Large language models ( #LLMs ) have emerged as transformative healthcare tools for clinical documentation, diagnostic reasoning, and medical education. However, effective utilization requires understanding prompt engineering principles—the strategic design of inputs to optimize performance while mitigating hallucination, bias, and outdated information.MethodsThis narrative review synthesizes evidence from a structured PubMed search through December 2025 using… Continue reading Effective prompt design for large language models in clinical #practice
Tag: artificial-intelligence
Comparison of verbal autopsy using a large #language model to biologically confirmed causes of death for #malaria and other communicable diseases among children in six sub-Saharan African countries
Malaria, a preventable parasitic disease, causes most child deaths in sub-Saharan Africa (SSA). Reliable cause-of-death data are essential to evaluate progress toward the national and global malaria control goals. However, civil registration and vital statistics are often weak and incomplete in many low- and middle-income countries. In such circumstances, verbal autopsy (VA) provides an alternative… Continue reading Comparison of verbal autopsy using a large #language model to biologically confirmed causes of death for #malaria and other communicable diseases among children in six sub-Saharan African countries
AI in Hand #Surgery: Assessing Large Language Models in the Classification and Management of #Hand Injuries
AbstractBackground: OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in… Continue reading AI in Hand #Surgery: Assessing Large Language Models in the Classification and Management of #Hand Injuries
The Future of #Artificial #Intelligence in Medical Education and Continuing #Medical Education
In this article, we explore the transformative potential of artificial intelligence (AI) in medical education and continuing medical education. We discuss the rapid evolution of AI technology, particularly generative AI and large language models, and their implications for teaching and learning. We emphasize the importance of AI literacy, ethical considerations, and evidence-based approaches to integrating… Continue reading The Future of #Artificial #Intelligence in Medical Education and Continuing #Medical Education
#Artificial #Intelligence and #Machine #Learning Applications in #Liver Disease
AbstractArtificial intelligence and machine learning are transforming hepatology by integrating clinical, laboratory, imaging, and wearable data for earlier diagnosis, risk prediction, and patient management. These technologies enable personalized care and noninvasive monitoring across metabolic dysfunction-associated steatotic liver disease, cirrhosis, hepatitis C, liver transplantation, and hepatocellular carcinoma. Ongoing advances in digital health and interpretability will enhance… Continue reading #Artificial #Intelligence and #Machine #Learning Applications in #Liver Disease
#Artificial #intelligence in #myeloid malignancies: Clinical applications of machine learning in myelodysplastic syndromes and acute myeloid #Leukemia
This review summarizes applications of machine learning (ML) in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), spanning diagnosis, prognostication, treatment prediction, and research tools. In diagnostics, deep learning applied to bone marrow smears, peripheral blood films, and flow cytometry has shown high sensitivity and specificity, outperforming conventional methods. ML-driven unsupervised clustering and consensus classification… Continue reading #Artificial #intelligence in #myeloid malignancies: Clinical applications of machine learning in myelodysplastic syndromes and acute myeloid #Leukemia
Beyond human ears: navigating the uncharted risks of #AI scribes in clinical #practice
Artificial intelligence (AI) scribes have been rapidly adopted across health systems, driven by their promise to ease the documentation burden and reduce clinician burnout. While early evidence shows efficiency gains, this commentary cautions that adoption is outpacing validation and oversight. Without greater scrutiny, the rush to deploy AI scribes may compromise patient safety, clinical integrity,… Continue reading Beyond human ears: navigating the uncharted risks of #AI scribes in clinical #practice
The Illusion of #Readiness: Stress Testing Large Frontier Models on Multimodal #Medical Benchmarks
Large frontier models like GPT-5 now achieve top scores on medical benchmarks. But our stress tests tell a different story. Leading systems often guess correctly even when key inputs like images are removed, flip answers under trivial prompt changes, and fabricate convincing yet flawed reasoning. These aren't glitches; they expose how today's benchmarks reward test-taking… Continue reading The Illusion of #Readiness: Stress Testing Large Frontier Models on Multimodal #Medical Benchmarks
#AI -guided patient stratification improves outcomes and efficiency in the AMARANTH #Alzheimer’s Disease clinical trial
Alzheimer’s Disease (AD) drug discovery has been hampered by patient heterogeneity, and the lack of sensitive tools for precise stratification. Here, we demonstrate that our robust and interpretable AI-guided tool (predictive prognostic model, PPM) enhances precision in patient stratification, improving outcomes and decreasing sample size for a AD clinical trial. The AMARANTH trial of lanabecestat,… Continue reading #AI -guided patient stratification improves outcomes and efficiency in the AMARANTH #Alzheimer’s Disease clinical trial
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