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Journal of Science Policy & Governance | Volume 18, Issue 01 | March 24, 2021

Policy Memo: Harnessing Artificial Intelligence and Machine Learning in Biomedical Applications with the Appropriate Regulation of Data

Nicole Bonan (1)*, Jaclyn Brennan (2), Anthony Hennig (3), Mark Alexander Kaltenborn (4,5)

  1. ​The George Washington University Institute for Biomedical Sciences, Washington, DC
  2. The George Washington University Department of Biomedical Engineering, Washington, DC
  3. The George Washington University Department of Engineering Management and Systems Engineering, Washington, DC
  4. The George Washington University Department of Physics, Washington, DC
  5. Los Alamos National Laboratory Computational Physics and Methods Group, Los Alamos, New Mexico​
*Corresponding author: [email protected]
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https://doi.org/10.38126/JSPG180102
Keywords: artificial intelligence; machine learning; software as a medical device; FDA; FDCA; database​
Executive Summary: Medical devices and systems are increasingly relying on software using artificial intelligence (AI) and machine learning (ML) algorithms to increase efficiency, provide better diagnoses, and increase the quality of care for patients. AI- and ML-based devices and systems have an advantage over traditional medical device systems because they are designed to learn and improve using large databases of actual or simulated patient data. However, the use of these datasets could introduce harmful biases to certain populations, restrict economic development if policy were to change in the future, and negatively impact healthcare. We recommend amending the Food Drug and Cosmetic Act to explicitly direct the Secretary of Health and Human Services to regulate databases used by AI systems and require that the premarket review of medical databases includes assessments of potential bias and security.

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Nicole Bonan is a 3rd year PhD student studying cancer biology at the George Washington University. Her dissertation research focuses on genetically modifying NK cells and administering tumor stroma-degrading enzymes to increase immune cell penetration and killing of solid tumors. She is interested in adoptive cell therapy, nanomedicine, and science policy. She received her MS in biology and BS in biochemistry from American University.

Jaclyn Brennan is a postdoctoral researcher in the Biomedical Engineering Department at GWU. She received her PhD from GWU in 2020, with a research focus in cardiac electrophysiology and arrhythmias of the cardiac conduction system. She hopes to eventually apply her technical knowledge and problem-solving skills toward a career in science policy. 

Anthony Hennig is a 5th year PhD candidate studying engineering complexity as part of the systems engineering program in the Department of Engineering Management and Systems Engineering. They graduated with a Master of Science in Science, Technology, and Public Policy from the Rochester Institute of Technology in 2016 and they are interested in aerospace technology policy as well as program and project management with respect to public policy. 

Mark Alexander Kaltenborn is a 5th year PhD student in the Department of Physics at the George Washington University (GWU) and a graduate student researcher in the Computational Physics and Methods Group at Los Alamos National Laboratory. He conducts numerical studies of white dwarf and neutron star mergers with particle-based hydrodynamics codes and develops the Advanced Simulation and Computing Program Ristra Project's FleCSPH code. Alexander has completed the International Science and Technology Policy graduate certificate program at GWU and continues to pursue his interest in science policy.
Acknowledgements
The authors would like to acknowledge the organizers of the National Science Policy Network’s Science Policy Memo workshop for providing an environment in which the authors could receive feedback on and refine this memorandum. 
 
Disclaimer
The authors declare no conflicts of interest.
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