Artificial intelligence for family medicine research in Canada: current state and future directions: Report of the CFPC AI Working Group.

OBJECTIVE: To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field. COMPOSITION OF THE COMMITTEE: Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders. METHODS: This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future. REPORT: The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm. CONCLUSION: Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.

Assisting the Non-invasive Diagnosis of Liver Fibrosis Stages using Machine Learning Methods.

Fibrosis is a significant indication of chronic liver diseases often due to hepatitis C Virus. It is becoming a global concern as a result of the rapid increase in the number of HCV infected patients, the high cost and flaws associated with the assessment process of liver fibrosis. This study aims to determine the features that significantly contribute to the identification of the stages of liver fibrosis and to generate rules to assist physicians during the treatment of the patients as a clinically non-invasive approach. Also, the performance of different Multi-layered Perceptron (MLP), Random Forest, and Logistic Regression classifiers are estimated and compared for the full and reduced feature sets. Decision Tree produced 28 rules in contrast with previous research work where 98002 rules had been generated from the same dataset with an accuracy rate of approximately 99.97%. The resulting rules of this study achieved a prediction accuracy for the histological staging of liver fibrosis of 97.45%. Among all the machine learning methods, MLP achieved the highest accuracy rate.