Adapting Artificial Intelligence Concepts to Enhance Clinical Decision-Making: A Hybrid Intelligence Framework

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare

Background:

• Artificial intelligence (AI) offers significant potential to improve healthcare by providing data-driven insights for clinical decisions.
• The complexity of medical information presents challenges for AI implementation in clinical settings.
• A hybrid approach, integrating AI with clinician expertise, is crucial for effective AI application in healthcare.

Purpose of the Study:

• To explore the adaptation of AI development methodologies to support clinical decision-making.
• To emphasize the importance of a hybrid approach combining AI capabilities with clinician expertise.
• To present a conceptual framework for integrating AI-driven hybrid intelligence into clinical practice.

Main Methods:

• Developed a conceptual framework for integrating AI-driven hybrid intelligence into clinical practice.
• Adapted key AI concepts, including backpropagation, quantization, and avoiding overfitting, for clinical decision support.
• Focused on enhancing clinicians' ability to interpret complex medical data for improved diagnosis and treatment planning.

Main Results:

• Backpropagation enables clinicians to refine assessments with new data, improving diagnostic accuracy.
• Quantization assists in breaking down complex medical problems, aiding in prioritizing care.
• Avoiding overfitting helps clinicians balance common and rare diagnoses, reducing errors and complexity.

Conclusions:

• AI-driven hybrid intelligence can enhance clinical decision-making by improving data analysis, treatment prioritization, and diagnostic accuracy.
• Adapting AI methodologies preserves the essential human element in healthcare.
• Combining AI strengths with clinician expertise leads to more balanced, effective, and patient-centered healthcare decisions.