arXiv:2606.25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention. As a multidisciplinary topic, these objective measures should be interpretable and accessible to health care professionals, ensuring effective collaboration and treatment planning in the realm of mental health care. Even though current automated depression diagnosis approaches improved over the last decade, a critical gap exists as they often lack affect-specifici
The increasing sophistication of AI models, coupled with a growing public health need for accessible and objective mental health diagnostics, drives the development of explainable AI in healthcare.
This development allows for earlier and more objective diagnosis of depression, potentially improving treatment outcomes and reducing the burden on healthcare systems.
The diagnostic process for mental health conditions can become more data-driven and transparent, providing clinicians with interpretable insights rather than black-box recommendations.
- · Mental healthcare providers
- · Patients with depression
- · AI healthcare technology companies
- · Medical diagnostic companies
- · Traditional diagnostic methods reliant solely on subjective assessment
- · AI models lacking explainability in clinical settings
Wider adoption of AI-driven tools in mental health diagnostics, leading to more standardized and scalable assessments.
Increased trust and acceptance of AI in clinical decision-making due to improved explainability, fostering further integration across medical specialties.
Ethical and regulatory frameworks for AI in mental health become more robust and complex, addressing issues of bias, privacy, and accountability.
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