Artificial Intelligence and Machine Learning in Health Care: Current Capabilities, Emerging Potential, and Future...

Area of Science:

• Health Informatics
• Artificial Intelligence
• Machine Learning

Background:

• Healthcare is undergoing a significant transformation driven by advancements in artificial intelligence (AI) and machine learning (ML).
• These technologies offer novel approaches to clinical decision-making, operational efficiency, and patient care.
• Understanding the current landscape, challenges, and future potential of AI/ML in medicine is crucial.

Purpose of the Study:

• To provide a comprehensive review of AI and ML applications in healthcare.
• To explore current clinical uses, ethical considerations, and future research directions.
• To emphasize the importance of responsible AI/ML adoption for patient and clinician benefit.

Main Methods:

• Historical overview of AI/ML in medicine.
• Glossary of key terms.
• Review of current clinical applications, including diagnostics, treatment planning, and operations.
• Examination of ethical, security, and bias concerns.
• Analysis of case studies and practical impacts.
• Discussion of regulatory trends and future research priorities.

Main Results:

• AI and ML are actively reshaping healthcare delivery across various domains.
• Radiology and pathology are mature fields for AI/ML application, while areas like sepsis prediction and personalized therapy show rapid advancement.
• Ethical, security, and bias issues require careful consideration for effective implementation.
• Case studies demonstrate the practical impact of AI/ML tools in clinical settings.

Conclusions:

• AI and ML hold immense potential to enhance patient care and support clinicians.
• Responsible and ethical adoption strategies are paramount to realizing these benefits.
• Continued research and regulatory oversight are necessary to guide the future of AI/ML in healthcare.