{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:15:01Z","timestamp":1772727301149,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prince Sultan University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes.<\/jats:p>","DOI":"10.3390\/jimaging10120322","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T10:34:12Z","timestamp":1734086052000},"page":"322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9937-2536","authenticated-orcid":false,"given":"Sibtain","family":"Syed","sequence":"first","affiliation":[{"name":"School of Computing Sciences, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology (PAF-IAST), Mang, Haripur 22621, Khyber Pakhtunkhwa, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0740-2810","authenticated-orcid":false,"given":"Rehan","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Computing Sciences, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology (PAF-IAST), Mang, Haripur 22621, Khyber Pakhtunkhwa, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7189-5564","authenticated-orcid":false,"given":"Arshad","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Sino-Pak Center for Artificial Intelligence (SPCAI), Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Mang, Haripur 22621, Khyber Pakhtunkhwa, Pakistan"}]},{"given":"Naveed","family":"Ahmad","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6892-5400","authenticated-orcid":false,"given":"Mohammed Ali","family":"Alshara","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"ref_1","first-page":"49","article-title":"Improving Heart Disease Prediction Accuracy Using a Hybrid Machine Learning Approach: A Comparative study of SVM and KNN Algorithms","volume":"3","author":"Ahmed","year":"2023","journal-title":"Int. J. Comput. Inf. Manuf. IJCIM"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2124","DOI":"10.7717\/peerj-cs.2124","article-title":"Recognition of inscribed cursive Pashtu numeral through optimized deep learning","volume":"10","author":"Syed","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.2166\/wpt.2023.081","article-title":"Application of coupling machine learning techniques and linear Bias scaling for optimizing 10-daily flow simulations, Swat River Basin","volume":"18","author":"Syed","year":"2023","journal-title":"Water Pract. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"26521","DOI":"10.1109\/ACCESS.2017.2775180","article-title":"Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities","volume":"5","author":"Baker","year":"2017","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.glohj.2019.07.001","article-title":"Smart healthcare: Making medical care more intelligent","volume":"3","author":"Tian","year":"2019","journal-title":"Glob. Health J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MCE.2019.2923929","article-title":"Smart Healthcare in the Era of Internet-of-Things","volume":"8","author":"Zhu","year":"2019","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1504\/IJHTM.2002.001131","article-title":"A vision of the e-healthcare era","volume":"4","author":"Rohm","year":"2002","journal-title":"Int. J. Healthc. Technol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9949","DOI":"10.1007\/s10916-013-9949-0","article-title":"U-Healthcare System: State-of-the-Art Review and Challenges","volume":"37","author":"Touati","year":"2013","journal-title":"J. Med. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1561\/1000000054","article-title":"Smart Healthcare","volume":"12","author":"Yin","year":"2018","journal-title":"Found. Trends\u00ae Electron. Des. Autom."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chiang, T.-A., Chen, P.H., Wu, P.F., Wang, T.N., Chang, P.Y., Ko, A.M.S., Huang, M.S., and Ko, Y.C. (2008). Important prognostic factors for the long-term survival of lung cancer subjects in Taiwan. BMC Cancer, 8.","DOI":"10.1186\/1471-2407-8-324"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Forte, G.C., Altmayer, S., Silva, R.F., Stefani, M.T., Libermann, L.L., Cavion, C.C., Youssef, A., Forghani, R., King, J., and Mohamed, T.L. (2022). Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis. Cancers, 14.","DOI":"10.3390\/cancers14163856"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","article-title":"Artificial intelligence in fracture detection: Transfer learning from deep convolutional neural networks","volume":"73","author":"Kim","year":"2018","journal-title":"Clin. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Ashraf, I., Alhaisoni, M., Dama\u0161evi\u010dius, R., Scherer, R., Rehman, A., and Bukhari, S.A.C. (2020). Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080565"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Azeem, M., Kiani, K., Mansouri, T., and Topping, N. (2023). SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network. Cancers, 16.","DOI":"10.3390\/cancers16010108"},{"key":"ref_15","first-page":"3861161","article-title":"Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans","volume":"22","author":"Abdullah","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","article-title":"Artificial intelligence for analyzing orthopedic trauma radiographs: Deep learning algorithms\u2014Are they on par with humans for diagnosing fractures?","volume":"88","author":"Olczak","year":"2017","journal-title":"Acta Orthop."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.05.011","article-title":"Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans","volume":"98","author":"Tomita","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110358","DOI":"10.1109\/ACCESS.2019.2933670","article-title":"Ensemble Learners of Multiple Deep CNNs for Pulmonary Nodules Classification Using CT Images","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/978-981-13-9282-5_52","article-title":"Computer-Aided Detection and Diagnosis of Diaphyseal Femur Fracture","volume":"Volume 159","author":"Balaji","year":"2020","journal-title":"Smart Intelligent Computing and Applications"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/978-981-10-9035-6_33","article-title":"Brain Tumor Classification Using Convolutional Neural Network","volume":"Volume 68\/1","author":"Abiwinanda","year":"2019","journal-title":"World Congress on Medical Physics and Biomedical Engineering 2018"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/jemt.23224","article-title":"Feature enhancement framework for brain tumor segmentation and classification","volume":"82","author":"Tahir","year":"2019","journal-title":"Microsc. Res. Tech."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1007\/s00261-019-01929-0","article-title":"Deep learning and radiomics: The utility of Google TensorFlowTM Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT","volume":"44","author":"Coy","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_24","first-page":"7","article-title":"Convolutional Neural Network for Diagnosing Skin Cancer","volume":"10","author":"Ottom","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_25","first-page":"45","article-title":"Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network","volume":"13","author":"Zia","year":"2020","journal-title":"Int. J. Hybrid. Inf. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cmpb.2019.02.006","article-title":"Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images","volume":"171","author":"Pranata","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"605","DOI":"10.2214\/AJR.19.22074","article-title":"Differentiation of Small (\u22644 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning","volume":"214","author":"Tanaka","year":"2020","journal-title":"Am. J. Roentgenol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s00371-020-02005-1","article-title":"Brain tumor classification based on hybrid approach","volume":"38","author":"Ayadi","year":"2022","journal-title":"Vis. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012099","DOI":"10.1088\/1757-899X\/1055\/1\/012099","article-title":"Automatic Classification and Accuracy by Deep Learning Using CNN Methods in Lung Chest X-Ray Images","volume":"1055","author":"Thamilarasi","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.clinimag.2021.06.016","article-title":"A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI","volume":"77","author":"Nikpanah","year":"2021","journal-title":"Clin. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, S., and Liu, D. (2021). Automated classification of solitary pulmonary nodules using convolutional neural network based on transfer learning strategy. J. Mech. Med. Biol., 21.","DOI":"10.1142\/S0219519421400029"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kadry, S., Nam, Y., Rauf, H.T., Rajinikanth, V., and Lawal, I.A. (2021, January 25\u201327). Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study. Proceedings of the 2021 Seventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India.","DOI":"10.1109\/ICBSII51839.2021.9445122"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3260","DOI":"10.1007\/s00261-021-02981-5","article-title":"Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: A preliminary study","volume":"46","author":"Zheng","year":"2021","journal-title":"Abdom. Radiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012045","DOI":"10.1088\/1742-6596\/2161\/1\/012045","article-title":"Performance analysis of texture characterization techniques for lung nodule classification","volume":"2161","author":"Kawathekar","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4593","DOI":"10.1007\/s00784-022-04427-8","article-title":"Detection and classification of mandibular fracture on CT scan using deep convolutional neural network","volume":"26","author":"Wang","year":"2022","journal-title":"Clin. Oral Investig."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Agarwal, K., and Singh, T. (2022). Classification of Skin Cancer Images using Convolutional Neural Networks. arXiv.","DOI":"10.2139\/ssrn.4055037"},{"key":"ref_37","first-page":"1","article-title":"Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning","volume":"2","author":"Syed","year":"2023","journal-title":"Int. J. Emerg. Multidiscip. Comput. Sci. Artif. Intell."},{"key":"ref_38","first-page":"200275","article-title":"Skin cancer classification using explainable artificial intelligence on pre-extracted image features","volume":"20","author":"Khater","year":"2023","journal-title":"Intell. Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s13244-023-01601-8","article-title":"Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset","volume":"15","author":"Klontzas","year":"2024","journal-title":"Insights Imaging"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"50205","DOI":"10.1109\/ACCESS.2023.3274848","article-title":"DeepSkin: A Deep Learning Approach for Skin Cancer Classification","volume":"11","author":"Gururaj","year":"2023","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Islam, M.N., Hasan, M., Hossain, M.K., Alam, M.G.R., Uddin, M.Z., and Soylu, A. (2022). Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-15634-4"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e50965","DOI":"10.2196\/50965","article-title":"Comparison of the Performance of GPT-3.5 and GPT-4 with That of Medical Students on the Written German Medical Licensing Examination: Observational Study","volume":"10","author":"Meyer","year":"2024","journal-title":"JMIR Med. Educ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Singh, P., Singh, N., Singh, K.K., and Singh, A. (2021). Diagnosing of Disease Using Machine Learning. Machine Learning and the Internet of Medical Things in Healthcare, Elsevier.","DOI":"10.1016\/B978-0-12-821229-5.00003-3"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hicks, S.A., Str\u00fcmke, I., Thambawita, V., Hammou, M., Riegler, M.A., Halvorsen, P., and Parasa, S. (2022). On evaluation metrics for medical applications of artificial intelligence. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-09954-8"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/12\/322\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:54:54Z","timestamp":1760115294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/12\/322"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["jimaging10120322"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10120322","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]}}}