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Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain–computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.

With the advent of artificial intelligence (AI), the field of creativity faces new opportunities and challenges. This manifesto explores several scenarios of human–machine collaboration on creative tasks and proposes “fundamental laws of generative AI” to reinforce the responsible and ethical use of AI in the creativity field. Four scenarios are proposed and discussed: “Co‐Cre‐AI‐tion,” “Organic,” “Plagiarism 3.0,” and “Shut down,” each illustrating different possible futures based on the collaboration between humans and machines. In addition, we have incorporated an AI‐generated manifesto that also highlights important themes, ranging from accessibility and ethics to cultural sensitivity. The fundamental laws proposed aim to prevent AIs from generating harmful content and competing directly with humans. Creating labels and laws are also highlighted to ensure responsible use of AIs. The positive future of creativity and AI lies in a harmonious collaboration that can benefit everyone, potentially leading to a new level of creative productivity respecting ethical considerations and human values during the creative process.

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