A team of researchers has demonstrated that an artificial intelligence model can now detect physiological signals associated with chronic stress using routine medical imaging scans. This breakthrough, presented at the annual meeting of the Radiological Society of North America (RSNA), marks a significant step forward in the search for an objective biomarker of prolonged stress, a phenomenon whose clinical impact is well documented but still difficult to quantify in a standardized manner1.
\n\n\n\nThe study is based on an analysis of chest CT scans performed on nearly 3,000 patients. The researchers applied a deep learning model capable of automatically extracting structural information about the adrenal glands, which are essential organs involved in the hormonal response to stress. The measurements obtained were combined with various clinical data, ranging from cortisol levels to blood pressure, in order to identify a robust and observable physiological marker reflecting the burden caused by chronic stress.
\n\n\n\nThe adrenal glands play a central role in regulating metabolism, blood pressure, the immune system, and stress responses. In this study, they serve as the primary biological source of the detected biomarker. Patients with high stress levels prior to the examination had, on average, larger adrenal glands, elevated cortisol levels, and a higher risk of heart failure2.
\n\n\n\nThis finding represents a significant contribution. Until now, chronic stress has been assessed using questionnaires or one-time hormonal measurements—methods that are useful but limited. The ability to identify a physiological marker directly in routine medical images opens up a new avenue for standardized and reproducible screening.
\n\n\n\nTo achieve this result, the researchers built a deep learning model trained on thousands of chest CT scans. They automatically extracted the size of the adrenal glands and then compared this data to various clinical indicators associated with chronic stress.
\n\n\n\nSome key figures from the study:
\n\n\n\nThis ability to extract biomedical signals invisible to the human eye confirms the potential of AI in the early detection of markers of mental and physical health.
\n\n\n\nThe identification of a biomarker for chronic stress opens up significant opportunities for prevention and medical management.
Among the potential applications:
This type of biomarker could also contribute to more targeted predictive medicine by identifying individuals at risk of physiological conditions that do not manifest in their symptoms.
\n\n\n\nLike any preliminary findings, these results should be interpreted with caution. The study has not yet been published in a peer-reviewed journal, and the widespread adoption of the biomarker requires:
\n\n\n\nWhile adrenal volume is linked to chronic stress, it cannot, on its own, fully capture the complexity of the phenomenon.
\n\n\n\nThis discovery also raises significant ethical issues. Medical images containing physiological indicators related to stress become particularly sensitive data. Their use in non-medical contexts—for example, for performance evaluations or insurance underwriting—would constitute a major misuse.
\n\n\n\nHealth data governance, informed consent, and restrictions on non-clinical uses are therefore essential to support the emergence of these new tools. Future regulations, particularly at the European level, will need to incorporate these new technological capabilities in order to prevent inequalities and discrimination.
\n\n\n\nThe identification of a biomarker for chronic stress using AI represents a significant breakthrough in understanding the physiological effects of stress. It paves the way for more objective diagnosis, more targeted prevention, and more personalized care.
\n\n\n\nThis discovery shows that AI, when combined with medical imaging, can reveal physiological signals that were previously undetectable. It is part of a broader trend in which medicine is leveraging automated analysis to develop predictive tools and facilitate the early identification of mental health risks.
\n\n\n\nTo learn more about the evolution of artificial intelligence tools in healthcare and understand how these technologies are reshaping medical practices, see: MedGPT: The Free French Medical AI That Rivals ChatGPT
\n\n\n\n1. Radiological Society of North America. (2025). Deep Learning Model Identifies Adrenal Biomarkers of Chronic Stress From Routine CT Imaging.
\nhttps://www.rsna.org \n
2. National Institutes of Health. (2024). Physiological Mechanisms of Chronic Stress: Cortisol, Adrenal Function, and Long-Term Health Outcomes.
https://www.nih.gov \n
A team of researchers has demonstrated that an artificial intelligence model can now detect physiological signals associated with chronic stress using routine medical imaging scans. This breakthrough, presented at the annual conference of…
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