Insights

Foundational Theory vs. Clinical Reality: Governing Clinical AI in 2026

Health systems are now being asked to innovate while simultaneously managing risk, accountability, and patient safety, often without a single, unified playbook.

White Paper--Beyond the Algorithm: Ensuring Clinical Validity at Scale

Most health systems lack the technical infrastructure to generate continuous evidence of AI performance. Instead, they rely on episodic, manual validation that fails to capture real-world shifts in data.

Foundation Models in Healthcare: What You Need to Know

They are rapidly gaining traction in healthcare for their ability to streamline clinical, operational, and patient-facing tasks.

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Medical Ethicist Weighs in on Direct-to-Patient Approach for AI Radiology, Part 1

In this blog post, we will explore the ethical issues surrounding this topic, with a particular focus on justice and rights to AI access drawing upon insights from an interview with Dr. Eric Keller, Senior IR Resident at Stanford, and an expert in medical ethics.

The Rocky Road of Standardization in AI Healthcare: Making Signal from Noise

In this week’s Domain Knowledge we explore the need for quality standardization, what it means and how it can be best established in the ever-changing field of healthcare AI.

Considerations When Selecting an AI Partner for Your Healthcare System

This week’s domain knowledge focuses on selecting the right healthcare AI partner. This is a critical decision that can significantly impact patient care, operational efficiency, and the overall success of the health system.

Using Artificial Intelligence in the Women’s Health Space: Managing Breast Imaging

This week’s Domain Knowledge explores ways radiologists can incorporate AI in their practice.

Using Artificial Intelligence in Oncology

AI has made significant inroads in various therapeutic areas, with cancer care benefiting greatly from this powerful technology.

Using Artificial Intelligence in Radiology

AI is poised to become a revolutionary force in healthcare. From the early identification of cancer to the treatment of chronic diseases, there are limitless opportunities for leveraging AI in radiology to ensure more equitable and efficient patient care.

AI and Healthcare: The Ultimate Solution for Health Equity

A notable AI use case in the medical field is the application of AI in radiology and the impact on health equity.

How AI Is Solving Medical Errors

Medical errors are the third leading cause of death and account for nearly 250,000 fatalities annually. AI provides solutions to the medical error dilemma by equipping healthcare professionals with tools that both improve accuracy and function as a safety net.

Determining the ROI of Healthcare AI

This week’s Domain Knowledge takes a high level look at the steps a health system can take to determine the ROI of their AI initiatives.

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