It’s a nice coincidence that my article on machine learning for clinicians has been published at around the same time that my poster on a similar topic was presented at WCPT. I’m quite happy with this paper and think it offers a useful overview of the topic of machine learning that is specific to clinical practice and which will help clinicians understand what is at times a confusing topic. The mainstream media (and, to be honest, many academics) conflate a wide variety of terms when they talk about artificial intelligence, and this paper goes some way towards providing some background information for anyone interested in how this will affect clinical work. You can download the preprint here.
The technology at the heart of the most innovative progress in health care artificial intelligence (AI) is in a sub-domain called machine learning (ML), which describes the use of software algorithms to identify patterns in very large data sets. ML has driven much of the progress of health care AI over the past five years, demonstrating impressive results in clinical decision support, patient monitoring and coaching, surgical assistance, patient care, and systems management. Clinicians in the near future will find themselves working with information networks on a scale well beyond the capacity of human beings to grasp, thereby necessitating the use of intelligent machines to analyze and interpret the complex interactions between data, patients, and clinical decision-makers. However, as this technology becomes more powerful it also becomes less transparent, and algorithmic decisions are therefore increasingly opaque. This is problematic because computers will increasingly be asked for answers to clinical questions that have no single right answer, are open-ended, subjective, and value-laden. As ML continues to make important contributions in a variety of clinical domains, clinicians will need to have a deeper understanding of the design, implementation, and evaluation of ML to ensure that current health care is not overly influenced by the agenda of technology entrepreneurs and venture capitalists. The aim of this article is to provide a non-technical introduction to the concept of ML in the context of health care, the challenges that arise, and the resulting implications for clinicians.