[Abstract]As the volume of medical research using large language models (LLMs) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published “TRIPOD-LLM reporting guideline for studies using large language models”. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLMs. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLMs into healthcare.
The umbrella trial has received increasing attention in the design of clinical trials for oncology drugs in recent years. This trial design categorizes a single disease into multiple sub-types based on predictive biomarkers or other predictive factors, and simultaneously evaluates the efficacy of multiple targeted therapies. When compared with the traditional drug development model of phase Ⅰ, phaseⅡ, and phase Ⅲ randomized controlled trials, umbrella trials are a more scientifically rigorous trial design that can speed up drug evaluation to address the conflict between numerous untested drugs and diseases with a lack of effective treatment options. This article will focus on the concept, main characteristics, eligibility criteria, design and statistical considerations, ethical considerations, and future directions of umbrella trials, with the aim of providing methodological guidance for the design of clinical trials for oncology drugs.
This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.