“The U.S. healthcare system is at an important crossroads as it faces major demographic shifts, burgeoning costs, and transformative technologies,” says a new report from the Government Accountability Office (GAO) and the National Academy of Medicine.
The report touts the use of artificial intelligence and machine learning (AI/ML) “to help identify new treatments, reduce failure rates in clinical trials, and generally result in a more efficient and effective drug development process.” However, applying AI/ML technologies within the healthcare system also raises ethical, legal, economic, and social questions, the report is quick to add.
For example, as AI applications grow in their ability to lend perspective to health and healthcare decision-making, there is a “compelling need for transparency in algorithms and data sources with the recognition that the need for algorithmic transparency is context-dependent, based on risk and intended use,” the report says. A high impact AI tool with immediate clinical implications warrants more stringent explanation requirements than a tool with a proven record of accuracy that is low risk and clearly conveys its recommendations to the end-user.
The report also calls for a revitalized approach to patient and practitioner education.
Intersection of AI, Patient-Centricity, and Ethical Considerations
Wondering how to harness the potential of artificial intelligence in clinical research while continuing to protect patient data and rights? Join Emory University’s Karen Lindsley at ACRP 2020 this May to learn about the progression of AI into healthcare and clinical research and associate challenges. This session is part of the new Technology and Future Trends Track at ACRP 2020 (learn more).
“Given the necessary reliance on information technology (IT) and ML to help health professionals keep pace with the rapidly growing knowledge base, medical education will need a substantial overhaul,” says the report. This needs to happen with an added focus on the use of AI as a routine decision-assistance tool. Training programs across multiple professions will require a focus on data science and the appropriate use of AI products and services, the report adds.
Researchers are beginning to use machine learning to improve clinical trial design, a point in the process where many potential drug candidates fail, according to the report. However, the “use of AI in clinical trials tends to be less mature than earlier steps in the process because privacy regulations limit the access to and use of patient data” the report says, citing industry reports.
Author: Michael Causey