So how can AI bring value to the healthcare industry? While the application of AI in Healthcare continues to grow, here are the top use cases:

- Diagnostics. AI-based image recognition and matching technology has evolved rapidly over the last few years and has created opportunities for specialists (think radiologists) to validate their findings. In parallel, this technology is already starting to put diagnostics in the hands of patients, for instance, to empower them to initially diagnose skin and eye conditions. As an example of where this is being used, as per Google , “2 billion people around the world have skin, hair, and nail conditions” and in response to this need, they recently launched the DermAssist tool which allows you to “find personalized information about your skin concerns after a few questions and three quick photos” and can “identify 288 skin, nail and hair conditions.”

- Drug Discovery and Development. Historically, less than 10% of drug candidates commercialize. The entire process can take about 10-15 years and the costs are normally in the millions (if not billions) of dollars. This is where AI can help reduce lead times and costs by (a) identifying potential drug targets (a molecule in the body, usually a protein, that is intrinsically associated with a particular disease process and that could be addressed by a drug to produce a desired therapeutic effect ) that have a high likelihood of providing positive patient outcomes for a specific disease or therapeutic area and then (b) understanding how patient cohorts will interact and react to that drug target. How do AI models do this? AI models generally parse and search through a vast amount of scientific data sets, look for matches (often by also considering existing drug targets in other therapeutic areas) and analyze/predict their safety and efficacy profiles.

Now, the use of AI doesn’t come with a lack of challenges and risks in the healthcare industry. For starters, the healthcare ecosystem consists of several players – think providers (healthcare systems, doctors, etc.), payers (insurance companies), and then pharma and biotech companies (that often perform drug discovery/development activities and commercialize these products). This means that for AI to deliver value across the entire healthcare value chain, frictionless coordination and integration is necessary, which has often been difficult to date.

Additionally, the healthcare industry is highly regulated, and the use of AI to provide and replace human-based diagnosis, although not impossible to achieve, will continue to be a steep climb and one that will always continue to require human intervention in my opinion - until AI models are 100% error-proof. Lastly, the promise of AI lies in its adoption, and with an increased focus and concern about data privacy and security, organizations deploying AI (especially directly to patients) need to ensure that patients have complete transparency to how their data is used, stored, and controlled.