AI and the medical expert of tomorrow

In my research I have addressed the consequences of the continuous shift and development of technologies in different work settings and what that means for the skills that we develop. For a number of years, our group have been heading interdisciplinary research initiatives in the medical area. This work encompasses radiologists, dentists, surgeons, radio-physicists and social scientists that jointly study the management of different technological advancements in medicine. We also design workplace-learning environments in which both experienced professionals and novices can develop and improve essential skills. The next step for us, is both to develop AI applications in these areas as well as scrutinising their adoption and their consequences for practice.

If we look at medicine and many other complex work settings, what we find today is an inherent dependence on various technological set-ups. Work proceeds, by necessity, through the incorporation and use of a vast array of technologies. Physical as well as digital. This has as a consequence, that when these tools and technologies change and develop, then the practitioners have to adapt and re-skill.

The general trend here is that tasks of lower complexity can be automated and taken over by technology. The more complex tasks however, have so far tended to require expert involvement. The more recent developments of AI for medicine can be seen as a continuation of a long trend. But it might also represent change on a different order of magnitude. In the near future we will most probably see systems extending and going beyond the current limits of possible performance. This will imply that the medical experts will take on new and even more advanced roles, as supervisors or developers of new forms of knowledge and inquiry.

Now, these are not new arguments. What I want to highlight here is an issue that I miss in the current discussion about how AI transforms work. When we promote the current workforce and let them take on more advanced tasks today, we do so given a pool of people who have undertaken a traditional training and who have become experts under certain conditions. And this is a long process. But these trajectories of becoming experts are themselves being shifted in these transformations. What will it mean to be knowledgeable or an expert radiologist in say 15-20 years from now? Surely something different from today. But how is an individual going to end up so knowledgeable about a professional domain when during training, a system can outperform her every move and diagnosis for years on end? How will we motivate people to keep training and to keep learning so that they will one day be able to contribute to the development of new knowledge?  

While I don’t think that this is an unsolvable problem, we need to start this discussion alongside whatever powerful systems we introduce into medical practice. Otherwise we might be getting a devil’s bargain, where we profit in the short term, whilst depleting the knowledge base in the long run.

Algorithmic Accountability

Side A: A Speculative Vignette

It’s early Monday morning and Andrea is still feeling unpleasantly chilled from the commute to work. The October wind had been tearing at the cable car as they traversed the river. At the busy changeover to the trams, she was caught off guard by a sudden gust of wind and rain which showered her horizontally from top to toe. Her woollen coat is now damp and she can sense the ripe smell of sheep as she takes a seat in the large Hospital lecture hall.

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