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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Artificial intelligence in digital pathology - time for a reality check.

Arpit Aggarwal¹, Satvika Bharadwaj¹, Germán Corredor^1,2

¹Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.

Nature Reviews. Clinical Oncology

|February 11, 2025

Summary

Artificial intelligence (AI) in digital pathology has advanced significantly, improving image analysis for clinical oncology. This perspective examines technological, regulatory, and commercial factors influencing AI adoption in pathology workflows.

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Area of Science:

Digital pathology
Artificial intelligence in medicine
Oncology imaging

Background:

Artificial intelligence (AI) is increasingly used in medicine, with digital pathology showing significant promise for image analysis in clinical oncology.
The period between 2019 and 2024 has witnessed substantial developments in AI applications within digital pathology.

Purpose of the Study:

To comprehensively examine the evolution of AI in digital pathology from 2019 to 2024.
To assess technological innovations, regulatory trends, and commercial implications impacting AI adoption.
To identify progress and challenges in AI-driven digital pathology for routine clinical oncology practice.

Main Methods:

Review of technological advancements in AI for digital pathology.
Analysis of regulatory landscapes, including in-house devices and laboratory-developed tests.
Evaluation of reimbursement frameworks and commercial investment strategies.

Main Results:

Significant technological improvements have led to more robust and scalable AI solutions for digital pathology.
Regulatory developments are actively shaping the integration of AI tools, particularly for in-house and laboratory-developed tests.
Reimbursement and commercial factors play a crucial role in the clinical adoption of AI in pathology.

Conclusions:

AI in digital pathology has progressed considerably, offering enhanced capabilities for clinical oncology.
Challenges remain in regulatory pathways, reimbursement, and commercialization for widespread adoption.
Addressing these challenges is key to integrating AI-driven digital pathology into routine oncological practice.