Health AI Needs an Appeal Path, Not Just an Accuracy Score

Opinion. Thesis: a health AI system is not ready for real-world use unless the person affected can reach a qualified human, challenge a consequential output and continue through a workable non-AI path. Accuracy scores matter, but an appeal path is the operational test of whether human oversight is real.

Canada’s federal, provincial and territorial privacy regulators published a joint resolution on October 29 calling for responsible AI across health care and health research. The timing matters: the resolution itself was adopted in Ottawa on September 15, while the public announcement arrived six weeks later. It is a policy signal, not a new statute, and organizations should not mistake it for a compliance deadline. But it is unusually specific about what responsible deployment should look like.

The regulators want six principles applied across an AI system’s full life cycle: privacy protection, validity and reliability, safety, respect for human rights, transparency and accountability. They also call on developers to support human-in-the-loop oversight and a mechanism for clients and end users to challenge AI outputs. Health providers are urged to name a contact person, offer an accessible way to challenge consequential decisions or inferences, provide opt-out choices where practicable, and verify outputs through appropriate human review.

The appeal path is part of the product

Those recommendations should change procurement. A vendor should not be able to satisfy “human oversight” by adding a sentence to a policy while the actual workflow makes the automated result difficult to question. The path from concern to review needs an owner, a response time, access to the relevant record and a clear authority to correct or override the output.

This applies beyond diagnostic tools. An AI scribe can insert an inaccurate statement into a clinical note. A scheduling system can infer priority. A fraud or eligibility system can make access more difficult. A research tool can reclassify participants or combine data in ways that create new privacy risks. In each case, a nominally advisory output can become consequential once staff, software or institutional policy routinely act on it.

Ontario’s privacy commissioner made the same issue concrete in January guidance for AI scribes. Its checklist asks organizations to disclose recourse options, assess vendors and alternatives, monitor systems through their lifespan and equip a knowledgeable contact person to answer concerns. That is a useful bridge between principle and implementation: recourse is not a help-centre link. It is a designed operating process.

Five questions before a health-AI pilot

Builders and buyers can turn the resolution into a short gate before deployment:

1. What can a person challenge? Define which outputs, inferences and data uses qualify, including errors that appear “low risk” to the vendor but materially affect the patient or participant.
2. Who reviews the challenge? Assign a qualified human with enough context and authority to change the record, pause the workflow or escalate the matter.
3. What evidence survives? Preserve the input, model or system version, output, relevant explanation, staff action and later correction. Without this record, an appeal becomes a contest of memory.
4. What happens while review is pending? Define a safe default. A challenge process is weak if the system’s consequence becomes irreversible before anyone responds.
5. Can the organization exit? Contracts should cover data return or deletion, audit access, incident notification, model changes, suspension and decommissioning. The resolution explicitly treats decommissioning as part of the life cycle.

These questions belong in vendor evaluation, technical design and staff training. They also belong in testing. Teams should simulate a disputed output before launch, measure how long review takes, confirm that the reviewer can see the necessary evidence and verify that corrections propagate to downstream systems.

Consent is not a substitute for recourse

Consent is important where the law requires it, but it cannot carry the entire governance burden. In health care, people may have limited alternatives, incomplete information or little time. A consent screen does not prove that a model is reliable for the relevant population, that data use is lawful, or that a harmful output can be corrected.

The same distinction matters for privacy due diligence. My earlier Canadian AI vendor privacy architecture checklist focused on data flows, retention and subcontractors. Health AI adds another layer: the organization must know how a person can contest what the system inferred from that data and how the operational record will change afterward.

Limits and what would change my view

The resolution is guidance from privacy regulators, not a clinical safety standard, a validation study or a harmonized national law. Health authority, provincial law and the intended use of a system will change the appropriate controls. An appeal mechanism also cannot rescue a tool that is unsafe, unlawfully trained or unreliable for its intended population.

I would soften this position if independent evidence showed that a particular low-impact use can be corrected automatically with no meaningful effect on care, rights or access. I would strengthen it if regulators turn these recommendations into enforceable requirements, or if incident evidence shows that nominal human review routinely fails under clinical workload.

For now, the practical standard should be simple: if a health-AI buyer cannot demonstrate the challenge, review, correction and exit workflow, the system is not ready to move from pilot enthusiasm to operational trust.

Disclosure: Jason Ansell is associated with Vector Smart Chain and writes books about blockchain, AI and digital infrastructure. Those interests are not the subject of the privacy regulators’ resolution, and no vendor or investment recommendation is made here.

Sources

• Office of the Privacy Commissioner of Canada: Responsible use of artificial intelligence in health and health research, adopted September 15, 2026; published October 29, 2026.
• Office of the Privacy Commissioner of Canada: News release on responsible AI in health and health research, October 29, 2026.
• Information and Privacy Commissioner of Ontario: AI Scribes — Checklist of Key Considerations for the Health Sector, January 28, 2026.