Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods

Area of Science:

• Radiology and Medicine
• Artificial Intelligence
• Machine Learning

Background:

• Machine learning (ML) and artificial intelligence (AI) are increasingly prevalent in medicine.
• Many medical professionals lack a deep understanding of ML/AI principles and evaluation methods.
• There's a growing need to critically appraise ML applications in clinical practice.

Purpose of the Study:

• To provide a foundational overview of ML concepts for a medical audience.
• To explain key ML metrics and interpret their significance.
• To demystify technical jargon associated with ML in healthcare.

Main Methods:

• Review of ML principles and common terminology.
• Explanation of essential ML performance metrics.
• Guidance on critical appraisal of ML studies and tools.

Main Results:

• The study aims to enhance understanding of ML functionality.
• It provides clarity on interpreting ML performance metrics.
• It equips readers to better evaluate ML applications in radiology and medicine.

Conclusions:

• Understanding ML metrics and concepts is crucial for clinicians.
• Transparency in ML methods and algorithm sharing is vital for trust.
• This work empowers medical professionals to confidently assess ML tools.