Machine learning and deep learning in internal medicine: demystifying concepts

Area of Science:

• Artificial Intelligence in Medicine
• Clinical Data Analysis
• Medical Decision Support

Background:

• Machine learning (ML) is revolutionizing clinical practice.
• ML tools analyze large datasets to find patterns and make predictions.
• This is crucial for modern medical decision-making.

Purpose of the Study:

• To provide an accessible introduction to ML concepts for internists.
• To highlight ML applications in clinical tasks like diagnosis and prognosis.
• To explore advanced ML techniques and ethical considerations.

Main Methods:

• Explanation of supervised, unsupervised, and reinforcement learning.
• Emphasis on the critical role of data quality in medical ML.
• Overview of the systematic process for developing ML projects in healthcare.

Main Results:

• ML enhances diagnostic accuracy and treatment personalization.
• It aids in optimizing healthcare resource allocation.
• Advanced methods like neural networks and explainability are discussed.

Conclusions:

• Integrating ML tools can significantly improve clinical outcomes.
• A critical and ethical approach is essential when using ML in medicine.
• ML empowers clinicians to enhance patient care and operational efficiency.