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?
Our 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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