Measuring clinician-machine agreement in differential diagnoses for dermatology.

Clicks: 370
ID: 60927
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #10 of 319 articles by views in the british journal of dermatology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 319 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Artificial intelligence (AI) algorithms have generated significant interest as a tool to assist in clinical workflows, particularly in image-based diagnostics such as melanoma detection. These algorithms typically answer narrowly-scoped questions, such as "is this lesion malignant?" By contrast, dermatologists frequently tackle less structured diagnostic questions, such as "what is this rash?" In practice, evaluating clinical cases often involves integrating insights from morphology, context, and history to determine a ranked-ordered list of possible diagnoses, i.e. a differential diagnosis, rather than a binary "yes" or "no" answer. An AI algorithm could aid a less experienced clinician by providing its own differential diagnosis, which may highlight potential diagnoses that have not been considered, and thereby help the clinician decide between additional evaluation and empiric treatment.
Reference Key
eng2019measuringthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Eng, C;Liu, Y;Bhatnagar, R;
Journal the british journal of dermatology
Year 2019
DOI
10.1111/bjd.18609
URL
Keywords
principal component analysis dimensionality reduction pca 2dpca outliers

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.