[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"clinical-decision-support-systems\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:clinical-decision-support-systems":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,51],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":30,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":50},"100651523","effect-of-agent-assisted-llm-assisted-and-traditional-workflows-on-physician-admission-diagnosis-100651523",false,"NCT07760051","Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission","Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission: A Randomized Controlled Study","Inclusion Criteria\n\n* Hold a Medical Practitioner Qualification Certificate and\u002For Medical License, or be a recognized standardized resident physician; able to independently read electronic medical records, laboratory and imaging reports on an HIS.\n* Currently engaged in clinical work in internal medicine or surgery at one of the 15 participating hospitals.\n* Able to complete the case assessment in one continuous hour without breaks.\n* Able to participate remotely under video proctoring, with a stable internet connection and a working camera.\n* Voluntarily agree to participate and sign the informed consent form, including the declaration not to use unauthorized AI tools during the assessment.\n* Have not participated in case drafting, review, rubric development, or any activity that may leak the reference standard.\n\nExclusion Criteria\n\n* Have previously accessed the official test cases or reference standard of this study.\n* Unable to complete the training module, qualification test, or all experimental tasks.\n* Have conflicts of interest, e.g. participation in developing core algorithms of the tested system.\n* Unwilling to comply with remote proctoring, including keeping the camera on throughout.\n* Judged unsuitable by the investigators.",true,"ALL","18 Years",{"count":20,"type":21},180,"ESTIMATED","INTERVENTIONAL",[24],"NA","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.\n\nThe main questions it aims to answer are:\n\n* Does the Agent-assisted workflow yield better structured admission diagnosis and management planning scores than standalone LLM assistance?\n* 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.\n\nParticipants will:\n\n* Be recruited from 15 hospitals in China and participate remotely under video proctoring\n* Be randomly assigned to one of the three fixed workflows, with randomization stratified by hospital tier, specialty and seniority\n* Complete 6 anonymized simulated HIS admission cases within one hour\n* 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\n* Have their operation logs and time consumption recorded automatically by the study platform",[27,28,29],"Clinical Decision Support Systems","Diagnostic Reasoning","Artificial Intelligence (AI)",[31,32,33,34,35,28,36,37],"AI agent","Large Language Model","Clinical Decision Support","Admission Diagnosis","Diagnostic Accuracy","Randomized Controlled Trial","Management Planning","NOT_YET_RECRUITING","2026-08-15",{"date":41,"type":42},"2026-08-18","ACTUAL",{"date":44,"type":21},"2026-08",{"date":46,"type":21},"2026-12",{"name":48,"class":49},"Second Affiliated Hospital, Zhejiang University, School of Medicine","OTHER",1,{"id":52,"slug":53,"hasResults":11,"nctId":54,"briefTitle":55,"officialTitle":56,"acronym":57,"eligibilityCriteria":58,"healthyVolunteers":16,"sex":59,"minAge":18,"maxAge":4,"enrollmentInfo":60,"targetDuration":4,"studyType":22,"phases":62,"briefSummary":63,"conditions":64,"keywords":79,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":96,"lastUpdatePostDateStruct":97,"startDateStruct":99,"completionDateStruct":101,"leadSponsor":103,"locationsCount":50},"100649333","prostate-cancer-risk-calculator-for-actionable-clinical-decision-making-in-nigeria-100649333","NCT07733440","PROstate Cancer Risk Calculator for ACTionable Clinical Decision-making in Nigeria","Clinical Risk Calculator Validation and Implementation to Optimize Prostate Cancer Early Detection in Nigeria","PROACT","Inclusion Criteria:\n\n1. Training - Eligible primary care providers (for the training surveys) will be healthcare practitioners at community-level hospitals.\n2. Supported Risk Assessment\n\n(i) Eligible key informants will be eligible primary care providers who were trained and implemented the supported risk assessment and the targeted population of patient participants who received the supported risk assessment.\n\n(ii) Eligible patient participants (for the collection of observation data) will be the target population of adult males 40 - 79 years accessing provider services.\n\nExclusion Criteria:\n\n\\-","MALE",{"count":61,"type":21},89,[24],"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.\n\nThe main questions the validation study aims to answer are:\n\n1. How accurately do existing prostate cancer risk calculators identify Nigerian men with clinically significant prostate cancer?\n2. Will a new risk calculator designed for Nigerian men more accurately identify those with clinically significant prostate cancer?\n\nThe main questions the intervention study aims to answer is:\n\nWill the primary care provider-facing risk calculator be feasible and acceptable for primary care providers to implement?\n\nThe intervention trial will be piloted among participants in community-level hospitals in order to primarily assess implementation outcomes",[65,66,27,67,68,69,70,71,72,73,74,75,76,77,78],"Prostate Cancer","Risk Assessment","Predictive Value of Tests","Models","Algorithms","Evidence-Based Practice","Implementation Research","Community Health Services","Primary Health Care","Biomarker","Prostate Specific Antigen","Global Health","Africa","Nigeria",[80,81,82,83,84,85,86,87,88,89,90,91,77,92,93,94,95,78],"Prostate cancer","early detection","prostate cancer screening","early diagnosis","prostate specific antigen","risk calculator","risk prediction model","diagnostic accuracy","validation","prostate biopsy","primary care","implementation science","precision prevention","clinically significant prostate cancer","clinical prediction model","community oncology","2026-07-24",{"date":98,"type":42},"2026-07-29",{"date":100,"type":21},"2029-03",{"date":102,"type":21},"2029-08",{"name":104,"class":49},"Ahmadu Bello University"]