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Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.

Lara J Kanbar1, Benjamin Wissel2, Yizhao Ni2,3

  • 1Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.

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|December 16, 2022
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Summary
This summary is machine-generated.

Artificial intelligence (AI) in healthcare shows promise for analyzing complex data. Successful clinical integration requires collaboration between AI experts, IT professionals, clinicians, and leadership to improve patient care.

Keywords:
artificial intelligenceclinical decision supportelectronic health recordemergency medicineepilepsymachine learningnatural language processing

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Area of Science:

  • Clinical informatics
  • Artificial intelligence in medicine
  • Health data analysis

Background:

  • AI technologies like machine learning offer potential for novel health data insights.
  • Clinical implementation of AI algorithms remains limited despite their power.

Purpose of the Study:

  • To outline essential elements for developing and integrating AI-driven research pipelines into clinical practice.
  • To describe the successful implementation of two AI systems in a pediatric hospital setting.

Main Methods:

  • Implementation of an epilepsy surgical candidate identification system (epilepsy ID) in neurology clinics.
  • Deployment of an automated clinical trial eligibility screener (ACTES) in a pediatric emergency department.
  • Summarized approach, results, and key learnings from both AI system implementations.

Main Results:

  • The epilepsy ID system demonstrated comparable performance to neurologists in identifying surgical candidates (71% sensitivity, 77% PPV).
  • The ACTES system reduced coordinator screening time by 12.9%.
  • Project success hinged on interdisciplinary collaboration (AI, IT, clinical providers, leadership).

Conclusions:

  • These projects highlight innovative AI-provider interactions for clinical decision support.
  • Real-time AI integration enhances patient care delivery within clinical workflows.