Recentering responsible and explainable artificial intelligence research on patients

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles

Autonomy · Beneficence · Economic Justice · Political science · Psychiatry · Psychotherapist · Transparency (behavior · Computer Science · Grief, Bereavement, and Mental Health · Maternal Mental Health During Pregnancy and Postpartum · Medicine · Neonatal and fetal brain pathology · Psychology

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• Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot)

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• Maternal psychological stress and distress as predictors of low birth weight, prematurity and intrauterine growth retardation

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• Maternal antenatal anxiety and children's behavioural/emotional problems at 4 years

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• Explainability for artificial intelligence in healthcare

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• AI4People—An Ethical Framework for a Good AI Society

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• Which Framework to Use? A Systematic Review of Ethical Frameworks for the Screening or Evaluation of Health Technology Innovations

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• Pay No Attention to That Man behind the Curtain

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• How Should AI Be Developed, Validated, and Implemented in Patient Care

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• SHIFTing artificial intelligence to be responsible in healthcare

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• From What to How

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