Implications
We believe the role of machine learning in medicine was best described by Rajkomar et al. in their New England Journal of Medicine article appropriately titled, “Machine Learning in Medicine.” They define machine learning not as a new tool, but a “fundamental technology required to meaningfully process data that exceed the capacity of the human brain to comprehend.”2
As rheumatologists, we use large amounts of clinical data to guide diagnostic and therapeutic decisions, but we still use anecdotal experience or trial and error to pick among between biologic treatments for patients with RA. As technology and understanding of deep molecular testing improve, we find ourselves with more data than ever. Machine learning can use this astronomical amount of data to predict responses to personalized treatment strategies.
In the case of Tao et al., this approach was used to predict treatment response to different TNFi’s. By entering a treatment decision with more certainty of response, the hope is to achieve more disease control up front and lower patients’ exposure to side effects and the costs of ineffective medications. This approach speaks to the idea of individualized medicine, with its goal of patient-specific care, to achieve better long-term outcomes.
Identifying unique signatures, such as these differentially expressed genes and differentially methylated positions, with the best predictive value across larger populations of patients with RA will be important to apply this machine learning model more broadly. Randomized controlled studies comparing traditional TNFi selection vs. this machine learning model should be considered. Other goals for research could include the identification of more unique differentiating signatures among responders and non-responders to medications for other rheumatic diseases.
Chances in the Tournament
Machine learning has unlimited potential. However, its complexities and lack of immediate applicability may make our team more of a dark horse than a front runner. Our most difficult competition may be our first-round competitor, AI: JIA Subtypes, as we take it on in a Battle-Bots style showdown. Other competitors we are wary of facing are Dalmatian Urate and Dog Osteoarthritis. Who doesn’t love man’s best friend?
We implore the Blue Ribbon panel to remember that although some may say that the art and nuance of medicine cannot be captured by a cold, unfeeling machine, AI: TNF Inhibitor Response is more of a collaboration than a Skynet-style takeover. In our base article, the processing capacity and algorithmic learning of our machine colleagues were shown to improve therapeutic response, removing some of the guesswork from our trial-and-error approach to prescribing TNFi therapy.
