The High Cost of Delay: Healthcare Transformation and Safety in the Age of AI
Artificial intelligence is no longer a future disruptor of health care. It is here, embedded in clinical documentation tools, risk-prediction algorithms, digital therapeutics, chatbots, and conversational systems that millions use daily. For healthcare systems, AI promises efficiency, scale, and innovation. For mental and behavioral health professionals, it promises expanded reach in a time of workforce shortages and rising demand.
But the internet, social media, and human history offers a warning: when emotionally immersive technologies outpace policy, research, and oversight, the cost of delay is measured not in regulatory inconvenience, but in human harm.
The stakes in health care are higher than in any prior technological revolution. AI in mental and behavioral health does not merely shape consumer behavior, it shapes diagnosis, treatment access, emotional attachment, and in some documented cases, life-or-death outcomes. Many people are using AI chatbots as therapists and diagnosticians.
Healthcare transformation in the age of AI must therefore be guided by one principle above all others: Don’t delay regulation and crisis response.
The Danger of Delay
The rapid adoption of generative AI systems, particularly conversational chatbots, has introduced a new category of psychological risk. A growing body of evidence documents patterns of anthropomorphism, emotional attachment, dependency behaviors, delusional thinking, and crisis incidents associated with intensive AI interaction.
Adolescents and individuals with preexisting mental health vulnerabilities appear especially susceptible to developing emotional reliance on these systems (Robert Head, 2025). Research cited in Minds in Crisis describes the formation of parasocial bonds with AI entities that mirror attachment models traditionally observed in human relationships (Robert Head, 2025). These are not abstract concerns. Documented crisis cases include reinforcement of suicidal ideation, exacerbation of psychosis, and escalating dependency behaviors (Robert Head, 2025). These are lessons we have already learned from the expansion of the internet, social media, and gaming industry.
At a population level, AI’s influence extends beyond individual users. Research tells us that AI affects mental health through three pathways: the delivery of care, shifts in social and economic context, and the policy environment that governs its use (Ettman and Galea, 2023). Replacing human compassion, judgment, and lived experience with automated responses introduces unknown long-term consequences (Ettman and Galea, 2023).
These are not abstract concerns. They are unfolding in real time. We also know that in the absence of understanding, awareness, policy, and regulation, big business and “innovators” will prey on vulnerable populations, especially children.
Advanced AI technologies, including deepfake image generation, AI chatbots used for online grooming, and digital image manipulation, have expanded the scale and sophistication of child exploitation. Furthermore, we know from Epstein and other traffickers that predatory behaviors don’t stay inside the screen. AI is/will be used for grooming and human trafficking.
Social media platforms scaled before guardrails were built. Only after widespread mental health impacts among youth did policymakers and public health officials mobilize. Conversational AI introduces an even more immersive human-like emotional dynamic. These systems simulate empathy, provide false companionship, and remain perpetually available. They engage attachment systems directly. At the same time, AI-powered mental health tools are entering clinical settings faster than professional standards are evolving
We already see the cost of delay.
Writing a Different Ending
Artificial intelligence will shape the future of health care. That reality is not in question. What remains undecided is whether the transformation will strengthen equity and safety, or deepen disparities and vulnerability. Guardrails around AI-generated responses are essential. AI systems could potentially provide harmful information related to self-harm or suicide methods if not properly constrained.
To prevent scalable harm and ensure responsible healthcare transformation, policy makers should be proactive:
Update child protection and cybercrime laws to explicitly address AI-based manipulation technologies such as deepfakes and AI-driven grooming.
Require built-in safeguards that prevent AI systems from facilitating suicide or violent behaviors and instead redirect users to crisis resources.
Support interdisciplinary research examining economic disruption, social fragmentation, and long-term attachment effects.
Extend privacy safeguards to AI-powered platforms and mobile health tools beyond traditional HIPAA boundaries.
Healthcare leaders, policymakers, and public health professionals have a narrow window to act. This time, safety must come first because once harm is automated, correction becomes exponentially harder.
And in healthcare, the price of waiting is too high.
References
Ettman, C. K., & Galea, S. (2023). The Potential Influence of AI on Population Mental Health. JMIR Mental Health, 10(37971803), e49936–e49936. https://doi.org/10.2196/49936
Head, K. R. (2025). Minds in Crisis: How the AI Revolution is Impacting Mental Health. JOURNAL of MENTAL HEALTH and CLINICAL PSYCHOLOGY, 9(3), 34–44. https://doi.org/10.29245/2578-2959/2025/3.1352
Kurian, N. (2024). “No, Alexa, no!”: designing child-safe AI and protecting children from the risks of the “empathy gap” in large language models. Learning, Media & Technology/Learning, Media and Technology, 1–14. https://doi.org/10.1080/17439884.2024.2367052
Saeidnia, H. R., Hashemi, G., Lund, B., & Ghiasi, N. (2024). Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact. Social Sciences, 13(7), 381–381. https://doi.org/10.3390/socsci13070381