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What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary

Richard A Young1, Carmel M Martin1, Joachim P Sturmberg1

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Summary

Artificial intelligence (AI) and machine learning (ML) show promise in primary care for specific tasks but struggle with complex cases and data inconsistencies. Careful development and testing are crucial for effective AI/ML integration.

Keywords:
Artificial IntelligenceClinical Decision-MakingComplexity ScienceInformation TechnologyMachine LearningMedical InformaticsPrimary Care PhysiciansPrimary Health CareQuality Improvement

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Area of Science:

  • Medical Informatics
  • Complexity Science
  • Health Services Research

Background:

  • Primary care physicians face both excitement and apprehension regarding artificial intelligence (AI) and machine learning (ML) integration.
  • Complexity science offers a framework to understand the potential impact and limitations of AI/ML in primary care.
  • Existing AI/ML applications have shown success in diagnostics and administrative tasks but face challenges in complex treatment recommendations and managing comorbidities.

Purpose of the Study:

  • To explore the potential applications and challenges of AI/ML in primary care settings.
  • To provide insights into which AI/ML applications are most likely to affect primary care in the future.
  • To evaluate the conditions under which AI/ML tools are most effective in healthcare.

Main Methods:

  • Analysis of current AI/ML applications in healthcare, focusing on successes and failures.
  • Application of complexity science and complex adaptive systems framework to evaluate AI/ML effectiveness.
  • Review of a specific intervention using AI/ML as an adjunct in medical decision-making.

Main Results:

  • AI/ML excels in image-based diagnostics and administrative tasks (e.g., voice-to-text notes) but is less successful in recommending treatments for complex diseases or managing multiple comorbidities.
  • AI/ML has exacerbated health equity disparities and its impact on physician-patient relationships is largely unknown.
  • AI/ML tools are most effective when tasks are limited in scope, data are clean and deterministic, and workflows are compatible.

Conclusions:

  • AI/ML tools are unlikely to improve comprehensive primary care, especially given the error-prone and inconsistent nature of primary care data.
  • Primary care involvement in AI/ML development and rigorous pre-implementation testing are essential.
  • Unlike electronic health records, AI/ML tools should not be assumed to automatically enhance primary care work life, quality, safety, or decision-making.