首页|Duke-National University of Singapore Medical School Reports Findings in Artific ial Intelligence (Assessing the Utility, Impact, and Adoption Challenges of an A rtificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes .. .)
Duke-National University of Singapore Medical School Reports Findings in Artific ial Intelligence (Assessing the Utility, Impact, and Adoption Challenges of an A rtificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes .. .)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Singapore, Sing apore, by NewsRx editors, research stated, "The clinical management of type 2 di abetes mellitus (T2DM) presents a significant challenge due to the constantly ev olving clinical practice guidelines and growing array of drug classes available. Evidence suggests that artificial intelligence (AI)-enabled clinical decision s upport systems (CDSSs) have proven to be effective in assisting clinicians with informed decision-making." Our news journalists obtained a quote from the research from the Duke-National U niversity of Singapore Medical School, "Despite the merits of AI-driven CDSSs, a significant research gap exists concerning the early-stage implementation and a doption of AI-enabled CDSSs in T2DM management. This study aimed to explore the perspectives of clinicians on the use and impact of the AI-enabled Prescription Advisory (APA) tool, developed using a multi-institution diabetes registry and i mplemented in specialist endocrinology clinics, and the challenges to its adopti on and application. We conducted focus group discussions using a semistructured interview guide with purposively selected endocrinologists from a tertiary hospi tal. The focus group discussions were audio-recorded and transcribed verbatim. D ata were thematically analyzed. A total of 13 clinicians participated in 4 focus group discussions. Our findings suggest that the APA tool offered several usefu l features to assist clinicians in effectively managing T2DM. Specifically, clin icians viewed the AI-generated medication alterations as a good knowledge resour ce in supporting the clinician's decision-making on drug modifications at the po int of care, particularly for patients with comorbidities. The complication risk prediction was seen as positively impacting patient care by facilitating early doctorpatient communication and initiating prompt clinical responses. However, the interpretability of the risk scores, concerns about overreliance and automat ion bias, and issues surrounding accountability and liability hindered the adopt ion of the APA tool in clinical practice."
SingaporeSingaporeAsiaArtificial I ntelligenceDiabetes Mellitus ManagementEmerging TechnologiesHealth and Med icineMachine LearningNon-Insulin Dependent Diabetes MellitusNutritional an d Metabolic Diseases and ConditionsType 2 Diabetes
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