Natural language processing (NLP) and artificial intelligence (AI) are becoming increasingly popular in the clinical community (Wang et al., 2020; Locke et al., 2021). Particularly, a growing interest surrounds the exploitation of speech and language as digital biomarkers, namely ‘objective, quantifiable behavioral data that can be collected and measured by means of digital devices, allowing for low-cost pathology detection, classification, and monitoring’ (Gagliardi et al., 2021: 1). In a nutshell, this technique consists of ascertaining subtle verbal changes in speech recordings, transcripts, or written texts produced by patients through automatic algorithms. In what follows, we will provide an overview of this emerging research field by sketching its theoretical background, methodological implementation, and possible clinical application.