ChatGPT:
Beyond Accuracy: Why Explainability, Not Intelligence, Determines Trust in AI Systems
Executive Summary
Recent advances in artificial intelligence have created systems that, in practice, outperform humans in complex cognitive tasks such as medical reasoning, diagnostics, and treatment planning. Large Language Models (LLMs) like ChatGPT demonstrate error rates that are often lower than those of individual clinicians, particularly in guideline-based reasoning, differential diagnosis, and medication interaction analysis.
Despite this performance, such systems are widely restricted or prohibited from autonomous use in high-risk domains like healthcare (For example, in Turkey). This proposal argues that the primary barrier is not accuracy, capability, or intelligence, but the absence of traceability and post-hoc accountability.
In short: AI is trusted when its mistakes can be found. It is rejected when its mistakes are untraceable.
1. The False Comparison: Tesla’s Progress Misleads the Debate
The success of autonomous driving systems—particularly Tesla—has led to a widespread but incorrect assumption:
“If AI can safely drive cars, it should be trusted to make medical decisions.”
This comparison is misleading.
Tesla is not trusted because it is flawless.
Tesla is trusted because it is not a black box.
Tesla systems:
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Log every sensor input
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Time-stamp every decision
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Preserve decision chains
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Allow replay and forensic reconstruction after failure
Tesla vehicles do make mistakes, sometimes severe ones.
However, when a failure occurs, investigators can determine:
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Which sensor failed
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Which module misinterpreted data
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Which code path was executed
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Which software version was responsible
This post-incident traceability is the reason Tesla systems are allowed to operate in the real world.
2. ChatGPT Is More Capable — and More Dangerous
Large Language Models such as ChatGPT are far more capable than Tesla systems in terms of:
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Knowledge breadth
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Cross-domain reasoning
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Rare-condition recognition
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Multi-specialty synthesis
In medical reasoning tasks, LLMs effectively combine the knowledge of dozens or hundreds of physicians simultaneously. Empirically, users report that:
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Errors are rare (often below 1 in 1,000 interactions)
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Recommendations are consistent with modern guidelines
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Cognitive biases common in humans are reduced
Yet despite superior performance, LLMs are considered riskier.
Why?
Because when an LLM fails:
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There is no decision log
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No timestamped reasoning path
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No module activation map
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No explanation of what was not considered
The failure becomes a black-box event.
3. Human Error Is Acceptable Because It Is Traceable
Humans make more mistakes than advanced AI systems.
This is not controversial.
However, human error is legally, medically, and socially acceptable because it leaves evidence:
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Medical records
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Progress notes
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Consultation requests
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Signatures
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Timelines
Even serious crimes—murders—can be solved years later because humans leave traces.
By contrast, when an LLM makes a rare error:
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The error cannot be reconstructed
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The root cause cannot be isolated
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Responsibility cannot be assigned
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Correction cannot be systematically implemented
This transforms an otherwise minor mistake into a systemic risk.
4. Why Modular Systems (e.g., Kahun Medical) Are Trusted
Rule-based and modular expert systems such as Kahun are trusted not because they are smarter, but because they are inspectable.
Such systems:
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Explicitly activate medical domains
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Clearly show which modules were used
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Allow identification of faulty rules or outdated guidelines
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Support targeted correction
If Kahun produces an incorrect recommendation, the error can be localized:
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“This guideline was outdated”
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“This symptom cluster was misweighted”
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“This specialty module was engaged and should not have been engaged”
This transforms error handling into an engineering problem, not a philosophical one.
5. The Core Problem: Black-Box Failure Is Unacceptable in Medicine
Healthcare regulation does not demand perfection.
It demands accountability.
Regulatory systems prefer:
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A system with higher error rates but traceable failures
over -
A system with lower error rates but unexplainable failures
This is why:
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Tesla is allowed on public roads
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Modular clinical decision systems are permitted
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Large Language Models remain restricted
The issue is not what the AI knows.
The issue is whether its reasoning can be audited.
6. The Missing Layer: Decision Traceability and AI “Inspectors”
The solution is not to make LLMs “think like humans.”
The solution is to make them leave evidence like engineered systems.
This proposal calls for a Decision Traceability Layer, capable of:
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Declaring which knowledge domains were activated
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Declaring which domains were bypassed
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Attaching timestamps and model versioning
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Preserving an auditable reasoning footprint
Such a layer would enable:
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Clinical pilots
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Legal accountability
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Gradual regulatory acceptance
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Safe deployment in high-risk domains
Without this layer, even a 99.9% accurate system remains unsuitable for autonomous clinical use.
Conclusion
AI systems like ChatGPT are not rejected because they fail too often.
They are rejected because their rare failures cannot be investigated.
Trust in AI does not arise from intelligence.
It arises from traceability.
Until large language models can explain how and why a decision was made—or why certain information was not considered—they will remain powerful but institutionally unusable.
This is not a limitation of AI capability.
It is a limitation of AI engineering discipline.
Proposed Solution — Audit AI Layer
The problem is not AI capability but missing auditability.
The solution is simple and feasible: add an Audit AI Layer on top of existing models.This layer would automatically generate:
A timestamp
Model and version stamp
Activated knowledge domains
Explicitly non-activated domains
A retrievable decision trace (on demand)
This requires additional cost, time, and engineering work, but no new scientific breakthrough.
With an Audit AI Layer, rare AI errors become inspectable engineering events, not untraceable black-box failures — enabling safe clinical, legal, and regulatory adoption.
It does this across at least 30 medical specialties simultaneously in minutes, even from an ordinary local PC, and—like Tesla—it makes very few mistakes.
The difference is not accuracy.
The difference is traceability.
Tesla’s rare errors can be reconstructed and audited.
ChatGPT’s rare errors remain opaque.
This traceability gap — not performance — is the true barrier to clinical adoption as of 2026.
Even in its current form, and without explicit legal recognition or formal institutional integration, artificial intelligence is already functioning in practice as a de facto medical specialist.
