From multivariate methods to an AI ecosystem
Nils R. Winter, Micah Cearns, Scott R. Clark, Ramona Leenings, Udo Dannlowski, Bernhard T. Baune, Tim Hahn
Abstract
A decade ago, at a major international conference, we vividly remember a symposium on psychiatric Artificial Intelligence (AI) drawing a crowd of seven people—including the four speakers. Today one might get the impression that every other funding proposal is required to include at least some degree of AI-based analyses. At a time when 17 of the 29 hot topics listed by the most recent Gartner Hype Cycle for emerging technologies [ 1 ]—an indicator of perceived innovation—are either AI technologies (e.g., Generative Adversarial Networks) or include AI as a core component (e.g., autonomous driving), there is no shortage of promises. In both psychiatry and medicine in general, expectations to move beyond classical group-level statistics and enter the promising future of personalized medicine are high. Although AI has not yet fully hit mainstream psychiatric research, the availability and advancement of technology and methods have indeed led to a growing adoption of AI methods and agreement on best practice [ 2 , 3 , 4 ]. Despite this progress and some promising first applications (e.g., in suicide prediction [ 5 ]), translation to clinical practice has been hampered by a large degree of estimate variability and diagnostic heterogeneity.