Clinical Scorecard: What Patients Aren’t Telling You: AI in Mental Health Care
At a Glance
| Category | Detail |
|---|---|
| Condition | Mental Health Support |
| Key Mechanisms | Use of generative AI tools for emotional support, psychoeducation, and processing experiences. |
| Target Population | Patients with mental health conditions, particularly youth and young adults. |
| Care Setting | Clinical settings where mental health assessments are conducted. |
Key Highlights
- 13% of US youth have sought mental health advice from AI tools.
- Nearly half of adult patients with mental health conditions use AI for support.
- AI tools may reveal unshared patient concerns and shape experience interpretation.
- Risks include misinformation, inappropriate responses to suicidal ideation, and privacy concerns.
- A patient-centered framework is proposed for integrating AI use into routine care.
Guideline-Based Recommendations
Diagnosis
- Routine assessment of AI tool use should be included in clinical evaluations.
Management
- Normalize the use of AI tools and explore their benefits before addressing concerns.
Monitoring & Follow-up
- Maintain ongoing dialogue about AI tool use and its implications for care.
Risks
- Be aware of potential misinformation and harmful outputs from AI tools.
Patient & Prescribing Data
Youth and adults with mental health conditions.
AI tools are used for emotional support and psychoeducation, often between clinical visits.
Clinical Best Practices
- Elicit patient perspectives on AI tool use.
- Provide information about AI tools with patient permission.
- Integrate AI discussions into routine care rather than treating them as one-time topics.
Related Resources & Content
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
