Pittsburgh innovation history of medical AI

John Kalafut — Tue, 09 Jun 2026 03:47:12 GMT

John F. Kalafut, PhD | Chief Technology & AI Officer, Asher Informatics | June 2026

SIIM’s Back in Pittsburgh

Before I get into this, a quick note for folks (‘yinz’) in my network who aren’t in the imaging informatics world. SIIM is the Society for Imaging Informatics in Medicine, the professional community of clinicians, researchers, engineers, and informaticists working across the full spectrum of medical imaging, from foundational information science and clinical validation research to the practical deployment and governance of the technology that makes it all run. If you work in health AI, clinical AI governance, or enterprise health IT, there’s more overlap with your world than you might think.

And look, when I attended my first SCAR/SIIM years ago as a wide-eyed graduate student and early-career engineer, I definitely did not think I’d someday be writing a post reflecting on the history of the field. That was something those ‘old guys’ did. Well. Here I am. Still geeky. Just with more context and considerably less hair.

SIIM is in Pittsburgh this week, and I’ve been thinking about why that matters beyond local pride, the inevitable Primanti Brothers pilgrimage, and trying to find the corner of the North Side where Dr. Robby and the rest of the crew from The Pitt do their thing (Hint – tell Uber to find Allegheny General Hospital (AHN)). Because the history of this region is foundational to what we all do in imaging informatics and health AI.

The medical AI origin story that lives here

You can’t get through five minutes anywhere today without someone saying “AI.” And Carnegie Mellon is always name-dropped. But do we actually know what happened there?

Allen Newell and Herbert Simon demonstrated their Logic Theorist at the 1956 Dartmouth workshop, one of the foundational moments for AI as an academic discipline. Newell eventually landed at CMU, and Simon, who would go on to win the Nobel Prize in Economics, was already there. These two, and numerous faculty, grad students etc. were architects of AI becoming a field. Topics that grew from these early efforts included symbolic reasoning, cognitive modeling, and how machines might replicate human problem-solving. Born, refined, and institutionalized in Pittsburgh.

And even back then, the tension existed. Symbolic and logic-driven approaches on one side, and the “connectionists,” the somewhat dismissive label for people working on neural network ideas, on the other. Sound familiar? That argument is still going, just with a much bigger compute budget. And a lot more charlatans preaching “just build more compute!!”

CMU, being CMU, took a pragmatic engineering posture. Data-driven methods mattered if they worked. Robotics flourished there (and yes, “physical AI” is the cringe term du jour, but the work was real). And in 2006, CMU founded the world’s first Machine Learning Department, formally establishing what had been building since 1997 through the Center for Automated Learning and Discovery. First. In the world. That’s a Pittsburgh factoid worth knowing.

First. In the world. CMU’s Machine Learning Department, founded in 2006, was the first of its kind anywhere.

UPMC and the clinical translation that made it real

The academic story is important, but Pittsburgh’s contribution to medical AI specifically runs through UPMC, one of the largest integrated health systems in the country and a genuine innovation engine. UPMC wasn’t just a place that adopted technology. It was a place that invented and exported it. The clinical informatics and imaging informatics work that happened at UPMC — in partnership with Pitt’s School of Medicine, the UPMC Hillman Cancer Center, and others — helped define what it meant to move AI from research into real clinical environments.

That includes early work on computer-aided detection, clinical decision support architectures, and the kind of deployment infrastructure questions that are — surprise — still being asked today. What does it mean to validate AI in a production environment? How do you monitor a model’s performance after it’s deployed? Who is responsible when it’s wrong? These aren’t new questions. Pittsburgh was asking them when most of the “thought leaders” on your LinkedIn feed were still in middle school.

Why this matters right now

We are at a moment in health AI where governance, oversight, and responsible deployment are finally getting serious traction. Regulations are coming. Liability is clarifying. Health systems are realizing that deploying AI without a management infrastructure is not a long-term viable posture.

Pittsburgh has been thinking about this longer than most. The intellectual DNA of careful, rigorous, clinically-grounded AI development lives here. That’s worth celebrating when SIIM comes to town. And it’s worth carrying forward as we build what comes next.

If you’re at SIIM this week, come find us. We’d love to talk about where the field has been and where it’s going.

Pennsylvania Drew a Line on Healthcare AI. The Next Step Is Drawing the Map.

hello@asherinformatics.com (Asher Informatics) — Wed, 20 May 2026 04:00:00 GMT

By Charlotte Kalafut, Co-Founder, Asher Informatics

Pennsylvania’s HB 1202 — the Artificial Intelligence in Health Care Act — passed the House in April 2026. It’s not law yet, but its passage marks a turning point. For the first time in Pennsylvania, a legislative body has said clearly: AI used in healthcare requires governance, transparency, and accountability. The question that follows is not whether health systems need to respond. It is how.

What the bill actually does

HB 1202 targets AI systems used in clinical decision-making, prior authorization, and treatment recommendations. It requires covered entities to disclose when AI is involved in a clinical decision, maintain audit trails, and designate a responsible person for AI oversight. It also establishes a right for patients to request human review of AI-assisted decisions.

This is not a compliance checklist. It is a signal that the governance posture health systems adopt today will determine their legal and operational exposure tomorrow.

What health systems need to do now

Most health systems do not have a complete inventory of the AI tools they’re running. Many cannot tell you whether those tools are performing as intended in their patient population. Very few have a documented process for what happens when an AI system fails.

HB 1202 makes those gaps visible. The organizations that will navigate this transition well are the ones that start building governance infrastructure before it’s legally required, not after.

How Asher Informatics can help

The Asher Informatics FORGE plaftorm was built for exactly this moment. It provides the infrastructure to inventory clinical AI tools, monitor their performance, document oversight, and demonstrate compliance — not as a one-time audit, but as a living operational system.

Pennsylvania drew a line. We can help you draw the map of what comes next.

Asher Informatics Clinical AI Blog