Computer-assisted diagnosis to improve diagnostic pathology: A review

Area of Science:

• Pathology
• Medical Informatics
• Artificial Intelligence

Background:

• Increasing demand for accuracy and efficiency in diagnostic pathology.
• Emergence of computer-assisted diagnosis (CAD) as a transformative solution.
• Growing integration of digital pathology and Artificial Intelligence (AI) in medical diagnostics.

Purpose of the Study:

• To explore practical applications, implications, strengths, and weaknesses of CAD in diagnostic pathology.
• To provide a comprehensive overview of recent advancements in CAD for pathology.
• To guide future research, clinical practice, and policy-making regarding CAD in pathology.

Main Methods:

• Comprehensive literature search of English-language studies.
• Focus on CAD tools, digital pathology, and AI applications in pathology.
• Review and synthesis of findings on CAD's impact and ethical considerations.

Main Results:

• CAD tools show transformative potential in streamlining diagnostic processes and reducing turnaround times.
• Significant strides in digital pathology and AI integration are enhancing diagnostic accuracy.
• Computational methods offer promising prospects for prognostic biomarker discovery.

Conclusions:

• Computer-assisted diagnosis (CAD) has the potential to revolutionize diagnostic pathology.
• Ethical considerations including data privacy, equity, and trust in AI deployment require careful examination.
• Elucidating CAD's horizons and pitfalls is crucial for successful integration into clinical practice.