Summary of Moving Healthcare Ai-support Systems For Visually Detectable Diseases Onto Constrained Devices, by Tess Watt et al.
Moving Healthcare AI-Support Systems for Visually Detectable Diseases onto Constrained Devices
by Tess Watt, Christos Chrysoulas, Peter J Barclay
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new pilot study explores the use of TinyML, a technique that processes data on low-spec devices, to provide healthcare support in areas with limited connectivity. The research focuses on diagnosing skin diseases using AI assistants on Raspberry Pi devices without internet access. A model was trained on 10,000 images of skin lesions and achieved a test accuracy of 78% and a test loss of 1.08 when applied to classify visually detectable diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A small pilot study uses TinyML technology to bring healthcare support to people in rural areas with limited internet access. The goal is to help doctors diagnose skin diseases using images taken by a camera on a low-cost computer called Raspberry Pi. The system was tested and found to be 78% accurate at diagnosing certain skin conditions. |
