When search answers sound certain but aren’t: Mind launches a global inquiry into AI and mental health

Why AI and mental health require sector-specific AI safety standards

• Executive summary for leaders
• Investigative reporting found that Google’s AI Overviews (short, AI-generated search summaries) can give inaccurate — sometimes dangerous — medical and mental-health advice. Investigative reporting prompted Mind to launch a year-long international commission on AI and mental health.
• The commission will bring clinicians, people with lived experience, health providers, policymakers and tech firms together to recommend safeguards, standards and possible regulation.
• For businesses using generative AI in customer- or patient-facing roles, mental-health AI is high risk: require transparent sourcing, human escalation, and tested safeguards before deployment.

Imagine someone typing “I think I’m having a panic attack” into search and getting a short, confident summary that downplays the risk or suggests the wrong next step. Two billion people a month rely on search for urgent health information, and many of those answers now come from generative AI.

AI Overviews — short, AI-generated search summaries — are produced by large language models (LLMs — AI systems that generate human-like text). These systems can produce incorrect or made-up information (sometimes called hallucinations). Investigative reporting prompted Mind, the UK mental-health charity, to open a year-long international commission examining how AI affects mental health.

Problem: concise answers that erase context

The Guardian’s investigation found multiple examples where Google’s AI Overviews delivered misleading or potentially harmful guidance on topics from psychosis and eating disorders to cancer and liver disease. These aren’t obscure edge cases: when users are in distress, a short, authoritative-sounding summary can be mistaken for medical advice and may deter someone from seeking professional help.

“AI could greatly improve access to support, but misleading mental-health guidance is ‘dangerously incorrect’ and can put lives at risk; safeguards must match the risk.”

— Dr Sarah Hughes, CEO of Mind

Rosie Weatherley from Mind captured the design failure neatly: an AI summary can feel definitive because it removes the “source-rich” context that helps readers judge reliability. Think of AI Overviews as slick executive summaries with the footnotes ripped out — convenient, but risky when the stakes are high.

What Mind is doing: a cross-disciplinary commission

Mind’s commission will convene clinicians, ethicists, legal experts, people with lived experience, health providers, policymakers and tech firms. The goal is practical: map specific harms, recommend safeguards and propose standards or regulation to protect people while preserving the accessibility benefits of AI for health.

Key objectives include:

• Assessing real-world harms and near-miss incidents where AI guidance has misled users.
• Defining minimum safety features for mental-health AI (source attribution, uncertainty indicators, escalation paths).
• Designing testing protocols that include people with lived experience and clinical scenarios.
• Proposing policy and regulatory levers — from audit trails to sector-specific certification.

Google has defended its approach, saying it invests in the quality of AI Overviews and seeks to surface crisis hotlines when users appear distressed. That defense matters, but it doesn’t remove the need for independent scrutiny or standards tailored to mental-health risk.

Why this matters to business leaders

AI for business and AI automation are moving fast. Many companies are embedding generative AI (the same class of technology behind ChatGPT) into customer support, employee assistance programs, and triage tools. When those tools touch mental health or clinical-adjacent advice, the risks multiply.

Practical risks for organizations:

• Reputational damage from a single harmful recommendation that appears endorsed by your brand.
• Liability exposure where users rely on automated summaries instead of seeking professional help.
• Regulatory and compliance costs as lawmakers adopt sector-specific rules (e.g., EU AI Act, national guidance on health AI).
• Operational risk from untested escalation processes — missed transfers to human clinicians or crisis services.

Expect regulators and civil society to demand three things: transparency about how answers were generated, evidence of real-world testing (including with vulnerable users), and robust human-in-the-loop processes for high-risk outputs.

Practical checklist for product teams

• Source attribution: Always show where core claims come from and link to primary sources or clinical guidance.
• Uncertainty indicators: Display confidence bands or explicit language such as “This summary may be incomplete; consult a professional.”
• Human escalation: Provide clear, one-click routes to clinicians, crisis lines, or live support when risk is detected.
• Audit logs: Record prompts, model versions and outputs for post-incident review.
• Testing with lived experience: Include people who have navigated mental-health crises in testing and design feedback loops.
• Legal and clinical sign-off: Require review by legal counsel and licensed clinicians before public roll-out of health-facing features.

What leaders should do now — a 30–90 day roadmap

1. Inventory risk: Map where your products touch health or wellbeing (even indirectly).
2. Pause high-risk features: If a feature provides guidance on mental health or clinical matters, consider an immediate safety review or temporary limitation until safeguards are in place.
3. Implement quick wins: Add visible disclaimers, source links, and clear escalation buttons to live systems.
4. Engage experts: Convene clinicians, ethicists and people with lived experience to run focused scenario testing.
5. Plan governance: Build audit trails, assign accountability, and prepare documentation for potential regulators or auditors.

Policy and regulatory outlook

Mind’s commission will interact with broader regulatory movements. The EU AI Act, national proposals in the UK and evolving FDA thinking on AI/ML in medical devices all point toward a future where health-facing AI will require explicit risk categorization, transparency, and auditability. That may mean certification, mandated human oversight, or legal obligations around crisis detection and response.

For businesses, proactive alignment with emerging standards will reduce friction and future compliance costs. Treat regulation as a design constraint that drives safer, more defensible products rather than a compliance tax alone.

What to watch next

• Mind’s interim milestones: expect initial findings and best-practice proposals within the year-long commission timeframe.
• Regulatory signals: track EU and UK updates on AI and health — these will likely shape national obligations.
• Vendor responses: watch how major platforms adjust product features, attribution, and crisis handling after independent reviews.

“AI Overviews trade the prior richness of credible sources and context for a short, confident-sounding summary that can feel definitive but reduces trustworthiness.”

— Rosie Weatherley, Information Content Manager, Mind

Mind’s commission marks a turning point: the conversation is no longer just about whether generative AI can help people, but how to deploy it safely when mental health is on the line. For leaders, the choice is straightforward — accelerate the promise of AI for health, but do the engineering and governance work that prevents avoidable harm.

Quick resources: visit Mind’s site for commission details, read the investigative reporting that prompted this effort at The Guardian, and review vendor statements on AI Overviews at platform websites. Treat mental-health AI as high-stakes systems engineering: design, test, document and remediate — because when an automated answer becomes the difference between seeking help and staying silent, safety isn’t optional.