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Artificial Intelligence and Machine Learning in Software as a Medical Device

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day.

The FDA is considering a total product lifecycle-based regulatory framework for these technologies that would allow for modifications to be made from real-world learning and adaptation, while still ensuring that the safety and effectiveness of the software as a medical device is maintained.

real-world examples of artificial intelligence and machine learning technologies include: Adaptive artificial intelligence and machine learning technologies differ from other software as a medical device (SaMD) in that they have the potential to adapt and optimize device performance in real-time to continuously improve health care for patients.

The ideas described in the discussion paper leverage practices from our current premarket programs and rely on IMDRF’s risk categorization principles, the FDA’s benefit-risk framework, risk management principles described in the software modifications guidance, and the organization-based total product lifecycle approach (also envisioned in the Digital Health Software Precertification (Pre-Cert) Program).

This plan would include the types of anticipated modifications—referred to as the “Software as a Medical Device Pre-Specifications”—and the associated methodology being used to implement those changes in a controlled manner that manages risks to patients —referred to as the “Algorithm Change Protocol.” In this approach, the FDA would expect a commitment from manufacturers on transparency and real-world performance monitoring for artificial intelligence and machine learning-based software as a medical device, as well as periodic updates to the FDA on what changes were implemented as part of the approved pre-specifications and the algorithm change protocol.

34 Pharma Companies Using Artificial Intelligence in Drug Discovery

If you read my list of startups using artificial intelligence to drug discovery, you may have wondered: how much traction do these companies actually have?

Here are relevant partnerships, memberships, and investments I'm aware of for Astellas: Here are relevant partnerships, memberships, and investments I'm aware of for AstraZeneca: Here are relevant partnerships, memberships, and investments I'm aware of for BASF: Here are relevant partnerships, memberships, and investments I'm aware of for Bayer: Here are relevant partnerships, memberships, and investments I'm aware of for Boehringer Ingelheim: Here are relevant partnerships, memberships, and investments I'm aware of for Bristol-Myers Squibb (BMS): Bristol-Myers Squibb (BMS) acquired Celgene, so look there for future information.

Here are relevant partnerships, memberships, and investments I'm aware of for GSK: In addition to what I've outlined below, in September 2019, Genentech and parent Roche disclosed a predictive analytics project with a paper in Nature on using deep learning to predict which patients with diabetic retinopathy will progress the fastest.

Here are relevant partnerships, memberships, and investments I'm aware of for Genentech: Here are relevant partnerships, memberships, and investments I'm aware of for Gilead: Here are relevant partnerships, memberships, and investments I'm aware of for Ipsen: Here are relevant partnerships, memberships, and investments I'm aware of for Janssen: Here are relevant partnerships, memberships, and investments I'm aware of for Merck Group: Here are relevant partnerships, memberships, and investments I'm aware of for Mitsubishi Tanabe Pharma: While not a traditional pharmaceutical company, Nestlé has a health science division.

Here are relevant partnerships, memberships, and investments I'm aware of for Roche: Here are relevant partnerships, memberships, and investments I'm aware of for SK Biopharmaceuticals: Here are relevant partnerships, memberships, and investments I'm aware of for Sanofi: Here are relevant partnerships, memberships, and investments I'm aware of for Santen: Here are relevant partnerships, memberships, and investments I'm aware of for Servier: Here are relevant partnerships, memberships, and investments I'm aware of for Sumitomo Dainippon Pharma: Here are relevant partnerships, memberships, and investments I'm aware of for Sunovion: Here are relevant partnerships, memberships, and investments I'm aware of for Takeda: Here are relevant partnerships, memberships, and investments I'm aware of for Wave Life Sciences: Yuhan is a South Korean pharmaceutical and chemical company.

Predicting what molecules to make next and how to make them

Our scientists are using AI to help redefine medical science in the quest for new and better ways to discover, test and accelerate the potential medicines of tomorrow.

The following sections tell just some of the stories behind how data science and AI are starting to make a difference to our R&D efforts.

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