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Fig. 1

Affective computing systems commonly consist of affective modalities, derived features, and machine learning models that predict affect.

Fig. 2

An affective computing-based dashboard that could support human-delivered therapy.

Fig. 3

Example therapy workflows that may benefit from affective computing-based applications.

Fig. 4

Depiction of the shift from Web2 to Web3 infrastructure, which may benefit health applications.

First page of article

Affective computing, the discipline aimed at enabling computers to interpret, express, and modify emotion, is rapidly maturing. Although not aimed exclusively at health care, the technology holds promise to increase our understanding of psychotherapy, train future generations of clinicians, and directly deliver care alongside human therapists. It may also enhance autonomous therapy systems to deliver care in a stepped-care model. This article reviews the principles of, and recent advances in, affective computing for psychotherapy. It outlines emerging and potential applications and pitfalls, concluding with steps toward ethical development and clinical adoption.

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