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Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning in Drug Development

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Report Type Reports and Testimonies
Report Date Dec. 20, 2019
Release Date Jan. 21, 2020
Report No. GAO-20-215SP
Summary:

Drug companies spend 10 to 15 years bringing a drug to market, often at a high cost. Machine learning could reduce the time and cost by finding new insights in large biomedical or health-related data sets.

Machine learning is already used throughout drug development, from discovery to clinical trials. Experts say further advances could be transformative. But a lack of high-quality data hinders its use, as do research gaps, obstacles to data access and sharing, human capital challenges, and regulatory uncertainty.

We present 6 policy options to address these challenges and discuss potential opportunities and implementation issues of each.

Illustration showing patients, lab research, literature, compounds, a laptop, and pills are components of machine learning in drug development

Illustration showing patients, lab research, literature, compounds, a laptop, and pills are components of machine learning in drug development

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