Artificial intelligence-enhanced patient evaluation: bridging art and science.

Area of Science:

• Digital health
• Artificial intelligence in medicine
• Clinical informatics

Background:

• Traditional patient evaluation methods persist despite advancements in digital health and AI.
• AI-enhanced tools are emerging to augment clinical encounters, shifting towards data-driven processes.
• Current clinical workflows have not yet fully integrated transformative digital health technologies.

Purpose of the Study:

• To present an evidence-based vision for AI's role in enhancing traditional clinical practices.
• To illustrate how digital technologies can be integrated into routine workflows for personalized medicine and efficient care.
• To explore the potential of AI-enabled devices in transforming patient evaluation.

Main Methods:

• Review of current AI applications in healthcare.
• Analysis of how AI tools can enhance history taking and physical examination.
• Examination of data from AI-enabled stethoscopes, cameras, and wearable sensors.
• Discussion of integrating digital technologies into clinical workflows.

Main Results:

• AI applications can streamline tasks and incorporate diverse data streams (e.g., AI-enabled stethoscopes, wearables).
• Digital tools offer a pathway to personalized medicine and efficient care delivery.
• The integration of these technologies can establish a new paradigm of longitudinal patient monitoring.

Conclusions:

• AI and digital health tools hold significant promise for revolutionizing patient evaluation and clinical care.
• Successful integration requires addressing practical, ethical, and regulatory challenges.
• A data-driven approach, augmented by AI, can enhance traditional medical practices.