AI IN DISEASE DIAGNOSIS IN MACHINE LEARNING AND DEEP LEARNING AND THEIR APPLICATION AND CHALLENGES
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2026
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| author | Mr. M.I. Yasar Ihdisham1*, Mr. R. Jagadesh1, Mr. R. Dhanush2, Mr. M. Premkumar2, Mr. M. Praveenkumar3, Mr. C. Jothimanivannan3 |
| author_facet | Mr. M.I. Yasar Ihdisham1*, Mr. R. Jagadesh1, Mr. R. Dhanush2, Mr. M. Premkumar2, Mr. M. Praveenkumar3, Mr. C. Jothimanivannan3 |
| contents | <p><span>Artificial Intelligence (AI) has emerged as a revolutionary technology in the current healthcare setting<span> </span>that<span> </span>allows<span> </span>for<span> </span>efficient<span> </span>disease<span> </span>diagnosis,<span> </span>prediction,<span> </span>and<span> </span>patient<span> </span>management.<span> </span>This<span> </span>review article offers<span> </span>an<span> </span>extensive review of<span> </span>AI-based approaches like Machine Learning (ML) and Deep Learning (DL) for the diagnosis of significant diseases such as cancer, diabetes, cardiovascular diseases, neurological disorders, and infectious diseases. The article reviews research articles published<span> </span>on<span> </span>significant<span> </span>scientific<span> </span>portals<span> </span>that<span> </span>strictly<span> </span>adhere<span> </span>to<span> </span>the<span> </span>PRISMA<span> </span>protocol<span> </span>that<span> </span>focuses on image data, Electronic Health Records (EHER), genomics information, and wearable sensor information.<span> </span>Other<span> </span>performance<span> </span>parameters<span> </span>like<span> </span>accuracy,<span> </span>sensitivity,<span> </span>specificity, Area<span> </span>Under<span> </span>the Curve (AUC), precision, recall, and F1 measure have been<span> </span>discussed for an<span> </span>understanding of<span> </span>the effectiveness of the approach. The article also focuses on the use of AI-based smart healthcare solutions and Internet of Things (IoT)-related devices that monitor diseases on a real-time basis. Nonetheless, despite the significant progress that AI has shown, issues of data privacy and the scarcity of data pose challenges.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18813159 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI IN DISEASE DIAGNOSIS IN MACHINE LEARNING AND DEEP LEARNING AND THEIR APPLICATION AND CHALLENGES Mr. M.I. Yasar Ihdisham1*, Mr. R. Jagadesh1, Mr. R. Dhanush2, Mr. M. Premkumar2, Mr. M. Praveenkumar3, Mr. C. Jothimanivannan3 <p><span>Artificial Intelligence (AI) has emerged as a revolutionary technology in the current healthcare setting<span> </span>that<span> </span>allows<span> </span>for<span> </span>efficient<span> </span>disease<span> </span>diagnosis,<span> </span>prediction,<span> </span>and<span> </span>patient<span> </span>management.<span> </span>This<span> </span>review article offers<span> </span>an<span> </span>extensive review of<span> </span>AI-based approaches like Machine Learning (ML) and Deep Learning (DL) for the diagnosis of significant diseases such as cancer, diabetes, cardiovascular diseases, neurological disorders, and infectious diseases. The article reviews research articles published<span> </span>on<span> </span>significant<span> </span>scientific<span> </span>portals<span> </span>that<span> </span>strictly<span> </span>adhere<span> </span>to<span> </span>the<span> </span>PRISMA<span> </span>protocol<span> </span>that<span> </span>focuses on image data, Electronic Health Records (EHER), genomics information, and wearable sensor information.<span> </span>Other<span> </span>performance<span> </span>parameters<span> </span>like<span> </span>accuracy,<span> </span>sensitivity,<span> </span>specificity, Area<span> </span>Under<span> </span>the Curve (AUC), precision, recall, and F1 measure have been<span> </span>discussed for an<span> </span>understanding of<span> </span>the effectiveness of the approach. The article also focuses on the use of AI-based smart healthcare solutions and Internet of Things (IoT)-related devices that monitor diseases on a real-time basis. Nonetheless, despite the significant progress that AI has shown, issues of data privacy and the scarcity of data pose challenges.</span></p> |
| title | AI IN DISEASE DIAGNOSIS IN MACHINE LEARNING AND DEEP LEARNING AND THEIR APPLICATION AND CHALLENGES |
| url | https://doi.org/10.5281/zenodo.18813159 |