AI in Healthcare: Unlocking the Potential of Data-Driven Medicine
DOI:
https://doi.org/10.56472/ICCSAIML25-101Keywords:
AI, Healthcare, Machine Learning, Deep Learning, Predictive Analytics, Data-Driven Medicine, Personalized Medicine, Clinical Decision Support Systems, Electronic Health Records, Medical Imaging, Precision Medicine, Drug Discovery, AI-Powered Virtual Assistants, Telemedicine, Wearable Devices, Predictive Healthcare, Data Privacy, Bias, Fairness, Regulatory Challenges, Healthcare Infrastructure, Patient Outcomes, Healthcare InnovationAbstract
Through leveraging data-driven medicine, improving clinical decision-making, and increasing patient outcomes, artificial intelligence (AI) is the one revolutionizing healthcare today. The giant accumulation of medical data serves as a tool for artificial intelligence systems to analyze large datasets, identify trends, forecast health issues, and offer treatment options that are personalized. Natural language processing, machine learning methodologies, and computer vision are applied significantly to boost diagnosis, make administrative tasks more effective, and support healthcare that provides exact therapy. AI technology shows the way to the point by which, through precisely pinpointing abnormalities, medical imaging is being revolutionized and radiologists are able to swiftly detect diseases including cancer and neurological disorders. In addition, through the use of artificial intelligence scientists can do the faster discovery of the right chemicals than in the old-style of research in which they were setting up expensive equipment. AI-based chatbots and virtual assistants connect patients with doctors, offer medical advice, and improve distance care services in telemedicine. A field in which artificial intelligence (AI) is a significant player is predictive analytics for early identification of the disease, optimization of hospital resources allocation, and reduction of emergency admissions to hospitals. Wearable devices and remote monitoring systems, which enable real-time data collection, help to enhance AI's potentials and provide healthcare professionals with monitoring the patients. AI in healthcare systems is only possible if the major hurdles such as data security, algorithmic bias, and transparent decision-making are done away with. Ensuring that the use of artificial intelligence fully respects patients’ safety and privacy where hospital practitioners, data scientists, and regulatory bodies cooperate is the key. A healthcare system combining human expertise with the AI-generated insights might be the next best way to make mankind happy, by providing more accurate and cheaper therapy, thus it would transform medical practice and world health outcomes
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References
[1] Dutta, A. (2023). Unlocking the power of artificial intelligence: Revolutionising clinical medicine for a healthier future. Journal of Medical Evidence, 4(3), 271-273.
[2] Rasool, S., Ali, M., Hussain, H. K., & Gill, A. Y. (2023). Unlocking the potential of healthcare: AI-driven development and delivery of vaccines. International Journal of Social, Humanities and Life Sciences, 1(1), 29-37.
[3] Majeed, A., & Hwang, S. O. (2021). Data-driven analytics leveraging artificial intelligence in the era of COVID-19: an insightful review of recent developments. Symmetry, 14(1), 16.
[4] Akyüz, K., Cano Abadía, M., Goisauf, M., & Mayrhofer, M. T. (2024). Unlocking the potential of big data and AI in medicine: insights from biobanking. Frontiers in Medicine, 11, 1336588.
[5] Kaur, P., Mack, A. A., Patel, N., Pal, A., Singh, R., Michaud, A., & Mulflur, M. (2023). Unlocking the potential of artificial intelligence (AI) for healthcare. In Artificial Intelligence in Medicine and Surgery-An Exploration of Current Trends, Potential Opportunities, and Evolving Threats-Volume 1. IntechOpen.
[6] Sherani, A. M. K., Khan, M., Qayyum, M. U., & Hussain, H. K. (2024). Synergizing AI and Healthcare: Pioneering advances in cancer medicine for personalized treatment. International Journal of Multidisciplinary Sciences and Arts, 3(2), 270-277.
[7] Arunkumar Paramasivan. (2020). Big Data to Better Care: The Role of AI in Predictive Modelling for Healthcare Management. International Journal of Innovative Research and Creative Technology, 6(3), 1–9. https://doi.org/10.5281/zenodo.14551652
[8] Madhavram, C., Boddapati, V. N., Galla, E. P., Sunkara, J. R., & Patra, G. K. (2023). AI-Powered Insights: Leveraging Machine Learning And Big Data For Advanced Genomic Research In Healthcare. Available at SSRN 5029402.
[9] Galla, E. P., Boddapati, V. N., Patra, G. K., Madhavaram, C. R., & Sunkara, J. (2023). AI-Powered Insights: Leveraging Machine Learning And Big Data For Advanced Genomic Research In Healthcare. Educational Administration: Theory and Practice.
[10] Fatima, G., Siddiqui, Z., & Parvez, S. (2024). AI and precision medicine: paving the way for future treatment.
[11] Pietronudo, M. C., Zhou, F., Caporuscio, A., La Ragione, G., & Risitano, M. (2022). New emerging capabilities for managing data-driven innovation in healthcare: the role of digital platforms. European Journal of Innovation Management, 25(6), 867-891.
[12] Alhajahjeh, A., & Nazha, A. (2024). Unlocking the potential of artificial intelligence in acute myeloid leukemia and myelodysplastic syndromes. Current Hematologic Malignancy Reports, 19(1), 9-17.
[13] Rahman, M. A., Moayedikia, A., & Wiil, U. K. (2023). Data-driven technologies for future healthcare systems. Frontiers in Medical Technology, 5, 1183687.
[14] R. Daruvuri, K. K. Patibandla, and P. Mannem, “Data Driven Retail Price Optimization Using XGBoost and Predictive Modeling”, in Proc. 2025 International Conference on Intelligent Computing and Control Systems (ICICCS), Chennai, India. 2025, pp. 838–843.
[15] Katal, N. (2024). Ai-driven healthcare services and infrastructure in smart cities. In Smart Cities (pp. 150-170). CRC Press.
[16] Parvin, K., & Mustafa, K. (2023). Cloud Computing and AI in Healthcare: Revolutionizing Predictive Analytics and Biomedical Research with Machine Learning.
[17] Nilius, H., Tsouka, S., Nagler, M., & Masoodi, M. (2024). Machine learning applications in precision medicine: overcoming challenges and unlocking potential. TrAC Trends in Analytical Chemistry, 117872.
[18] Arunkumar Paramasivan. (2023). Transforming Healthcare Supply Chains: AI for Efficient Drug Distribution and Inventory Management. International Journal on Science and Technology, 14(3), 1–15. https://doi.org/10.5281/zenodo.14551612
[19] Nirali Shah (2024). Validation and Verification of Artificial Intelligence Containing Products Across the Regulated Healthcare or Medical Device Industries, International Journal of Science and Research (IJSR), 13 (7), 66-71.
