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The integration of artificial intelligence (AI) in the field of health care especially in medical imaging and cancer care has immensely contributed to diagnosis, treatment, and clinical decision-making. It is crucial to have clinical validation to ensure their safety, reliability, and effectiveness for widespread adoption. This paper describes the clinical validation within the AI for Health Imaging (AI4HI) network, a collaboration of EU-funded projects focused on enhancing cancer care through AI-driven innovations. It also addresses the importance of rigorous validation processes and frameworks adopted like FUTURE-AI. Dealing with interoperability and standardisation, ethical and legal compliance, generalisability, and robustness were some of the factors considered as challenges in clinical validation processes for AI4HI Projects. The best practices and lessons learned from various AI4HI projects remain valuable insights to guide future work on AI integration in healthcare, guiding as a benchmark to enhance better outcomes.

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Clinical Validation in AI for Healthcare: An Experiential Learning

  • 章节

  • 发表日期：

    2025年

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  • Shereen Nabhani-Gebara,
  • Lithin Zacharias,
  • Luis Marti Bonmati,
  • Adrian Galiana-Bordera,
  • Sara Colantonio,
  • Ioanna Chouvarda,
  • Ghada Elkhoury

摘要

The integration of artificial intelligence (AI) in the field of health care especially in medical imaging and cancer care has immensely contributed to diagnosis, treatment, and clinical decision-making. It is crucial to have clinical validation to ensure their safety, reliability, and effectiveness for widespread adoption. This paper describes the clinical validation within the AI for Health Imaging (AI4HI) network, a collaboration of EU-funded projects focused on enhancing cancer care through AI-driven innovations. It also addresses the importance of rigorous validation processes and frameworks adopted like FUTURE-AI. Dealing with interoperability and standardisation, ethical and legal compliance, generalisability, and robustness were some of the factors considered as challenges in clinical validation processes for AI4HI Projects. The best practices and lessons learned from various AI4HI projects remain valuable insights to guide future work on AI integration in healthcare, guiding as a benchmark to enhance better outcomes.