liveBook · Manning

Medical diagnosis

As explained in Inside AI

Medical diagnosis is the identification of a disease or medical condition based on symptoms, observations, and clinical evidence.

Medical diagnosis identifies a likely condition from a patient’s symptoms, observations, and test results. For AI, the challenge is not merely to suggest a condition, but to support decisions that are useful in clinical care. section 8.0 section 6.2.1

An illustrative diagnostic sequence is:

  • Address emergencies first, such as active blood loss. section 8.0
  • Check an obvious diagnosis with a decisive test where appropriate, then provide treatment. section 8.0
  • When the answer is unclear, test likely hypotheses using affordable, safe, noninvasive options before more burdensome ones. section 8.0

The COVID imaging test

A proposed shortcut was to train a machine-learning model on chest images labeled “infected” or “not infected,” then use uploaded scans for rapid diagnosis. Yet a review of 2,212 studies found no identified COVID imaging model suitable for clinical use, citing methodological flaws or underlying biases. A plausible demonstration is not the same as a reliable diagnostic tool. section 6.2.1

How Inside AI covers it

How deep it goes
Outlines diagnostic priorities and test selection, then assesses COVID imaging models without detailing their implementation.
Examples
Uses blood loss, a suspected broken arm, bronchitis testing, and COVID chest scans to illustrate decisions and limitations.
What it assumes
Introduces medical reasoning through everyday scenarios without requiring medical training.
What you can do after
Evaluate/understand it by distinguishing a plausible AI diagnosis from one shown to be clinically useful.
How it connects
Links diagnosis to expert systems, machine learning, medical imaging, and diagnostic testing.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.