Paper Contents
Abstract
The integration of artificial intelligence (AI) into healthcare is poised to transform the industry by enhancing diagnostics, patient care, disease forecasting, and drug development. This review explores the broad applications of AI in healthcare, emphasizing its role in improving accuracy and efficiency within clinical practices. In diagnostics, AI technologies, particularly deep learning models, have demonstrated significant potential in analyzing medical images such as X-rays and MRIs, enabling the early detection of various conditions. The paper provides an in-depth review of AI methodologies, including supervised learning, deep learning, and reinforcement learning, along with their applications in health-related fields. AI's capability to process extensive datasets of patient histories allows for the prediction of diseases like diabetes, heart disease, and cancer, facilitating timely intervention. Additionally, the review highlights AI's impact on patient management by optimizing administrative tasks and enabling personalized treatment strategies. In drug discovery, AI accelerates the process by identifying promising drug candidates and predicting molecular interactions with greater speed and precision than conventional methods. The discussion also addresses critical challenges, such as data privacy, the transparency of AI models, and the necessity for interdisciplinary collaboration in healthcare. The paper concludes by exploring the future of AI in healthcare, underscoring the importance of ongoing research to address current limitations and promote the ethical integration of AI technologies environments.
Copyright
Copyright © 2024 Gignesh. This is an open access article distributed under the Creative Commons Attribution License.
