Downloads: 1 | Views: 115 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1
Analysis Study Research Paper | Information Technology | United States of America | Volume 11 Issue 1, January 2022 | Rating: 4.6 / 10
Data Engineering Challenges in AI for Healthcare
Nithin Reddy Desani [5] | Srujan Reddy Jabbi Reddy
Abstract: Artificial Intelligence (AI) is revolutionizing the healthcare industry by enabling advanced diagnostics, personalized treatment, and efficient operational workflows. The integration of AI in healthcare promises to enhance patient outcomes, streamline clinical processes, and reduce costs. However, the successful implementation of AI in healthcare presents significant data engineering challenges. This paper explores the critical data engineering issues in AI for healthcare, including data heterogeneity, data privacy and security, data quality, and data integration. Additionally, it addresses the complexities of handling large - scale datasets, the need for real - time data processing, and the importance of interoperability between different healthcare systems. Addressing these challenges is essential to harness the full potential of AI in healthcare, ensuring accurate, reliable, and ethical AI - driven solutions. This comprehensive exploration provides insights into the current state of AI in healthcare, highlights key obstacles, and proposes strategies to overcome these barriers, paving the way for a future where AI can be seamlessly integrated into healthcare practices.
Keywords: AI in healthcare, advanced diagnostics, data engineering, patient outcomes, data privacy
Edition: Volume 11 Issue 1, January 2022,
Pages: 1647 - 1652