Are we there yet? AI on traditional blood tests efficiently detects common and rare diseases

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Abstract Chronic workforce shortages, unequal distribution, and rising labor costs are crucial challenges for most healthcare systems. The past years have seen a rapid technological transition to counter these pressures. We developed an AI-assisted software with ensemble learning on a retrospective data set of over one million patients that only uses routine and broadly available blood tests to predict the possible presence of major chronic and acute diseases as well as rare disorders. We evaluated the software performance with three main approaches that are 1) statistics of the ensemble learning focusing on ROC-AUC (weighted average: 0.9293) and DOR (weighted average: 63.96), 2) simulated recall by the model-generated risk scores in order to estimate screening effectiveness and 3) performance on early detection (30–270 days before established clinical diagnosis) via creating historical anamnestic patient timelines. We found that the software can significantly improve three important aspects of everyday medical practice. The software can recognize patterns associated with both common and rare diseases, including malignancies, with outstanding performance. It can also predict the later diagnosis of selected disease groups 1–9 months before the establishment of clinical diagnosis and thus could play a key role in early diagnostic efforts. Lastly, we found that the tool is highly robust and performs well on data from various independent laboratories and hospitals on widely available routine blood tests. Compared to decision systems based on medical imaging, our system relies purely on widely available and inexpensive diagnostic tests.