Use Case

Uncertain Accountability In Medicine

Problem Statement

  • A diagnostic Artificial Intelligence company was faced with legal and diagnostic problems
  • Firstly the same symptoms can have different meanings in different areas – in one area it may mean food poisoning, in another malaria
  • If a diagnosis was incorrect, who was to blame – the company, the algorithm, the individual programmer or the doctor?

Brain Everywhere Solution

  • Doctors trained models directly for each diagnosis in each area.
  • As Brain Everywhere is 100% transparent and each outcome can be traced back to the original medical decision.
  • It was clear that the doctor was the legal decision taker and that it fell under his/her malpractice insurance.

Metrics Gathered

  • Varied metrics depending on area and diagnosis – Brain Everywhere supported a suite of different models which could be easily modified per area

Success and Improvements

  • The twin problems of diversity and accountability were eradicated
  • The doctor’s insurers were willing to cover the Sue decisions as well

Implementation Timeline

  • Approx 1 doctor week per diagnosis

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Mass customization scenarios and uncertain accountability situations where costs of mistakes are high

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