Ákos Németh ¹ Gábor Tóth ¹ Péter Fülöp ¹ György Paragh ¹ Bíborka Nádró ¹ Zsolt Karányi ¹ Gyorgy Paragh ² Zsolt Horváth ³ Zsolt Csernák ⁴ Erzsébet Pintér ⁴ Dániel Sándor ⁵ Gábor Bagyó ⁶ István Édes ¹ Janos Kappelmayer ¹ Mariann Harangi ¹ Balint Daroczy ^7*

• ¹ University of Debrecen, Debrecen, Hajdu-Bihar, Hungary
• ² Roswell Park Comprehensive Cancer Center, University at Buffalo, Buffalo, New York, United States
• ³ Bács-Kiskun County Teaching Hospital, Nyíri út 38, Kecskemét, H-6000, Hungary
• ⁴ SYNLAB Hungary Kft., Budapest, Hungary
• ⁵ Budapest University of Technology and Economics, Budapest, Hungary
• ⁶ Evidia MVZ Radiologie, Nürnberg, Germany
• ⁷ Institute for Computer Science and Control HUN-REN SZTAKI, Budapest, Hungary

The integration of AI into healthcare is widely anticipated to revolutionize medical diagnostics, enabling earlier, more accurate disease detection and personalized care. In this study, we developed and validated an AI-assisted diagnostic support tool using only routinely ordered and broadly available blood tests to predict the presence of major chronic and acute diseases as well as rare disorders. Our model was tested on both retrospective and prospective datasets comprising over one million patients. We evaluated the diagnostic performance by 1) implementing ensemble learning (mean ROC-AUC .9293 and mean DOR 63.96); 2) assessing the model's sensitivity via risk scores to simulate its screening effectiveness; 3) analyzing the potential for early disease detection (30-270 days before clinical diagnosis) through creating historical patient timelines and 4) conducting validation on real-world clinical data in collaboration with Synlab Hungary, to assess the tool's 1 performance in clinical setting. Uniquely, our model not only considers stable blood values but also tracks changes from baseline across 15 years of patient history. Our AI-driven automated diagnostic tool can significantly enhance clinical practice by recognizing patterns in common and rare diseases, including malignancies. The models' ability to detect diseases 1-9 months earlier than traditional clinical diagnosis could contribute to reduced healthcare costs and improved patient outcomes. The automated evaluation also reduces evaluation time of healthcare providers, which accelerates diagnostic processes. By utilizing only routine blood tests and ensemble methods, the tool demonstrates high efficacy across independent laboratories and hospitals, making it an exceptionally valuable screening resource for primary care physicians.