Recent evolution in the field of data science has revealed the potential utility of machine learning (ML) applied to criminal justice. Hence, the literature focused on finding better techniques to predict criminal recidivism risk is rapidly flourishing. However, it is difficult to make a state of the art for the application of ML in recidivism prediction. In this systematic review, out of 79 studies from Scopus and PubMed online databases we selected, 12 studies that guarantee the replicability of the models across different datasets and their applicability to recidivism prediction. The different datasets and ML techniques used in each of the 12 studies have been compared using the two selected metrics. This study shows how each method applied achieves good performance, with an average score of 0.81 for ACC and 0.74 for AUC. This systematic review highlights key points that could allow criminal justice professionals to routinely exploit predictions of recidivism risk based on ML techniques. These include the presence of performance metrics, the use of transparent algorithms or explainable artificial intelligence (XAI) techniques, as well as the high quality of input data.

EEG microstates are brief, stable topographical configurations of brain activity that provide insights into alterations in brain function and connectivity. Anomalies in microstates are associated with different neuropsychiatric conditions, especially schizophrenia. Recent advances in both EEG techniques and machine learning point to the potential role of microstates as diagnostic markers for psychotic disorders. This systematic review aims to gather current knowledge on machine learning applied to EEG microstate analysis in psychotic disorders. Following PRISMA guidelines, we searched Scopus, PubMed, and Scholar databases, including 10 studies. Overall results show that EEG microstates can be used to accurately classify diagnoses within the psychosis spectrum, across all stages, outperforming models based on conventional EEG measures, with a prominent role of microstate D. One study also suggests that microstate anomalies may be directly linked to symptom severity. Integrating EEG microstates with machine learning shows promise in improving our understanding of psychotic disorders and developing more precise diagnostic tools.

Psychoeducation is a key intervention in mood disorders. With the rise of artificial intelligence (AI) conversational agents, tools like ChatGPT are increasingly consulted by patients. Yet, empirical data on how AI-generated psychoeducational content is perceived by patients and professionals remain limited. In this cross-sectional study, 30 depressed inpatients submitted five open-ended questions to ChatGPT-4o. Responses were rated by patients using 5-point Likert scales for relevance, comprehensibility, usefulness, empathy, and acceptance. Independent safety checks were applied to all outputs. The same responses were later blindly evaluated, in randomized order, by three psychiatrists and three psychiatric rehabilitation technicians (PRTs). All outputs passed safety review. Patients assigned higher total scores (mean ± SD = 22.43 ± 2.64) than PRTs (17.63 ± 3.39) and psychiatrists (15.42 ± 2.02). The largest gaps involved empathy and acceptance, whereas relevance, usefulness, and comprehensibility differed less. PRT ratings were intermediate: closer to patients on relevance, comprehensibility, and usefulness, but closer to psychiatrists on empathy and acceptance. Within patients, no associations emerged with age, education, depression severity, or prior psychoeducation. Patients with mood disorders perceived ChatGPT-generated responses as more relevant, comprehensible, useful, accepting, and empathetic than health professionals did. With conversational agents entering psychoeducation, clinicians must develop strategies to critically integrate such tools, ensuring safety and quality while guiding patient use. The challenge is not resisting AI adoption, but framing it within safe, effective, and ethically sound psychoeducational care.