When healthcare executives deploy AI systems, they often ask, “Is this accurate?” or “Will this improve efficiency?”
These are important questions! But there are a few more questions you should consider.
- Whose interests it serves?
- Who bears the risk when it fails?
- Who pays for damages if patients are harmed?
The Alignment Gap Nobody’s Talking About–
AI Alignment sounds abstract until something goes wrong.
- A radiologist relies on an AI tool that may underdiagnose disease in certain demographics.
- A hospital automation system denies care authorizations using a ‘logic’ no one can explain.
- A clinical decision support system recommends a treatment plan that aligns perfectly with hospital revenue goals but not with the patient outcomes.
In healthcare, AI alignment isn’t a technical problem but a governance problem. Healthcare providers are outsourcing it to vendors, regulatory checkboxes, and hoping for the best.
The Values Question Nobody's Answering
Every AI system encodes values. When you train a model on historical data, you’re encoding historical inequities. When you optimize for cost reduction, you’re encoding a choice about who bears the burden of efficiency. When you design a system without the voices of frontline clinicians, you’re encoding a particular vision of what healthcare should be.
The question isn’t whether AI has values. It is, whose values? And did anyone ask?
Most healthcare organizations can’t answer this. They audit for accuracy but not for alignment. They check whether a model works and not for whom it works, or whom it works against!
The Risk Nobody's Taking
Here’s the uncomfortable truth: when AI causes harm in healthcare, liability is murky.
- Did the vendor fail?
- Did the hospital misimplement?
- Did the clinician fail to override?
- Did the patient fail to question?
Three Things Healthcare Leaders Should Do Now
Second: Involve stakeholders in governance, not just implementation. The people closest to the problem, whether that’s clinicians, patients, data teams, or operational staff, should be able to see what happens when AI systems meet reality. They need seats at the table before systems are deployed, not after failures occur.
Third: Build transparency into contracts. You should be able to audit what your AI systems are doing, why they’re making specific decisions, and whose values they’re serving. If you can’t audit it, you don’t control it.
The Question That Matters Most
AI in healthcare will only become smarter, faster, and more embedded in care. The question isn’t whether to use it. It’s whether you’ll use it with alignment or with blind faith.
Because when an AI system in healthcare fails, someone always pays.
The only question is whether that someone is the organization that deployed it, the vendor who built it, the clinician who trusted it, or the patient who never had a choice.
Alignment isn’t a technical milestone. It’s a moral obligation in the age of intelligent care.
