International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI for Healthcare: How Can Hospitals Share Data to Build Better Models? |
|---|---|
| ABSTRACT | The study has explored the importance of sharing data in hospitals for training Artificial Intelligence (AI) models. It has been observed that sharing data in the model training faces challenges such as ethical and privacy concerns, and the revelation of sensitive data of the patients. AI has been used in the detection of skin diseases and in the prediction of cancer from X-Ray images. However, limited data within a single hospital poses challenges and raises data privacy concerns. Investigation on this topic was found to be effective for improving the patient survival and recovery rates. The study has outlined several techniques to address the challenge of data privacy concerns while sharing data for AI training. This includes Federated Learning techniques, Homomorphic encryption, synthetic data generation and differential privacy. |
| TITLE | |
| AUTHOR | SEJAL GUPTA Student, United World Academy, Bengaluru, Karnataka, India |
| VOLUME | 187 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1408009 |
| pdf/9_AI for Healthcare How Can Hospitals Share Data to Build Better.pdf | |
| KEYWORDS | |
| References | 1. Abdel-Jaber, H., Devassy, D., Al Salam, A., Hidaytallah, L. and El-Amir, M., (2022). A review of deep learning algorithms and their applications in healthcare. Algorithms, 15(2), p.71. Available at: https://www.mdpi.com/1999-4893/15/2/71 2. Alanazi, A., (2023). Clinicians’ views on using artificial intelligence in healthcare: opportunities, challenges, and beyond. Cureus, 15(9). Available at: https://pubmed.ncbi.nlm.nih.gov/37842420/ 3. Ashraf, M.H., Saeed, N., Cullinane, K. and Dresner, M., (2026). Enhancing resilience and sustainability: an inductive approach to innovations in cruise industry. Transportation Research Part D: Transport and Environment, 150, p.105118. https://www.sciencedirect.com/science/article/pii/S1361920925005280 4. Baklola, M., Elmahdi, R.R., Ali, S., Elshenawy, M., Mossad, A.M., Al-Bawah, N. and Mansour, R.M., (2025). Artificial intelligence in disease diagnostics: a comprehensive narrative review of current advances, applications, and future challenges in healthcare. Annals of Medicine and Surgery, 87(7), pp.4237-4245. Available at :https://pubmed.ncbi.nlm.nih.gov/40851938/ 5. Banks K. (2026) My breast cancer was too small to be detected by the human eye - it was found by AI. Available at: https://www.bbc.com/news/articles/cjd9gn4j7dyo [Accessed on: 13th May 2026] 6. Batool, A., Zowghi, D. and Bano, M., (2025). AI governance: a systematic literature review. AI and Ethics, 5(3), pp.3265-3279. Available at: https://link.springer.com/content/pdf/10.1007/s43681-024-00653-w.pdf 7. de Vries, C.F., Colosimo, S.J., Staff, R.T., Dymiter, J.A., Yearsley, J., Dinneen, D., Boyle, M., Harrison, D.J., Anderson, L.A., Lip, G. and iCAIRD Radiology Collaboration, (2023). Impact of different mammography systems on artificial intelligence performance in breast cancer screening. Radiology: Artificial Intelligence, 5(3), pp.e220146. Available at: https://pubmed.ncbi.nlm.nih.gov/37293340/ 8. De Vries, C.F., Lip, G., Staff, R.T., Dymiter, J.A., Tse, B., Ng, A., Fox, G., Oberije, C. and Anderson, L.A., (2026). Prospective evaluation of artificial intelligence integration into breast cancer screening in multiple workflow settings: the GEMINI study. Nature Cancer, pp.1-10. Available at: https://www.nature.com/articles/s43018-026-01126-1 9. Devi, K.J., Alghamdi, W., N, D., Alkhayyat, A., Sayyora, A. and Sathish, T., (2023). Artificial intelligence in healthcare: diagnosis, treatment, and prediction. In E3S web of conferences (Vol. 399, p. 04043). EDP Sciences. Available at: https://www.researchgate.net/publication/372338887_Artificial_Intelligence_in_Healthcare_Diagnosis_Treatment_and_Prediction 10. Enshaei, N. and Naderkhani, F., (2024). The role of data quality for reliable AI performance in medical applications. IEEE Reliability Magazine, 1(3), pp.24-28. Available at: https://colab.ws/articles/10.1109/mrl.2024.3430192 11. Google Cloud (2026) kakao healthcare logo. Available at: https://cloud.google.com/customers/kakao-healthcare-federated-learning [Accessed on: 13th May 2026] 12. Heyndrickx, W., Mervin, L., Morawietz, T., Sturm, N., Friedrich, L., Zalewski, A., Pentina, A., Humbeck, L., Oldenhof, M., Niwayama, R. and Schmidtke, P., (2023). MELLODDY: cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information. Journal of chemical information and modeling, 64(7), pp.2331-2344. Available at: https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00799 13. Hong, S., Park, J.H., Cho, W., Choe, H. and Cheon, J.H., (2022). Secure tumor classification by shallow neural network using homomorphic encryption. BMC genomics, 23(1), p.284. Available at: https://link.springer.com/content/pdf/10.1186/s12864-022-08469-w.pdf 14. Karalis, V.D., (2024). The integration of artificial intelligence into clinical practice. Applied Biosciences, 3(1), pp.14-44. Available at: https://www.mdpi.com/2813-0464/3/1/2 15. Karmitsa, N., Airola, A., Pahikkala, T. and Pitkämäki, T., (2025). A comprehensive guide to differential privacy: From theory to user expectations. arXiv preprint arXiv:2509.03294. Available at: https://www.researchgate.net/publication/395244318_A_Comprehensive_Guide_to_Differential_Privacy_From_Theory_to_User_Expectations 16. Keith Blankenship B. (2025) Regulations and considerations needed to legally use patient data for artificial intelligence (AI) and to align with data protection laws in U.S. and internationally. Available at: https://www.oralhealthgroup.com/features/regulations-and-considerations-needed-to-legally-use-patient-data-for-artificial-intelligence-ai-and-to-align-with-data-protection-laws-in-u-s-and-internationally/ [Accessed on: 13th May 2026] 17. Klumpp, M., Hintze, M., Immonen, M., Ródenas-Rigla, F., Pilati, F., Aparicio-Martínez, F., Çelebi, D., Liebig, T., Jirstrand, M., Urbann, O. and Hedman, M. (2021). Artificial intelligence for hospital health care: application cases and answers to challenges in European hospitals. In Healthcare (Vol. 9, No. 8, p. 961). MDPI. Available at: https://pubmed.ncbi.nlm.nih.gov/34442098/ 18. LeewayHertz (2026) Comprehensive Guide to Synthetic Data: Types, generation, evaluation, use cases and applications. Available at: https://www.leewayhertz.com/what-is-synthetic-data/#Synthetic-data-generation-process [Accessed on: 13th May 2026] 19. Li, Y.H., Li, Y.L., Wei, M.Y. and Li, G.Y., (2024). Innovation and challenges of artificial intelligence technology in personalized healthcare. Scientific reports, 14(1), p.18994. Available at: https://www.nature.com/articles/s41598-024-70073-7 20. Lim, W.M., (2025). What is qualitative research? An overview and guidelines. Australasian marketing journal, 33(2), pp.199-229. https://journals.sagepub.com/doi/10.1177/14413582241264619 21. Mammen, P.M., (2021). Federated learning: Opportunities and challenges. arXiv preprint arXiv:2101.05428. Available at: https://arxiv.org/abs/2101.05428 22. Manchester Digital (2026) Cryptographic Techniques used in Securing AI Datasets. Available at: https://www.manchesterdigital.com/post/ve3/cryptographic-techniques-used-in-securing-ai-datasets [Accessed on: 17th May 2026] 23. Melloddy (2019) New Research Consortium Seeks to Accelerate Drug Discovery Using Machine Learning to Unlock Maximum Potential of Pharma Industry Data. Available at: https://www.melloddy.eu/new-research-consortium [Accessed on: 13th May 2026] 24. Mijwil, M.M., Faieq, A.K. and Al-Mistarehi, A.H., 2022. The Significance of digitalisation and artificial intelligence in the healthcare sector: A review. Significance, 10(3), pp.25-32. Available at: https://www.researchgate.net/profile/Maad-Mijwil/publication/365114420_The_Significance_of_Digitalisation_and_Artificial_Intelligence_in_The_Healthcare_Sector_A_Review/links/636514ef2f4bca7fd03023f8/The-Significance-of-Digitalisation-and-Artificial-Intelligence-in-The-Healthcare-Sector-A-Review.pdf 25. News Medical Life Sciences (2025) AI rollout in NHS hospitals faces major challenges. Available at: https://www.news-medical.net/news/20250910/AI-rollout-in-NHS-hospitals-faces-major-challenges.aspx [Accessed on: 13th May 2026] 26. Norori, N., Hu, Q., Aellen, F.M., Faraci, F.D. and Tzovara, A., (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(10). Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8515002/ 27. Ogugua, J.O., Onwumere, C., Arowoogun, J.O., Anyanwu, E.C., Odilibe, I.P. and Akomolafe, O., (2024). Data science in public health: A review of predictive analytics for disease control in the USA and Africa. World Journal of Advanced Research and Reviews, 21(1), pp.2753-2769. Available at: https://pdfs.semanticscholar.org/6375/bb6feec312250cadf2243eb6d0b0c7259402.pdf 28. Prescience (2025) The Data Problem in Generative AI: Key Challenges and How to Overcome Them. Available at: https://prescienceds.com/the-data-problem-in-generative-ai-key-challenges-and-how-to-overcome-them/ [Accessed on: 13th May 2026] 29. Qian, Z., Callender, T., Cebere, B., Janes, S.M., Navani, N. and van der Schaar, M., (2024). Synthetic data for privacy-preserving clinical risk prediction. Scientific Reports, 14(1), p.25676. Available at: https://www.nature.com/articles/s41598-024-72894-y 30. Reddy, S., Allan, S., Coghlan, S. and Cooper, P., (2020). A governance model for the application of AI in health care. Journal of the American medical informatics association, 27(3), pp.491-497. Available at: https://pubmed.ncbi.nlm.nih.gov/31682262/ 31. Siniosoglou, I., Bibi, S., Kollias, K.F., Fragulis, G., Radoglou-Grammatikis, P., Lagkas, T., Argyriou, V., Vitsas, V. and Sarigiannidis, P., (2024). Federated learning models in decentralized critical infrastructure. Shaping the Future of IoT with Edge Intelligence, pp.95-115. Available at: https://pubmed.ncbi.nlm.nih.gov/38564569/ 32. Sun, M., (2024). Llamacare: A large medical language model for enhancing healthcare knowledge sharing. arXiv preprint arXiv:2406.02350. Available at: https://arxiv.org/abs/2406.02350 33. Thomason, J., (2024). Data, digital worlds, and the avatarization of health care. Global Health Journal, 8(1), pp.1-3. Available at: https://www.sciencedirect.com/science/article/pii/S2414644724000034 34. Wang, L., (2024). Mammography with deep learning for breast cancer detection. Frontiers in oncology, 14, p.1281922. Available at: https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1281922/full 35. William, F.K.A., (2024). Interpretivism or constructivism: Navigating research paradigms in social science research. International Journal of Research Publications, 143(1), pp.134-138. https://www.researchgate.net/publication/378396560_Interpretivism_or_Constructivism_Navigating_Research_Paradigms_in_Social_Science_Research 36. Youssef, A., Ng, M.Y., Long, J., Hernandez-Boussard, T., Shah, N., Miner, A., Larson, D. and Langlotz, C.P., (2023). Organizational factors in clinical data sharing for artificial intelligence in health care. JAMA network open, 6(12), p.e2348422. Available at: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2813016 |
