Clinical Report: Restoring Informed Consent for Utilizing Patient Data in Mental Health AI Training
Overview
This report discusses the use of patient data for AI training in mental health, emphasizing the need for explicit opt-in consent and patient-led governance.
Background
The integration of AI in mental health care raises ethical and legal concerns regarding patient consent and data use, often relying on vague terms of service.
Data Highlights
No numerical or trial data was provided in the source material.
Key Findings
['AI models in mental health are trained on therapy transcripts without explicit patient consent.', 'Current consent models often obscure data use.', 'There is a risk of reidentification of patients from deidentified data used in AI training.', 'Historical examples highlight the potential for exploitation in medical innovation.', 'Patient-led governance and explicit opt-in consent are proposed as necessary reforms.']
Clinical Implications
Healthcare professionals should be aware of the limitations of current consent models.
Conclusion
Reforming consent practices in AI training for mental health is discussed as a means to ensure patient autonomy.
