AI in Healthcare

From a policy perspective smart and effective regulations and responsive authorities are important enablers of AI in healthcare. Regulators are faced with the difficult task of providing frameworks that both ensure the safety of patients and are conducive to impactful innovation in a very fast moving technology landscape. Historically, the US Food and Drug Administration (FDA) has not exactly been known as an agile authority, but at this time, speaking to entrepreneurs from both sides of the Atlantic we often hear the FDA highlighted as well ahead in of its European counter parts[6].

In the US in 2023, 221 AI-enabled tools were cleared by the FDA, up from 155 in 2022[7]. While most of these algorithms and devices are within radiology, the use cases for AI in healthcare are many and varied. Hundreds of tools are already widely deployed, many are currently under development, and yet others are still pies in the sky.

Developing healthcare technology is expensive. In terms of investments, both private investors as well as hospitals are pouring money and resources into AI ventures and innovation efforts[8]. While Venture Capital (VC) investments generally dropped in both 2022 and 2023, also within life science and health, the proportion of AI focused investments across med, health, and biotech, increased consistently from 2015 – 2024 and more than doubled over those years[9]. However, when it comes to VC investment in AI, Europe, including Denmark, dwarfs in comparison with the US[10]. 

In the US, hospitals with their often large research budgets and more corporate structures (when compared to Danish counterparts), have the ability and incentives to invest in commercialization of their research and inventions, and are key drivers of AI based innovation. Take as an example Mass General Hospital in the Boston area which has at its disposal an annual research budget north of 2.3 billion USD and its own successful VC fund that can finance internal start-ups as well as a dedicated venture fund, the Mass General Brigham AI and Digital Innovation Fund, for supporting AI and digital health startups specifically[11].  Later in the value chain, one of the most transformational roles hospitals can play in helping innovators succeed is being a good reference customer. This is a key lesson for Danish hospitals, where Danish entrepreneurs sometimes find that they have an easier time getting their products sold abroad than at home[12]. Hospitals like Mass General Brigham play a key role all the way through the innovation life cycle: as drivers of academic research, as incubators, testing grounds, investors, and first customers.

Successful development of AI-based solutions in healthcare requires several key elements: funding, hospitals capable of driving and implementing innovation, world class research and expertise in computer science and healthcare, and, equally important, entrepreneurial and academic talent. Not surprisingly, in the US we see leadership in healthcare AI emerging in the eco-systems in California and the Boston area where this confluence of factors can be found. A prime example from Boston is the research and innovation produced by the Broad Institute, that draws on both the engineering capabilities of MIT, the biomedical research from Harvard, and the clinical excellence of leading research hospitals in the greater Boston area[13]. At institutes like the Broad and across the MIT community there is a strong tradition of entrepreneurship (encouraged and supported by accelerators, world class mentorship-programmes, and affiliated VCs[14]). When AI and healthcare researchers and start-ups are looking to scale the impact of their innovation they do not have to look far for corporate partners. Within walking distance from MITs main campus you can find not only labs from major pharmaceutical and med-tech companies but also research hubs from major tech and AI companies like Google, Microsoft, and Amazon that are all increasingly interested in applying their computational capabilities in healthcare.