By Jeffrey H. Brochin, J.D.
Collaborative report by the GAO and the National Academy of Medicine examines the implications and key options for optimizing the use of Artificial Intelligence and Machine Learning in health care.
The U.S. Government Accountability Office (GAO) has issued a report that explores the use of Artificial Intelligence (AI) and Machine Learning (ML) in health care, assesses its implications, and identifies key options available for optimizing its use. In the interest of complementing the work of both the GAO and National Academy of Medicine (NAM) in this field, the two bodies cooperated on the development of two publications: NAM’s Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril, and the GAO’s Technology Assessment, Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning in Drug Development (GAO Report, GAO-20-215SP, December 31, 2019).
U.S. Health Care at a crossroads. The U.S. health care system is at an important crossroads as it faces major demographic shifts, burgeoning costs, and transformative technologies. Total annual health care spending in the United States is projected to reach nearly $6 trillion by 2027. Federal spending for health care programs—which accounts for more than a quarter of all health care spending—has grown faster than the overall economy in recent years, a trend that is projected to continue. AI/ML is a set of technologies that includes automated systems able to perform tasks that normally require human intelligence such as visual perception, speech recognition, and decision-making. AI/ML has promising applications in health care including in drug development, and may have the potential 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 health care system also raises ethical, legal, economic, and social questions.
Why GAO conducted the study. The GAO report is the first in a series of technology assessments on the use of AI technologies in health care that the GAO has planned at the request of Congress. The report discusses three topics: (1) current and emerging AI technologies available for drug development and their potential benefits; (2) challenges to the development and adoption of these technologies; and (3) policy options to address challenges to the use of machine learning in drug development.
How the study was conducted. As one component of the review, NAM worked closely with the GAO in organizing a July 2019 meeting of 19 experts to explore the above topics. NAM staff provided expertise to the GAO in identifying experts from federal agencies, academia, biopharmaceutical companies, machine learning-focused companies, and legal scholars. The meeting was intended to enhance the GAO’s understanding of ML in health care and drug development.
What the study found. One of the report’s high-level findings was that machine learning holds tremendous potential in drug development as noted by stakeholders from government, industry, and academia. The current drug development process is lengthy and expensive, and it can take 10 to 15 years to develop a new drug and bring it to market. ML techniques are already used throughout the drug development process and have the potential to expedite the discovery, design, and testing of drug candidates, decreasing the time and cost required. Such improvements could save lives and reduce suffering by getting drugs to patients in need more quickly.
The GAO’s technology assessment demonstrated the breadth of machine learning research and applications with examples from the first three steps of the drug development process—drug discovery, preclinical research, and clinical trials. In drug discovery, researchers are using ML to identify new drug targets, screen known compounds for new therapeutic applications, and design new drug candidates, among other applications. In preclinical research, ML was found to augment preclinical testing of drug candidates and predict toxicity before human testing. Researchers are also beginning to use ML to improve clinical trial design—a point where many drug candidates fail. Those efforts include applying ML to patient selection and recruitment, and identifying patient populations who may react better to certain drugs, thereby advancing towards the promise of precision medicine.
What the GAO recommends. The GAO report described options for policymakers—which the GAO defines broadly to include federal agencies, state and local governments, academic and research institutions, and industry, among others—to use in addressing the current challenges. In addition to the status quo, the GAO identified five policy options centered on research, data access, standardization, human capital, and regulatory certainty.
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