Concern 621 · Working draft · editorial review in progress
Diagnostic errors
Diagnostic error happens when AI suggests the wrong explanation for a person's symptoms or fails to identify an illness that is present.
Understanding the concern
A diagnosis connects many clues: what someone feels, their medical history, an examination and test results. AI may recognize a familiar pattern while overlooking a detail that changes its meaning. A wrong answer can delay treatment, while a false alarm can lead to tests or treatment someone does not need.
Studies of particular models show that answering medical questions well does not guarantee sound decisions when information arrives gradually. Think of solving a mystery from selected clues rather than seeing the whole scene. Useful tools should show which evidence supports a suggestion, what is missing and when a clinician needs to investigate another possibility.
An example
A system explains abdominal pain using a common condition but misses a clue suggesting a different urgent problem.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The study evaluates selected older language models and four abdominal conditions. It supports a specific limitation, not a claim that every current clinical AI performs poorly.
Hager and colleagues · Nature Medicine 2024 · Clinical model evaluation
Evaluation and mitigation of the limitations of large language models in clinical decision-making ↗ (opens in a new tab)Evaluation of selected Llama 2 models on four abdominal diagnoses. This is not an assessment of every current model or a clinical outcome trial.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What evidence should a clinician check before accepting an AI-generated diagnosis?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Service
Service means using abilities and resources to meet real needs. It asks whether the people meant to benefit actually receive help, rather than treating them as a means to another goal.
Speak with the people the project is meant to serve. Notice burdens it may add, such as extra checking or confusing forms. Judge success by their experience as well as the organization's targets.
Ask together: Who is this meant to serve, and do they experience it as helpful?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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