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Nature Computational Science
@natcomputsci.nature.com

In this Focus, we discuss major challenges, such as ethical concerns around AI, data privacy issues, and the development of measurements for ensuring the successful deployment of the models in real-world practice. #mentalhealthresearch #Psychiatry ➡️ nature.com/articles/s43... 🧵(2/8)

nature.comRethinking mental illness through a computational lens - Nature Computational ScienceNature Computational Science presents a Focus that explores the field of computational psychiatry and its key challenges, from privacy concerns to the ethical use of artificial intelligence, offering new insights into the future of mental health care.
Nature Computational Science
@natcomputsci.nature.com

In a Comment, @docqhuys.bsky.social and Michael Browning argue that the field of computational psychiatry will improve patient outcomes through its increasing focus on interventions and trials. #mentalhealthresearch #Psychiatry ➡️ nature.com/articles/s43... 🔓 rdcu.be/eKp1v 🧵(3/8)

nature.comTrials for computational psychiatry - Nature Computational ScienceComputational psychiatry is increasingly delivering causal evidence by focusing on interventions research and clinical trials. Causal evidence could improve patient outcomes through improved precision, repurposing, novel interventions, scaling of psychotherapy and better translation to the clinic.
Nature Computational Science
@natcomputsci.nature.com

In another Comment, Nicole Martinez-Martin discusses that the development of mental health AI tools must address challenges such as bias, contextual effectiveness, and data representativeness. #mentalhealthresearch #ArtificialIntelligence ➡️ nature.com/articles/s43... 🔓 rdcu.be/eKp2q 🧵(4/8)

nature.comDeveloping mental health AI tools that improve care across different groups and contexts - Nature Computational ScienceIn order to realize the potential of mental health AI applications to deliver improved care, a multipronged approach is needed, including representative AI datasets, research practices that reflect and anticipate potential sources of bias, stakeholder engagement, and equitable design practices.
Nature Computational Science
@natcomputsci.nature.com

In a Perspective, Yi-Chieh Lee and colleagues introduce a taxonomy that categorizes LLMs’ roles in psychotherapy along two critical dimensions: autonomy and emotional engagement. #mentalhealthresearch #Psychiatry #ArtificialIntelligence ➡️ nature.com/articles/s43... 🔓 rdcu.be/eKp3M 🧵(5/8)

nature.comComputational and ethical considerations for using large language models in psychotherapy - Nature Computational ScienceLarge language models (LLMs) offer promising ways to enhance psychotherapy through greater accessibility, personalization and engagement. This Perspective introduces a typology that categorizes the roles of LLMs in psychotherapy along two critical dimensions: autonomy and emotional engagement.