Understanding challenges with implementing ML in healthcare and life sciences

We have seen multiple examples of the use of ML in healthcare and life sciences. These include use cases for providers, payers, genomics, drug discovery, and many more. While we have shown how ML can solve some of the biggest challenges that the healthcare industry is facing, implementing it at scale for healthcare and life sciences workloads has some inherent challenges. Let us now review some of those challenges in more detail.

Healthcare and life sciences regulations

Healthcare and life sciences is a highly regulated industry. There are laws that protect a patient’s health information and ensure the security and privacy of healthcare systems. There are some laws that are specific to countries that the patients reside in, and any entity that interacts with data for those patients needs to comply with those rules. Let’s look at some examples of such regulations:

• Health Insurance...