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Learning from chess engines: how reinforcement learning could redefine clinical decision-making in rheumatology
  1. Thomas Hügle
  1. Department of Rheumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  1. Correspondence to Professor Thomas Hügle, Department of Rheumatology, University of Lausanne, Lausanne 1011, Switzerland; thomas.hugle{at}

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It is the year 2035. For many years now, the concept of ‘shared decision making’ has looked nothing like it did in earlier times. Many clinical decisions, such as dose adjustments of methotrexate or certain biologics, are made neither by the rheumatologist nor by the patient, but by computer systems which are more or less autonomous. These consist of digital biomarkers, implanted or skin-integrated sensors and drug delivery systems based on microtechnology and nanotechnology, which have been used for some time in diabetes care. In the meantime, it has been shown that for rheumatoid arthritis and other rheumatological disorders, the disease activity and quality of life can be better controlled with these self-learning systems (formerly called artificial intelligence) than by the rheumatologist alone. Even in the case of non-drug treatments, such as physiotherapy or diet, the patient now receives personalised support through various algorithms. In any desired situation, the options are systematically assessed for their effectiveness and the best ones are suggested. If the treating rheumatologist retires, many years of experience about the individual course of the patient’s disease are not lost, but the model continues to improve. It combines existing and new data, enabling it to treat more accurately with every passing day. Non-individual treatment recommendations for diseases no longer exist and treat-to-target strategies are not reviewed every 3–6 months, but daily to hourly. Of course, rheumatologists still exist. But their role has changed, especially when it comes to treating patients with common diseases and uncomplicated disease courses.

How did this development happen? As is often the case, such knowledge was initially developed outside of medicine. Learning systems initially came from the gaming industry, robotics and autonomous driving. In each of these fields, simulators are available that can be used to generate enormous amounts of data in order to …

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