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Explainable Artificial Intelligence in Paediatric: Challenges for the Future.

Ahmed M Salih1,2,3,4, Gloria Menegaz5, Thillagavathie Pillay6

  • 1Department of Population Health Sciences University of Leicester Leicester UK.

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Summary
This summary is machine-generated.

Explainable artificial intelligence (XAI) offers transparency in machine learning but requires careful use in pediatrics. Challenges like generalizability and trustworthiness must be addressed by involving pediatricians and domain knowledge for safe adoption.

Keywords:
challengesexplainable artificial intelligenceinterpretationpaediatrics

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Pediatrics

Background:

  • Explainable Artificial Intelligence (XAI) aims to enhance machine learning model transparency and human understanding of AI decisions.
  • XAI simplifies complex models for better human comprehension.
  • Current XAI development requires cautious application in sensitive fields like pediatrics due to potential adverse outcomes.

Purpose of the Study:

  • To discuss concerns and challenges in implementing and interpreting XAI methods in pediatrics.
  • To raise awareness of key issues hindering XAI adoption in pediatric care.

Main Methods:

  • A comprehensive literature review was conducted.
  • The review focused on exploring the challenges associated with adopting XAI within the pediatric domain.

Main Results:

  • XAI implementation in pediatrics faces significant challenges, including issues with generalizability, trustworthiness, causality, and intervention.
  • Evaluating XAI methods is a critical hurdle.

Conclusions:

  • Pediatrics is a high-stakes field where AI misinterpretation can have severe consequences.
  • XAI adoption in pediatrics necessitates careful evaluation, pediatrician involvement, and integration of domain knowledge to bridge existing gaps.