The current state of data in healthcare is chaotic, to put it nicely. A person’s data resides in different databases (Electronic Health Records,) is often outdated, and contains errors. Most people do not have genetic data and the state of the available data often makes it hard to use with AI. For example, much of the historic data used in drug discovery experiments is recorded in lab notebooks and has to be digitized before AI can learn from those experiments. The EHR data that currently sits in closed vaults within medical centers would be of great use to those researchers but getting that data is not easy for them. There are a myriad of political, legal, privacy, security, technical, and other challenges that slow down or prevent easy access to this valuable data for researchers.

Beyond the data issue, there are a number of other barriers. These include ambiguous regulatory guidelines; yet to be determined return on investments for such solutions; lack of clinical trial data that establish the efficacy and safety of using these solutions in the real world environment; concerns regarding privacy and safety; high complexity in choosing and implementing the best AI solutions; designs that leads to poor fit with existing workflows; lack of enough talent that knows how to use AI solutions in healthcare and life sciences; fear amongst medical practitioners about the medical-legal risks, job security, and risk to their income, and more. That’s a lot of barriers. This explains why, in spite of its enormous promise, we have not seen widespread adoption of AI-powered solutions in healthcare. None of these barriers are insurmountable and I fully expect that in the next couple of decades AI will be the technology that will take us to the promised land in health. But given these barriers, it is good to be realistic about the pace of adoption and what needs to be done to make this a reality.

For the healthcare and life sciences executives who are hearing so much about AI everyday and want to get into the game, the good news is that less than perfect will be the name of the game for years to come. There is no perfect use case or technology to start with. How you get started is really a matter of your business needs, your human and capital resources, and your ambition level. AI is not plug and play technology. Each use case has to be stood up to provide benefits to your business. That means a complex implementation process, ongoing monitoring, and maintenance, and plenty of change management. As such, following the crowd to launch a cool radiology algorithm to read your routine X-rays may not end up having the hoped-for impact. However, exploring some of the emerging use cases in using generative AI in simplifying clinical documentation or improving coding can lead to huge benefits with the clinical staff satisfaction.

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