Clinical Decision Support Systems

The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system. The main questions it aims to answer are: * Does the Agent-assisted workflow yield better structured admission diagnosis and management planning scores than standalone LLM assistance? * Does the Agent-assisted workflow outperform the traditional workflow without AI tools? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the Agent tool can improve diagnostic accuracy and efficiency. The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system. The main questions it aims to answer are: * Does the Agent-assisted workflow yield better diagnosis and management planning scores than the LLM-assisted workflow? * Does the Agent-assisted workflow yield better diagnosis and management planning scores than the conventional workflow without AI assistance? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the agent tool can improve diagnostic and management planning performance and efficiency. Participants will: * Be recruited from 15 hospitals in China and participate remotely under video proctoring * Be randomly assigned to one of the three fixed workflows, with randomization stratified by hospital tier, specialty and seniority * Work through 6 anonymized simulated HIS admission cases and complete as many as they can. * Submit structured answers for each case covering principal diagnosis, secondary diagnoses, differential diagnoses, diagnostic justification, next diagnostic or therapeutic steps, consultation and referral decisions, and diagnostic confidence. * Have their operation logs and time consumption recorded automatically by the study platform. * Complete a post-session questionnaire after submitting all cases.

Cardiopulmonary exercise testing requires clinicians to integrate multiple physiological measurements and explain their meaning, limitations, safety implications, and appropriate next steps. This randomized two-period crossover study will evaluate whether physician-supervised GPT-assisted drafting preserves the clinical acceptability of final patient-facing responses while reducing task completion time compared with physician-only drafting. Licensed physicians or institutionally recognized clinical trainees will respond in Chinese to 20 synthetic cardiopulmonary exercise testing scenarios. Each participant will complete 10 scenarios in each condition using two non-overlapping case sets. In the GPT-assisted condition, a frozen model-generated draft will be displayed and may be edited, deleted, or completely rewritten by the physician. The primary hypothesis is that GPT-assisted drafting is noninferior to physician-only drafting for clinical acceptability, using a noninferiority margin of 5 percentage points. Completion time will be evaluated for superiority only if noninferiority is established.

This study is a pilot trial that builds on findings from the validation of prostate cancer risk calculators in Nigerian men. The goal of the overall study is to improve the early detection of prostate cancer in a high-risk population. The main questions the validation study aims to answer are: 1. How accurately do existing prostate cancer risk calculators identify Nigerian men with clinically significant prostate cancer? 2. Will a new risk calculator designed for Nigerian men more accurately identify those with clinically significant prostate cancer? The main questions the intervention study aims to answer is: Will the primary care provider-facing risk calculator be feasible and acceptable for primary care providers to implement? The intervention trial will be piloted among participants in community-level hospitals in order to primarily assess implementation outcomes