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# PhRMA, Others Boost FDA Discussion Paper on AI
- URL: https://www.fdaweb.com/phrma-others-boost-fda-discussion-paper-on-ai/
- Published: 2023-08-10T12:00:00.000Z
- Updated: 2026-09-14T18:49:32.000Z
- Author: David McFarland
- Tags: Drugs, #legacy-id-D5155078

Pharmaceutical Research and Manufacturers of America (PhRMA) says it “strongly agrees with the potential benefits of artificial intelligence (AI) and machine learning (MI) in drug development, including the potential to bring safe and effective drugs to patients faster; provide broader access to drugs and thereby improve health equity; increase the quality of manufacturing; enhance drug safety; and develop novel drugs and drug classes, as well as personalized treatment approaches.” [Commenting](https://www.regulations.gov/comment/FDA-2023-N-0743-0065?ref=fdaweb.com) on an FDA discussion paper on AI/ML in drug development, PhRMA provides general considerations on:

- the scope of FDA’s regulatory authority;
- standards in the use of AI/ML;
- risk-based approach;
- international harmonization;
- flexibility in guidance; and
- training and education.

The Duke University Margolis Center for Health Policy [comments](https://www.regulations.gov/comment/FDA-2023-N-0743-0062?ref=fdaweb.com) on human-led governance, accountability, and transparency; quality, reliability, and representativeness of data; and model development, performance, monitoring, and validation.

Flatiron Health [says](https://www.regulations.gov/comment/FDA-2023-N-0743-0064?ref=fdaweb.com) it welcomes the agency discussion paper and recognizes the power of AI/ML to enhance the development of drugs and biologics through the creation of enormous efficiencies powered, in part, by leveraging rapid technological innovations in data collection and evidence generation. It answers questions FDA posed in the discussion paper.

Finally, the Critical Path Institute [says](https://www.regulations.gov/comment/FDA-2023-N-0743-0067?ref=fdaweb.com) the agency’s recommendations about accountability, quality, reliability, and performance, and Verification, Validation, and Uncertainty Quantification “indicate that there is perhaps a need for a neutral third party to provide certification of AI/ML models and algorithms. As a general recommendation with regards to the usage of AI/ML models in drug development, it is suggested to include a mention of how public/private partnerships can help provide neutral frameworks to verify algorithms for quality, reliability, etc.”