Abstract
This paper explores the critical role of Interoperability (IOP) in the integration of Artificial Intelligence (AI) for clinical applications. As AI gains prominence in medical analytics, its application in clinical practice faces challenges due to the lack of standardization in the medical sector. IOP, the ability of systems to exchange information seamlessly, emerges as a fundamental solution. Our paper discusses the indispensable nature of IOP throughout the Data Life Cycle, demonstrating how interoperable data can facilitate AI applications. The benefits of IOP encompass streamlined data entry for healthcare professionals, efficient data processing, enabling the sharing of data and algorithms for replication, and potentially increasing the significance of results obtained by medical data analytics via AI. Despite the challenges of IOP, its successful implementation promises substantial benefits for integrating AI into clinical practice, which could ultimately enhance patient outcomes and healthcare quality.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
