Expert Ivan Kairatov joins us to discuss the rapidly evolving landscape of health technology, specifically focusing on the safety and reliability of consumer-facing AI tools. As a biopharma expert with deep experience in research and development, Kairatov offers a unique perspective on the intersection of large language models and clinical practice. Our conversation explores the findings of recent independent evaluations that highlight significant gaps in how these tools handle medical emergencies and mental health crises, the impact of demographic variables on AI recommendations, and the necessary evolution of medical training in an age of automated health advice.
AI tools often correctly identify textbook emergencies like strokes but under-triage more subtle conditions like respiratory failure. How do these systems fail when explaining dangerous symptoms while simultaneously advising against urgent care, and what specific metrics should developers use to bridge this clinical gap?
The disconnect we see in these systems is a phenomenon where the model displays “internal” clinical knowledge but fails to translate that into a safe “external” directive. In the recent study involving 60 structured clinical scenarios, researchers found that while ChatGPT Health correctly identified signs of respiratory failure in its reasoning, it still recommended waiting rather than seeking immediate help. This indicates that the logic layer is decoupled from the safety-action layer, which is extremely dangerous when more than half of emergency cases are under-triaged. Developers must move beyond simple accuracy metrics and implement “fail-safe” clinical benchmarks that prioritize high-sensitivity triage. We need specific metrics that penalize a model more heavily for a “false negative” in an emergency than for a “false positive” to ensure that whenever a life-threatening symptom is mentioned in the explanation, the output must automatically default to an emergency recommendation.
Automated crisis alerts sometimes appear for low-risk scenarios but fail to trigger when individuals share specific, actionable plans for self-harm. Why does this inversion of clinical risk occur in large language models, and what step-by-step improvements are necessary to ensure high-risk signals consistently trigger life-saving resources?
This inversion is one of the most alarming findings, as the 988 Suicide and Crisis Lifeline alerts appeared inconsistently and often missed the most severe cases. In clinical practice, a specific, actionable plan for self-harm is the highest red flag, yet the AI seems to treat these detailed descriptions as less urgent than more general, lower-risk mentions of distress. This likely happens because the model’s training on safety guardrails might be too focused on keyword matching rather than understanding the intent and lethality of the content. To fix this, we need a step-by-step overhaul: first, integrating gold-standard clinical psychiatric protocols into the model’s core logic; second, conducting rigorous “red-teaming” specifically for high-lethality scenarios; and third, ensuring that any mention of specific self-harm methods triggers a mandatory, unblockable redirect to crisis resources.
Physicians rely on nuanced judgment that separates a missed emergency from a minor concern, a skill that current consumer AI frequently lacks. In what specific medical scenarios does clinical nuance most often clash with algorithmic logic, and how can medical training adapt to help practitioners identify these automated blind spots?
Clinical nuance is most vital in cases where symptoms are vague or “sub-textual,” such as an asthma patient who isn’t gasping yet but shows early signs of tiring out. The study demonstrated that while AI handles “textbook” cases like strokes well, it falters in these subtle transitions toward instability. As these tools reach 40 million daily users, medical training must evolve to include “AI literacy” as a core competency for students. Future doctors need to be trained to identify where these 960 tested interactions typically fail, learning to treat AI outputs as one of many data points rather than an absolute authority. We must teach practitioners to recognize the specific “blind spots” of LLMs, such as their tendency to be overly reassuring even when the data suggests a patient is deteriorating.
Health recommendations can fluctuate based on a user’s race, gender, or social dynamics, such as whether a patient minimizes their own symptoms. How should evaluation frameworks account for these contextual variables to ensure equity, and what are the practical implications for patients who lack health insurance?
Evaluation frameworks must be built on the reality that medicine does not happen in a vacuum, which is why testing under 16 different contextual conditions—including race, gender, and social barriers—is so critical. If a patient minimizes their symptoms or mentions they lack health insurance, the AI might inadvertently deprioritize their urgency, leading to inequitable care. For those without insurance or transportation, a “wait and see” recommendation from an AI can be the difference between a treatable condition and a fatal one because these patients often lack a secondary safety net. We need to mandate that AI developers test their models against diverse socio-economic profiles to ensure that the “logic” of the machine doesn’t bake in existing systemic biases or discourage vulnerable populations from seeking necessary care.
Millions of people now use artificial intelligence as their first stop for medical guidance, yet these tools remain least reliable at the extremes of clinical risk. What are the primary trade-offs when treating these systems as assistants rather than substitutes, and how can they be safely integrated into care?
The primary trade-off is the balance between accessibility and safety; while AI offers instant health information to millions, it lacks the accountability of a human physician who follows guidelines from 56 different medical societies. When we treat AI as an assistant, we use it to summarize information or explain terms, but we must never let it be the final arbiter of triage urgency. Safe integration requires a “human-in-the-loop” philosophy where the AI provides data but clearly directs patients to professional care for symptoms like chest pain, shortness of breath, or mental status changes. We must view these tools as evolving technologies that require constant, independent evaluation—much like the fast-tracked research in Nature Medicine—to ensure that as the technology updates, the safety thresholds are not lowered.
What is your forecast for consumer-facing AI health tools?
I believe we are entering an era of “clinical-grade” AI where the wild-west approach of general-purpose chatbots will be replaced by specialized, highly regulated medical LLMs. In the next few years, I forecast that independent, routine safety evaluations will become a regulatory requirement rather than an optional academic exercise. We will see these tools transition from being simple “answer engines” to sophisticated triage assistants that are deeply integrated with electronic health records and emergency services. However, this progress depends entirely on our ability to close the safety gaps identified today; if we can’t ensure that a system identifies a life-threatening respiratory failure 100% of the time, these tools will remain a high-risk gamble for the average consumer.
