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Artificial Intelligence (AI) and Machine Learning (ML) enhance healthcare workflows for better patient outcomes. Implementing AI requires a structured approach focusing on culture, governance, and personalized care for improved public health management.

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Area of Science:

  • Artificial Intelligence (AI) in healthcare
  • Machine Learning (ML) and Deep Learning (DL) applications
  • Public health management and personalized medicine

Background:

  • AI and ML offer predictive and precision insights for managing disorders like cardiology and hematological diseases.
  • Positive perceptions of AI support its integration into healthcare for improved wellbeing and safety.
  • Existing workflows require structured transformations to effectively manage AI in public health.

Purpose of the Study:

  • To elucidate AI workflow for practitioners and citizens to comprehend personalized healthcare platforms.
  • To review AI management structures and practices impacting public health and medicine.
  • To appraise perspectives influencing AI adoption in healthcare.

Main Methods:

  • Conducted structure and practice reviews of AI in healthcare.
  • Appraised perspectives on AI management in public health and medicine.
  • Analyzed end-to-end AI workflow structures and step-by-step designs.

Main Results:

  • AI workflow management requires a model encompassing structure, culture, assurance, process steps, values, and governance.
  • Explanans and explanandum in AI workflow interpret step-by-step designs for AI success and biological analytic value.
  • Personalized care graphics illustrate AI's role in managing biological analytic value.

Conclusions:

  • Integrating AI, intelligent workflows, and structured processes improves wellbeing through personalized care.
  • AI in population health management supports descriptive, predictive, precise, and prescriptive medicine.
  • AI implementation aligns national, regional, and local healthcare needs for improved outcomes.