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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Health Data Management

Background:

  • Artificial Intelligence (AI) is increasingly used in medical analytics.
  • Clinical applications of AI are hindered by a lack of standardization in the medical sector.
  • Interoperability (IOP) is essential for seamless data exchange between systems.

Purpose of the Study:

  • To explore the critical role of Interoperability (IOP) in integrating Artificial Intelligence (AI) for clinical applications.
  • To demonstrate how interoperable data facilitates AI applications throughout the Data Life Cycle.
  • To highlight the benefits and challenges of IOP implementation in clinical AI integration.

Main Methods:

  • Literature review and conceptual analysis of Interoperability (IOP) and Artificial Intelligence (AI) in clinical settings.
  • Discussion of the Data Life Cycle in relation to AI and IOP.
  • Examination of benefits and challenges associated with IOP implementation.

Main Results:

  • Interoperability (IOP) is a fundamental solution to standardization challenges in clinical AI integration.
  • Interoperable data streamlines data entry, enhances data processing, and facilitates algorithm sharing for AI applications.
  • IOP can increase the significance of results from AI-driven medical data analytics.

Conclusions:

  • Successful implementation of Interoperability (IOP) is indispensable for integrating AI into clinical practice.
  • IOP adoption promises substantial benefits, including improved patient outcomes and enhanced healthcare quality.
  • Addressing IOP challenges is key to unlocking the full potential of AI in healthcare.