Artificial Intelligence in Healthcare A Review of Machine Learning Applications
Answer: AI in medicine. AI in medicine has been a massive advance in diagnostics, predictive analytics, and patient care. Despite its potential, however, there are significant barriers to widespread adoption, such as data privacy issues, high computational costs, AI bias, lack of standardized evalua...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
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| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_01012.pdf |
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| author | Karajgi Santosh R Vijaya Prakash Kumar K. Gagan Selvi P Tamil Shankar Bhukya S Jeyanthi |
| author_facet | Karajgi Santosh R Vijaya Prakash Kumar K. Gagan Selvi P Tamil Shankar Bhukya S Jeyanthi |
| author_sort | Karajgi Santosh |
| collection | DOAJ |
| description | Answer: AI in medicine. AI in medicine has been a massive advance in diagnostics, predictive analytics, and patient care. Despite its potential, however, there are significant barriers to widespread adoption, such as data privacy issues, high computational costs, AI bias, lack of standardized evaluation, regulatory barriers, and integration with legacy healthcare systems. At present, the challenges explored highlight the need for federated learning as a new way to train AI without exposing sensitive patient data, bias-aware models which promote equitable and fair healthcare decisions for all patients, cloud and edge AI to ensure that processing is cost effective and appropriate, and Explainable AI (XAI) to promote trust and transparency to patients and communities. Additionally, we introduce an AI middleware framework, developed to integrate AI into existing Electronic Health Records (EHRs), enabling seamless uptake into clinical arenas. Summary: To enable privacy-preserving, fair, efficient, and regulatory-compliant AI and accelerate AI-driven innovations in the healthcare domain this research will develop an AI benchmarking framework where the progress of AI will be monitored and regulated. This will pave the way for scalable, interpretable, and sustainable AI applications that can close the gap between the existing theoretical AI models and their use in real-world clinical settings. |
| format | Article |
| id | doaj-art-917e7ec3f0724c29a517bf110f722333 |
| institution | DOAJ |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-917e7ec3f0724c29a517bf110f7223332025-08-20T03:04:30ZengEDP SciencesITM Web of Conferences2271-20972025-01-01760101210.1051/itmconf/20257601012itmconf_icsice2025_01012Artificial Intelligence in Healthcare A Review of Machine Learning ApplicationsKarajgi Santosh0R Vijaya Prakash1Kumar K. Gagan2Selvi P Tamil3Shankar Bhukya4S Jeyanthi5Professor and Head, Department of Pharmaceutical Quality Assurance, BLDEA's SSM College of Pharmacy and Research Centre VijayapurProfessor, School of Computer Science and Artificial Intelligence, SR UniversityDepartment of Computer Science and Engineering, MLR Institute of TechnologyAssistant Professor, Department of ECE, Sree Sakthi Engineering CollegeSenior Assistant Professor, Department of Electronics and Communication Engineering, CVR College of EngineeringAssistant Professor, Department of Computer Applications, New Prince Shri Bhavani College of Engineering and TechnologyAnswer: AI in medicine. AI in medicine has been a massive advance in diagnostics, predictive analytics, and patient care. Despite its potential, however, there are significant barriers to widespread adoption, such as data privacy issues, high computational costs, AI bias, lack of standardized evaluation, regulatory barriers, and integration with legacy healthcare systems. At present, the challenges explored highlight the need for federated learning as a new way to train AI without exposing sensitive patient data, bias-aware models which promote equitable and fair healthcare decisions for all patients, cloud and edge AI to ensure that processing is cost effective and appropriate, and Explainable AI (XAI) to promote trust and transparency to patients and communities. Additionally, we introduce an AI middleware framework, developed to integrate AI into existing Electronic Health Records (EHRs), enabling seamless uptake into clinical arenas. Summary: To enable privacy-preserving, fair, efficient, and regulatory-compliant AI and accelerate AI-driven innovations in the healthcare domain this research will develop an AI benchmarking framework where the progress of AI will be monitored and regulated. This will pave the way for scalable, interpretable, and sustainable AI applications that can close the gap between the existing theoretical AI models and their use in real-world clinical settings.https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_01012.pdfartificial intelligence in healthcaremachine learning in medicineai-driven diagnosticsexplainable ai (xai)federated learningbias-aware ai models |
| spellingShingle | Karajgi Santosh R Vijaya Prakash Kumar K. Gagan Selvi P Tamil Shankar Bhukya S Jeyanthi Artificial Intelligence in Healthcare A Review of Machine Learning Applications ITM Web of Conferences artificial intelligence in healthcare machine learning in medicine ai-driven diagnostics explainable ai (xai) federated learning bias-aware ai models |
| title | Artificial Intelligence in Healthcare A Review of Machine Learning Applications |
| title_full | Artificial Intelligence in Healthcare A Review of Machine Learning Applications |
| title_fullStr | Artificial Intelligence in Healthcare A Review of Machine Learning Applications |
| title_full_unstemmed | Artificial Intelligence in Healthcare A Review of Machine Learning Applications |
| title_short | Artificial Intelligence in Healthcare A Review of Machine Learning Applications |
| title_sort | artificial intelligence in healthcare a review of machine learning applications |
| topic | artificial intelligence in healthcare machine learning in medicine ai-driven diagnostics explainable ai (xai) federated learning bias-aware ai models |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_01012.pdf |
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