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Blog
Andy Pollen

From Black Box to Glass Box: Transparency as the New Normal for Clinical AI

Adoption, algorithms, Analytics
Table of Contents

Healthcare leaders no longer ask whether clinical AI can improve time-to-treatment, streamline workflows or improve margins. Those benefits are well established. The defining question now is: “Why should we trust it?”

In boardrooms and at the bedside, this is the tension at the heart of the next era of clinical AI – an era shaped not only by smarter, more efficient tools like foundation models but also by the degree to which patients, clinicians and the C-suite feel safe adopting them.

AI Transparency as a Competitive Strategy

Healthcare has always run on trust:

  • Patients trust clinicians
  • Clinicians trust systems
  • C-suite trusts data

AI disrupts this chain. Its complexity and opacity – the “black box” problem – make it harder to assure safety, explain outputs or assign accountability. Without transparency, trust falters at every level of patient care.

The Joint Commission and the Coalition for Health AI (CHAI) have underscored this in their “Responsible Use of AI in Healthcare (RUAIH)” draft guidance: transparency, risk assessment and bias monitoring are among the non-negotiables for responsible AI adoption.

For leaders, transparency is more than a compliance requirement. It’s becoming a new form of currency in the healthcare economy:

  • Reducing friction in procurement
  • Lowering risks for enterprise adoption
  • Strengthening regulatory confidence
  • Signaling leadership and innovation

However, transparency only works if it’s consistent and comparable. Without a standardized way to disclose information, every AI vendor can define transparency differently.

What AI Model Cards Deliver

Model cards were first proposed in the AI research community as a way to bring accountability and standardization to algorithm development. They function like “nutrition labels” for AI:  concise, structured documents that disclose what’s inside a model, including how it was built, where it works, where it doesn’t and what risks it carries.

CHAI has advanced this idea by creating a standardized model card framework specifically for healthcare AI. This moves information beyond data scientists, making it accessible to anyone seeking clarity without wading through raw code or dense technical papers.

Aidoc Is Leveraging CHAI Model Cards

Aidoc has long partnered with CHAI to advance responsible AI governance. Building on CHAI’s framework, we’ve introduced model cards for all cleared and released algorithms, providing standardized disclosures that include:

  • Algorithm purpose, intended users and out-of-scope scenarios
  • Data sets it was developed and tested on
  • Target populations and regulatory clearances
  • Performance benchmarks (safety, fairness and reliability)
  • Known risks, limitations and bias mitigation strategies
  • Ongoing monitoring and governance practices

Model cards are shared with our customers and updated with every algorithm release, ensuring stakeholders always have the most current information.

Why It Matters Now

The Joint Commission and CHAI’s draft guidance are shaping the direction of healthcare AI governance. Aidoc’s adoption of model cards reflects alignment with these emerging standards and makes transparency practical for customers. Organizations that can operationalize transparency will accelerate adoption, strengthen trust and stay ahead of regulatory expectations.

For Aidoc, transparency has always been built into how we design and deliver AI. Our commitment to providing model cards – alongside tools like Aidoc Analytics, which gives customers self-service visibility into AI performance, adoption and impact – underscores that approach.

The future of healthcare AI won’t be defined by who builds the most complex models but by who earns the most trust. By putting transparency at the core of our strategy, Aidoc is helping shape the standards that will guide the industry’s next era.

Want to learn more about our model cards or Aidoc Analytics? Schedule time to speak with an AI expert. 

Want to learn more about our model cards or Aidoc Analytics?
Schedule time to speak with an AI expert. 

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Andy Pollen
Andy Pollen is a former Aidocee.
Andy Pollen
Former Aidocee

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