Authors : Prathamesh Gujjeti; Anjali Pal

Volume/Issue : Volume 9 - 2024, Issue 5 - May

---

Google Scholar : https://shorturl.at/uVPCq

Scribd : https://shorturl.at/LzrOX

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1593

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Artificial Intelligence (AI) and Deep Learning (DL) are revolutionizing the landscape of medical research, offering unprecedented advancements in diagnostics, personalized treatments, and medical data management. This paper delves into the diverse applications of AI and DL within the medical field, highlighting their transformative roles in imaging, genomics, drug discovery, and clinical decision-making. Moreover, it addresses the challenges and ethical considerations inherent in these technologies, and proposes future pathways for their seamless integration into healthcare systems. Through this exploration, we aim to provide a comprehensive overview of how AI and DL are shaping the future of medicine and improvingpatient outcomes.

Keywords : Revolutionary AI in Healthcare, Advanced DL Applications, Precision Medicine Innovations, AI-Driven Medical Imaging, Ethical AI in Medicine

References :

1. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular Pharmaceutics, 13(7), 2524-2530.
2. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
3. Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., ... & Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158-164.
4. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
5. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.
6. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Doyle, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.
7. Siva pragasam, M., & Dhanalakshmi, R. (2019). Deep learning techniques for healthcare image analysis. Handbook of Research on Machine Learning Innovations and Trends, 97-121.