AI Spots Hidden Heart Disease Signals in Ordinary Chest X-Rays, Study Finds

A routine chest X-ray, taken millions of times a day in hospitals and clinics around the world, may contain far more information about the heart than radiologists have traditionally extracted from it. A new study from researchers at Basaksehir Cam and Sakura City Hospital in Istanbul suggests that a vision-capable large language model, the same class of artificial intelligence systems that can describe photographs and answer questions about them, can pick up subtle hints of coronary artery calcification from plain chest radiographs. The findings, published in BMC Medical Imaging, point toward a future in which a test ordered for a cough or chest pain could double as an opportunistic screen for silent cardiovascular risk, without any additional radiation, cost, or scheduling.

Coronary artery calcium, or CAC, is one of the most reliable markers available for predicting future cardiovascular events. Calcium deposits build up in the walls of the coronary arteries over years, and the amount present, quantified by the well-established Agatston score on a non-contrast computed tomography scan, correlates closely with the likelihood of heart attack and stroke. Despite this diagnostic power, dedicated cardiac CT for calcium scoring is not performed universally, largely because it is costly, involves radiation exposure, and is generally reserved for patients already considered at intermediate risk. The vast reservoir of cardiovascular information embedded in images already being acquired, the chest X-rays gathering dust in hospital archives, remains largely untapped.

The research team set out to test whether a general-purpose, vision-capable large language model could bridge that gap. Their retrospective, single-center diagnostic accuracy study included 349 patients who had undergone both a non-contrast cardiac CT for calcium scoring and a chest radiograph within a six-month window. Using the CT-derived Agatston classification as the reference standard, the researchers divided the cohort into two clinically meaningful groups: those with a score of 100 or below, and those exceeding 100, the threshold generally regarded as indicating clinically significant coronary calcification that warrants aggressive risk management. Of the 349 patients, 131 fell into the high-calcium category.

The technical approach was striking in its simplicity. Rather than training a bespoke neural network on thousands of labeled images, as is typical for medical AI studies, the team fed preprocessed chest radiographs to GPT-5.5 in a zero-shot setting, meaning the model received no task-specific training whatsoever. Each image passed through a preprocessing pipeline that included contrast-limited adaptive histogram equalization, a technique that enhances local image contrast and can make faint calcific densities more visible. The model was given a structured prompt instructing it to estimate the probability that clinically significant calcification was present, assign a binary classification, and describe any visible calcification findings, mimicking the reasoning process of a human reader.

The results were cautiously encouraging. Using the model’s native binary output, the system correctly classified 292 of the 349 patients, for an overall accuracy of 83.7 percent. Sensitivity, the ability to detect patients who truly had high calcium burden, reached 72.5 percent, while specificity, the ability to correctly clear patients with low calcium, was higher at 90.4 percent. The positive predictive value stood at 81.9 percent and the negative predictive value at 84.5 percent. Perhaps most notably, the area under the receiver operating characteristic curve, a threshold-independent measure of discriminative ability, was 0.771, a figure comparable to some dedicated deep-learning systems developed for similar opportunistic screening tasks after extensive training.

The picture became more complicated, however, when the researchers examined the model’s continuous probability scores rather than its binary judgments. Applying a prespecified threshold of 0.50 to the raw scores yielded dramatically asymmetric performance: sensitivity dropped to 36.6 percent while specificity climbed to 94.0 percent. In other words, when the model did flag a radiograph as suspicious, that flag was very likely to be correct, but it missed the majority of high-calcium patients at that cutoff. The model-generated scores were indeed significantly higher in patients with CT-confirmed CAC above 100, confirming a genuine statistical association, yet the scores overlapped substantially in the intermediate range, blurring the boundary between low and high risk for a meaningful fraction of patients.

Calibration emerged as another significant weakness. A well-calibrated diagnostic score should correspond reliably to actual probability, with a reported risk of 70 percent meaning the finding is truly present in roughly seven of ten such cases. The calibration slope in this study was 2.49, far from the ideal value of 1, indicating that the model’s expressed confidence was poorly aligned with reality. For clinicians accustomed to interpreting validated risk tools, this miscalibration matters enormously, because clinical decisions often hinge not merely on whether a test is positive or negative, but on how much weight the numeric estimate can bear. The authors were transparent about this limitation, framing their results as exploratory and hypothesis-generating rather than clinically actionable.

The concept underlying the study, sometimes called opportunistic screening, has been gaining momentum across medical imaging. The idea is elegant: rather than recruiting patients for dedicated cardiovascular examinations, extract additional diagnostic value from examinations already performed for other reasons. CT scans done for lung screening have been mined for calcium scores, bone density, and fatty liver indicators. Chest radiographs, being the most common imaging examination in medicine, represent perhaps the largest potential reservoir of such incidental information. If an AI system could reliably flag patients with significant coronary calcification from these routine images, those patients could be referred for confirmatory CT calcium scoring, targeted statin therapy, or other preventive interventions years before a first cardiac event.

What makes the use of a zero-shot vision-language model particularly noteworthy is the departure from the conventional AI development paradigm. Building a dedicated medical imaging classifier typically demands curated datasets, expert annotation, fine-tuning, and validation across institutions, investments that slow the translation of algorithms into practice. Foundation models, by contrast, acquire broad visual and linguistic capabilities from web-scale training and can be steered toward new tasks through prompting alone. The Istanbul study offers an early signal that this generality may extend to subtle radiological patterns, such as coronary calcification, that were never explicitly part of the model’s training objectives. It also raises intriguing questions about whether such models perceive image features that resemble human radiological signs or rely on entirely different statistical correlations invisible to human interpretation.

The authors are careful to emphasize the considerable distance between their findings and any bedside application. The cohort was selected, comprising patients who had already undergone cardiac CT, a population very different from the unselected stream of patients whose chest X-rays would be analyzed in a true screening scenario. Human-reader benchmarking was not performed, so it remains unknown how the model compares with radiologists deliberately searching for calcification. Prospective validation in diverse populations, assessment of calibration after refinement, and evaluation of downstream clinical outcomes would all be required before opportunistic AI flagging could enter practice. Still, the central result stands: a general-purpose vision-capable large language model, without a single example of task-specific training, produced image-derived scores significantly associated with CT-defined coronary artery calcium category on plain chest radiographs. That a conversation-ready AI can glance at a decades-old imaging technology and detect the chemical fingerprints of atherosclerosis hints at how much diagnostic potential remains hidden in the world’s existing medical images, waiting for the right tools to reveal it.

Subject of Research: Using a vision-capable large language model to classify coronary artery calcium on chest radiographs

Article Title: Association between a vision-capable large language model’s image-derived score and CT-defined coronary artery calcium category on routine chest radiographs: a single-center retrospective study

Article References: Ayyildiz, H., Banaz, T., Kazanbas, M. F., Gunes, S. T., Arslan, A., Kartal, Y. C., & Ozkok, S. (2026). Association between a vision-capable large language model’s image-derived score and CT-defined coronary artery calcium category on routine chest radiographs: a single-center retrospective study. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02757-7

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02757-7

Keywords: coronary artery calcium, chest radiograph, large language model, GPT-5.5, zero-shot learning, diagnostic accuracy, opportunistic screening, cardiovascular risk, Agatston score, medical imaging AI, computed tomography, BMC Medical Imaging