{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:58:57Z","timestamp":1772103537871,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science, Technological Development and Innovation of the Republic of Serbia","award":["451-03-137\/2025-03\/200102"],"award-info":[{"award-number":["451-03-137\/2025-03\/200102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 \u00b1 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 \u00b1 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study.<\/jats:p>","DOI":"10.3390\/computers14080320","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T10:58:26Z","timestamp":1754564306000},"page":"320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Healthcare AI for Physician-Centered Decision-Making: Case Study of Applying Deep Learning to Aid Medical Professionals"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1796-7536","authenticated-orcid":false,"given":"Aleksandar","family":"Milenkovic","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Electronic Engineering in Nis, University of Nis, 18000 Nis, Serbia"}]},{"given":"Andjelija","family":"Djordjevic","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Electronic Engineering in Nis, University of Nis, 18000 Nis, Serbia"}]},{"given":"Dragan","family":"Jankovic","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Electronic Engineering in Nis, University of Nis, 18000 Nis, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4998-2036","authenticated-orcid":false,"given":"Petar","family":"Rajkovic","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Electronic Engineering in Nis, University of Nis, 18000 Nis, Serbia"}]},{"given":"Kofi","family":"Edee","sequence":"additional","affiliation":[{"name":"Institut Pascal, CNRS, Clermont Auvergne INP, Universit\u00e9 Clermont Auvergne, F-63000 Clermont-Ferrand, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7850-2778","authenticated-orcid":false,"given":"Tatjana","family":"Gric","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shneiderman, B. (2022). Human-Centered AI, Oxford Academic.","DOI":"10.1093\/oso\/9780192845290.001.0001"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chouhan, V., Singh, S.K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Dama\u0161evi\u010dius, R., and de Albuquerque, V.H.C. (2020). A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. Appl. Sci., 10.","DOI":"10.3390\/app10020559"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abidoye, I., Ikeji, F., Coupland, C.A., Calaminus, S.D.J., Sander, N., and Sousa, E. (2025). Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs. J. Imaging, 11.","DOI":"10.3390\/jimaging11060183"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.procs.2018.05.198","article-title":"An Analysis of Convolutional Neural Networks For Image Classification","volume":"132","author":"Neha","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shaheed, K., Szczuko, P., Abbas, Q., Hussain, A., and Albathan, M. (2023). Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier. Healthcare, 11.","DOI":"10.3390\/healthcare11060837"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.tranpol.2025.03.002","article-title":"Exploring passengers\u2019 choices in the event of denied boarding compensation","volume":"166","author":"Tsai","year":"2025","journal-title":"Transp. Policy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1016\/S0140-6736(21)00370-6","article-title":"SARS-CoV-2 variants and ending the COVID-19 pandemic","volume":"397","author":"Arnaud","year":"2021","journal-title":"Lancet"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/S0140-6736(20)30185-9","article-title":"A novel coronavirus outbreak of global health concern","volume":"395","author":"Chen","year":"2020","journal-title":"Lancet"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"901","DOI":"10.2471\/BLT.21.286249","article-title":"Towards a universal understanding of post COVID-19 condition","volume":"99","author":"Janet","year":"2021","journal-title":"Bull. World Health Organ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ijid.2020.03.071","article-title":"Insight into 2019 novel coronavirus\u2014An updated intrim review and lessons from SARS-CoV and MERS-CoV","volume":"94","author":"Xie","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_12","unstructured":"European Data (2025, April 30). COVID-19 Coronavirus Data 2020. Available online: https:\/\/data.europa.eu\/euodp\/en\/data\/dataset\/covid-19-coronavirus-data\/resource\/55e8f966-d5c8-438e-85bc-c7a5a26f4863."},{"key":"ref_13","unstructured":"Basant, A., Valentina, E.B., Lakhmi, C.J., Ramesh, C.P., and Manisha, S. (2020). Deep Learning Techniques for Biomedical and Health Informatics, Academic Press. [1st ed.]."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., and Pfeiffer, D. (2019). Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-42557-4"},{"key":"ref_15","unstructured":"Linda, W., and Alexander, W. (2020). COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv."},{"key":"ref_16","unstructured":"Schmitt, M. (2025, April 30). How to Fight COVID-19 with Machine Learning. Available online: https:\/\/medium.com\/data-science\/fight-covid-19-with-machine-learning-1d1106192d84."},{"key":"ref_17","unstructured":"Ali, N., Ceren, K., and Ziynet, P. (2020). Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., and Su, J. (2020). Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia. arXiv.","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.clinimag.2020.04.001","article-title":"Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review","volume":"64","author":"Adam","year":"2020","journal-title":"Clin. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, N., Paul, R., Guerra, S., Liu, Y., Doulgeris, J., Shi, M., Lin, M., Engeberg, E.D., Hashemi, J., and Vrionis, F.D. (2024). The Frontiers of Smart Healthcare Systems. Healthcare, 12.","DOI":"10.3390\/healthcare12232330"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108405","DOI":"10.1016\/j.compeleceng.2022.108405","article-title":"COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario","volume":"104","author":"Babukarthik","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1001\/jama.2020.3413","article-title":"Priorities for the US Health Community Responding to COVID-19","volume":"3","author":"Adalja","year":"2020","journal-title":"JAMA"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ramirez-Alcocer, U.M., Tello-Leal, E., Romero, G., and Mac\u00edas-Hern\u00e1ndez, B.A. (2023). A Deep Learning Approach for Predictive Healthcare Process Monitoring. Information, 14.","DOI":"10.3390\/info14090508"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bhati, D., Neha, F., and Amiruzzaman, M. (2024). A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. J. Imaging, 10.","DOI":"10.20944\/preprints202408.0765.v1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2618","DOI":"10.3390\/ai5040126","article-title":"ChatGPT: Transforming Healthcare with AI","volume":"5","author":"Neha","year":"2024","journal-title":"AI"},{"key":"ref_26","unstructured":"(2024, December 05). TensorFlow. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_27","unstructured":"(2025, April 30). Keras. Available online: https:\/\/keras.io\/."},{"key":"ref_28","unstructured":"(2025, April 30). NumPy. Available online: https:\/\/numpy.org\/."},{"key":"ref_29","unstructured":"(2025, April 30). Pandas. Available online: https:\/\/pandas.pydata.org\/."},{"key":"ref_30","unstructured":"(2025, April 30). Matplotlib. Available online: https:\/\/matplotlib.org\/."},{"key":"ref_31","unstructured":"(2025, April 30). OpenCV. Available online: https:\/\/opencv.org\/."},{"key":"ref_32","unstructured":"(2025, April 30). Scikit-Learn. Available online: https:\/\/scikit-learn.org\/."},{"key":"ref_33","unstructured":"(2025, April 30). PyCharm. Available online: https:\/\/www.jetbrains.com\/pycharm\/."},{"key":"ref_34","unstructured":"Cohen, J.P., Morrison, P., and Dao, L. (2020). COVID-19 image data collection. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0140-6736(22)01485-4","article-title":"Acute respiratory distress syndrome: Causes, pathophysiology, and phenotypes","volume":"400","author":"Bos","year":"2022","journal-title":"Lancet"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/S0140-6736(19)33221-0","article-title":"Middle East respiratory syndrome","volume":"395","author":"Memish","year":"2020","journal-title":"Lancet"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bugl, P. (2007). SARS in Context: Memory, History, Policy, McGill-Queen\u2019s University Press. The Lancet Infectious Diseases.","DOI":"10.1016\/S1473-3099(07)70259-1"},{"key":"ref_38","unstructured":"Mooney, P. (2025, June 06). Chest X-Ray Images (Pneumonia). Available online: https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mienye, I.D., Swart, T.G., Obaido, G., Jordan, M., and Ilono, P. (2025). Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information, 16.","DOI":"10.3390\/info16030195"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Salehi, A.W., Khan, S., Gupta, G., Alabduallah, B.I., Almjally, A., Alsolai, H., Siddiqui, T., and Mellit, A. (2023). A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. Sustainability, 15.","DOI":"10.3390\/su15075930"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Raptis, C., Karavasilis, E., Anastasopoulos, G., and Adamopoulos, A. (2024). Comparative Analysis of Conventional CNN v\u2019s ImageNet Pretrained ResNet in Medical Image Classification. Information, 15.","DOI":"10.3390\/info15120806"},{"key":"ref_42","unstructured":"Brownlee, J. (2025, June 06). Transfer Learning in Keras with Computer Vision Models. Available online: https:\/\/machinelearningmastery.com\/how-to-use-transfer-learning-when-developing-convolutional-neural-network-models\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"871","DOI":"10.3390\/agriengineering4040056","article-title":"A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease","volume":"4","author":"Nguyen","year":"2022","journal-title":"AgriEngineering"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.3390\/make6020065","article-title":"Cross-Validation Visualized: A Narrative Guide to Advanced Methods","volume":"6","author":"Allgaier","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104224","DOI":"10.1016\/j.ijmedinf.2020.104224","article-title":"Extensions and Adaptations of Existing Medical Information System in Order to Reduce Social Contacts During COVID-19 Pandemic","volume":"141","author":"Milenkovic","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_46","unstructured":"Krishnan, M. (2025, April 30). Understanding the Classification Report Through Sklearn. Available online: https:\/\/muthu.co\/understanding-the-classification-report-in-sklearn\/."},{"key":"ref_47","unstructured":"Jayaswal, V. (2025, June 06). Performance Metrics: Confusion Matrix, Precision, Recall, and F1 Score. Available online: https:\/\/towardsdatascience.com\/accuracy-precision-recall-or-f1-331fb37c5cb9."},{"key":"ref_48","first-page":"643","article-title":"Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine","volume":"5","author":"Sethy","year":"2020","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","article-title":"A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)","volume":"31","author":"Wang","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.tre.2019.07.001","article-title":"Optimal overbooking strategies in the airlines using dynamic programming approach in continuous time","volume":"128","author":"Fard","year":"2019","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Imani, M., Beikmohammadi, A., and Arabnia, H.R. (2025). Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels. Technologies, 13.","DOI":"10.20944\/preprints202501.2274.v2"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Grzelak, M., Owczarek, P., Stoica, R.-M., Voicu, D., and Vil\u0103u, R. (2024). Application of Logistic Regression to Analyze the Economic Efficiency of Vehicle Operation in Terms of the Financial Security of Enterprises. Logistics, 8.","DOI":"10.3390\/logistics8020046"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhou, Z. (2025). Ensemble Methods: Foundations and Algorithms, CRC press.","DOI":"10.1201\/9781003587774"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.2298\/CSIS120523056R","article-title":"Developing and Deploying Medical Information Systems for Serbian Public Healthcare\u2014Challenges, Lessons Learned and Guidelines","volume":"10","year":"2013","journal-title":"ComSIS"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"122","DOI":"10.5633\/amm.2018.0417","article-title":"Analysis of the level of use and acceptance of the medical information system in primary health care","volume":"57","year":"2018","journal-title":"Acta Medica Median."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/8\/320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:25:28Z","timestamp":1760034328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/8\/320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"references-count":55,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["computers14080320"],"URL":"https:\/\/doi.org\/10.3390\/computers14080320","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}