Not all innovation glitters
The convergence between the life sciences and digital technology has generated for the first time in history the real opportunity for a fruitful contamination between sectors and skills. When a convergence between distant sectors occurs, it is a harbinger of great innovation.
However, it is not enough that there are innovations in the lab to speak of service innovation. Socio-cultural, regulatory, and business implications are key for translating scientific discoveries into market innovations.
While there is no doubt that the topic of Artificial Intelligence (AI) in the health care industry is widely debated and is experiencing a moment of media hype, the applications deriving from AI have different levels of readiness for adoption by the market. In order to understand the effective applicability of AI in Italian health care, it is therefore necessary to look at individual applications and avoid speaking in a general way of machine learning in health care.
To do this, every yea the LIFT Lab of the SDA Bocconi School of Management puts out the "LIFT Radar", a new research tool that offers a reasoned mapping of new AI applications, conceived to help decision makers acquire a clearer and more complete vision of the opportunities emerging out of the frontier of innovation born from the convergence between digital technologies and life sciences. By looking at the LIFT Radar, the reader can see an evaluation of different AI applications, based on precise variables, and gain a clearer understanding of how ready a specific application is to be successfully introduced in the Italian market. By applying this methodology, it appears evident that the domestic market is ready for the adoption of some artificial intelligence applications, while there are other areas for which normative, regulatory and organizational interventions are necessary so that technological innovation can find full-fledged business application.
The predictive models of radiogenomics, for example, which require the integration of data of different nature based on AI algorithms, find the necessary conditions of adoptability in our market. Therefore, it is definitely an area that health care organizations need to invest in today to remain competitive tomorrow. The solution of digital twins (digital aliases on which simulations can be performed), which today represent an area of great interest for venture capital investment in the United States, is limited in Italy by the presence of a few homogeneous and shared databases, as well as a limited diffusion of wearables and other sensors for dynamic data collection. In this case, although the technology is ready, the conditions (i.e. for example the need for dynamic data collection, the existence of homogeneous and shared databases) make the translation of the technology into a business solution not immediate.
The same limitations are noted for the use of AI applied to drug discovery. This solution is becoming increasingly relevant also thanks to the huge capital invested overseas by venture capitalists, the artificial intelligence algorithms are ready for the creation of the necessary models, but there is still a lack of shared databases and of a sufficiently robust regulatory framework, in order to enable the full translation of technological breakthroughs in business innovation. Not all AI is intelligent innovation.