This article, from RGA’s ReFlections, explores how AI is already improving medical care and efficiency, but its greatest long-term value will depend on whether we pair innovation with validation, transparency, and human accountability.
Artificial intelligence has moved from possibility to practice. In medicine, it is helping clinicians detect disease earlier, reduce administrative burden, and extract meaning from increasingly complex data. In insurance, it is beginning to reshape how evidence is gathered, organized, and interpreted.
The opportunity is real, but so is the need for restraint. In both domains, the central challenge is the same: How do we let AI inform decisions without quietly letting it make them for us?
An old story offers a useful frame. In the myth of Pandora, her curiosity leads her to open the box, releasing hardship into the world; but one thing remains inside – hope.
AI feels similar. The box is already open. New tools are arriving quickly, and some consequences are difficult to predict. That does not require pessimism. It necessitates informed optimism: A willingness to recognize AI’s value, confront its risks honestly, and keep human judgment at the center.
AI’s most convincing success in medicine so far is not futuristic autonomy. It is practical augmentation. Some of the earliest gains are administrative benefits – transcription, summarization, and workflow support – that reduce clerical burden and may return time to patient care.
More importantly, AI is beginning to surface subtle disease patterns before they are apparent to human eyes or fully captured by conventional investigations.
A particularly powerful example is CHARTWatch, an AI early-warning system developed at Unity Health Toronto to identify hospitalized patients at risk of unexpected deterioration. In one study, its implementation was associated with a 26% reduction in unanticipated mortality.1 Across medicine, similar promise is emerging in imaging, mammography, AI-ECG, and lung cancer risk prediction from low-dose CT scans. These tools suggest that AI’s greatest strength may be its ability to surface weak signals early, before they are obvious to the human eye.
For underwriters, that creates both opportunity and caution. AI can reduce the friction of fragmented records and large evidence files, helping professionals focus less on data assembly and more on judgment. But AI-derived signals should be interpreted as prompts or risk markers, not as stand-alone rating triggers, unless they are validated, calibrated, and clearly understood in the relevant population.
AI raises many legitimate concerns, among them privacy, bias, liability, and explainability. But a key emerging risk may be false confidence. AI outputs often sound polished, efficient, and authoritative. That style can create the illusion of reliability, even when the underlying conclusion is incomplete, biased, or wrong. In high-stakes fields such as medicine and underwriting, authoritative error is especially dangerous.
This risk appears in several forms. Some models perform well without offering a defensible explanation of how they reached a conclusion. Generative systems can produce fluent but inaccurate answers. Because AI learns from historical data, it can reproduce and scale existing biases while giving them a veneer of objectivity. Science itself is rarely perfect or final; evidence evolves, datasets are incomplete, and human interpretation is never entirely free of cognitive bias. Even when data is intended to be accurate, we still bring motivated reasoning and other universal human biases to how evidence is collected, interpreted, and applied. AI does not erase those distortions. It can perpetuate and proliferate them at scale. That is why expertise remains essential: Professionals must be able to challenge conclusions, judge the quality of the underlying data, and resist treating AI outputs as absolute truth.
Pandora’s story offers a useful perspective, acknowledging that transformative discoveries can introduce risk and uncertainty while still leaving space for something meaningful and enduring.
AI can reduce clerical and administrative burden, improve consistency, and help detect disease earlier. In insurance, it can improve workflow, highlight relevant patterns, and support more efficient decision-making. But its proper role is augmentation, not replacement.
Maintaining optimism inside requires discipline: Start with real use cases, demand validation, distinguish assistive tools from predictive ones, and preserve privacy, transparency, and accountability. Above all, humans must remain responsible for the decisions and their consequences.
The future of AI in medicine will not be decided by the technology alone. It will be decided by how wisely we choose to govern it.
AI’s early success in medicine is real, and its relevance to underwriting is growing. The opportunity lies in earlier insight, greater efficiency, and better support for human decision-making. The risk lies in trusting these systems more than they deserve. Between those two lies the discipline this moment requires: A willingness to welcome AI where it sharpens insight and improves care, while resisting the temptation to grant it more authority than the evidence supports. Its promise will be realized not through blind adoption or premature dismissal, but through governance, validation, and the continued presence of human judgment at the point of decision.
Do you want to learn more? Find additional information about AI in medicine in ReFlections, the industry’s premier medical underwriting publication.
