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Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.

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Doctors prefer detailed visualizations for artificial intelligence (AI) health predictions. This study explored how healthcare professionals best receive machine learning algorithm results via a mobile app.

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Area of Science:

  • Healthcare technology
  • Medical informatics
  • Artificial intelligence in medicine

Background:

  • Artificial intelligence (AI) algorithms are increasingly integrated into healthcare, offering potential for improved patient outcomes.
  • Predictive machine learning models in mobile health applications require clear communication strategies for healthcare professionals.
  • A consensus on effectively presenting AI-driven health predictions to clinicians is currently lacking.

Purpose of the Study:

  • To investigate healthcare professionals' preferred methods for receiving predictions from machine learning algorithms.
  • To assess the usability of a novel mobile application, RandomIA, designed for clinical outcome prediction.
  • To determine optimal visualization techniques for AI-generated prognostic and diagnostic health data.

Main Methods:

  • Systematic literature search conducted in MEDLINE, PubMed, EMBASE, and Web of Science.
  • Development of the RandomIA mobile application for predicting clinical outcomes, initially for COVID-19.
  • Usability assessment using the System Usability Scale (SUS) questionnaire with 69 Brazilian doctors evaluating three distinct prediction visualization methods.

Main Results:

  • A majority of doctors (62.9%) preferred a complex visualization for prognostic outcomes (mechanical ventilation, ICU admission, death), including bar graphs and probability density graphs.
  • Similarly, 65.4% of doctors favored the same detailed visualization for COVID-19 diagnostic predictions.
  • The findings suggest a preference among clinicians for comprehensive, detailed presentation of machine learning algorithm outputs.

Conclusions:

  • Healthcare professionals may be more receptive to detailed, complex visualizations of AI-generated predictions.
  • Effective communication of machine learning insights is crucial for successful AI implementation in clinical practice.
  • Further research should explore user-centered design principles for AI tools in healthcare to enhance adoption and utility.