Last month, a CIO told me his AI vendor promised 99% accuracy. I asked him one question: “Can they show you how they got there?”
Silence.
That’s the problem plaguing health care IT right now. We’re buying AI that works like magic tricks. Impressive results, zero visibility into the method. And in health care, where every decision affects patient care and regulatory compliance, magic isn’t enough.
The Black Box Problem Is Real
I’ve spent the last five years building AI for health care, and here’s what vendors don’t want you to know: Most “AI-powered” solutions are just neural networks with fancy marketing. They’re pattern matchers. Nothing more.
Sure, they can find patterns in your data. They’ll flag potential diagnoses, identify risk factors and surface coding opportunities. But ask them to explain why they flagged something? You get a confidence score. That’s it.
Try explaining a 87.3% confidence score to an auditor. Or a physician. Or your compliance team when it’s defending a multimillion-dollar claim. Good luck with that.
Your teams know this problem intimately. They spend hours, sometimes days, trying to reverse-engineer what the AI found. They become human translators for machine decisions. It’s backward, and everyone knows it.
Enter Neuro-Symbolic AI (and Why You Should Care)
Neuro-symbolic AI isn’t new. It’s been around in research labs for years. What’s new is that it finally works at scale in production health care environments.
Here’s the difference: Instead of one black box, you get two transparent systems working together. Neural networks do what they do best: process massive amounts of unstructured data, recognize patterns and extract information from messy clinical notes. Symbolic reasoning does what IT does best: apply rules, follow logic and create audit trails.
Together? You get AI that not only finds answers but also shows its work.
I watched a coding team test this recently.
Its old NLP system flagged a potential diabetes diagnosis.
Confidence: 92%.
Time to verify: 35 minutes of digging through records.
Our neuro-symbolic AI technology flagged the same diagnosis but also showed: “E11.22 = The diagnosis is documented in the assessment section. The condition is supported by Treatment MEAT, including the current use of Jardiance, Insulin Glargine, and metformin, which are all prescribed for managing diabetes. CKD is supported by Assessment MEAT with a planned referral to nephrology and Treatment MEAT with the medication Jardiance, which is indicated for kidney disease in diabetic patients.” Time to verify: 2 minutes.
That’s not a marginal improvement. That’s a different category of technology.
What To Actually Look For (Skip the Marketing Decks)
When vendors pitch you their “explainable AI,” ask these specific questions:
“Show me a reasoning trace for a complex decision.” Not a simplified example. A real one. If they show you confidence scores or heat maps, that’s not reasoning. You want to see logical steps: “Because X and Y, therefore Z.”
“How do you encode domain knowledge?” Real neuro-symbolic systems have explicit knowledge representations: rules, ontologies and logic programs that your team can actually see and modify. If it’s all “learned from data,” you’re looking at dressed-up neural networks.
“What happens when the neural network and symbolic reasoner disagree?” This reveals if it’s truly hybrid or just two systems duct-taped together. Good systems have conflict-resolution mechanisms. Bad ones just pick the highest confidence score.
“Can my team modify the reasoning rules?” If the answer involves retraining models or calling their data science team, run. You should be able to update coding guidelines or compliance rules without touching the neural components.
The ROI Nobody Talks About
Everyone calculates AI ROI on efficiency gains. Charts processed, time saved, headcount reduced. But that misses the real value in health care.
The actual ROI comes from decisions you can defend. Audits you pass. Physicians who trust the system because they can see its logic. Compliance teams that sleep at night knowing every decision has a paper trail.
One health system I work with discovered its “95% accurate” risk-adjustment program was actually only defensible for 67% of codes. The other 28% were technically “correct” patterns but lacked the specific clinical documentation required by the Centers for Medicare & Medicaid Services. In a RAC audit, that 28% represents a massive financial liability, with potential clawbacks that could run into the millions. The rest? Good luck proving them in an audit. After implementing neuro-symbolic AI, its defensible rate hit 98%+. Same codes, but now with proof. It moved from “hoping it doesn’t get audited” to “ready to be audited.”
Your Next Steps (If You’re Serious)
First, audit your current AI. Not its accuracy, but its explainability. Pick ten complex decisions, and ask your vendor to show the complete reasoning. If it can’t, you have a problem.
Second, prepare your organization. Neuro-symbolic AI isn’t plug and play. Your team needs to understand both machine learning and logical reasoning. Budget for training.
Third, demand proof-of-concept projects with your data. Not demos, not case studies. Real implementations with your messy, complicated, exception-filled data. That’s where black box AI falls apart and neuro-symbolic AI shines.
The choice facing health care CIOs isn’t whether to adopt AI. It’s whether to adopt AI you can trust, verify and defend. In health care, there’s really only one answer to that question.
