pith. sign in

arxiv: 2008.07734 · v1 · pith:TKWIYDBMnew · submitted 2020-08-18 · 💻 cs.AI · cs.CY

Trust and Medical AI: The challenges we face and the expertise needed to overcome them

classification 💻 cs.AI cs.CY
keywords medicaltrustchallengesconsequencesgroupshealthcareinstitutionsaccreditation
0
0 comments X
read the original abstract

Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. However, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes two contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GraphFlow: An Architecture for Formally Verifiable Visual Workflows Enabling Reliable Agentic AI Automation

    cs.AI 2026-05 unverdicted novelty 4.0

    GraphFlow is an architecture for formally verifiable visual workflows that treats diagrams as executable specs with proof-checkable contracts, backed by a pilot of 8728 runs at 97.08% completion on an early prototype ...