures, treatment options; promoted self-management behaviors lifestyle modification symptom coping. Additionally, facilitate compassionate emotional support, social connections, care resources improve outcomes diseases. However, face addressing privacy, language, cultural issues; undertaking tasks, medication, comorbidity personalized regimens real-time adjustments multiple modalities. have transform at individual, social, levels; their direct application clinical settings still its infancy. multifaceted approach incorporates data security, domain-specific model fine-tuning, multimodal integration, wearables crucial evolution into invaluable adjuncts professionals PROSPERO CRD42024545412; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024545412.

Language: Английский

---

Leveraging Artificial Intelligence for Personalized Rehabilitation Programs for Head and Neck Surgery Patients

Technologies, Journal Year: 2025, Volume and Issue: 13(4), P. 142 - 142

Published: April 4, 2025

Background: Artificial intelligence (AI) and large language models (LLMs) are increasingly used in healthcare, with applications clinical decision-making workflow optimization. In head neck surgery, postoperative rehabilitation is a complex, multidisciplinary process requiring personalized care. This study evaluates the feasibility of using LLMs to generate tailored programs for patients undergoing major surgical procedures. Methods: Ten hypothetical scenarios were developed, representing oncologic resections complex reconstructions. Four LLMs, ChatGPT-4o, DeepSeek V3, Gemini 2, Copilot, prompted identical queries plans. Three senior clinicians independently assessed their quality, accuracy, relevance five-point Likert scale. Readability quality metrics, including DISCERN score, Flesch Reading Ease, Flesch–Kincaid Grade Level, Coleman–Liau Index, applied. Results: ChatGPT-4o achieved highest (Likert mean 4.90 ± 0.32), followed by V3 (4.00 0.82) 2 (3.90 0.74), while Copilot underperformed (2.70 0.82). produced most readable content. A statistical analysis confirmed significant differences across (p < 0.001). Conclusions: can varying readability. clinically relevant plans, generated more AI-generated plans may complement existing protocols, but further validation necessary assess impact on patient outcomes.

Language: Английский

---

Integrating Generative AI with Neurophysiological Methods in Psychiatric Practice

Asian Journal of Psychiatry, Journal Year: 2025, Volume and Issue: unknown, P. 104499 - 104499

Published: April 1, 2025

Language: Английский

---