Summary of Adapting An Artificial Intelligence Sexually Transmitted Diseases Symptom Checker Tool For Mpox Detection: the Hehealth Experience, by Rayner Kay Jin Tan et al.
Adapting an Artificial Intelligence Sexually Transmitted Diseases Symptom Checker Tool for Mpox Detection: The HeHealth Experience
by Rayner Kay Jin Tan, Dilruk Perera, Salomi Arasaratnam, Yudara Kularathne
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 The research paper presents an AI-powered digital screening test for Monkeypox (Mpox) symptoms. The test was developed by leveraging an existing tool to screen for sexually transmitted diseases. The AI model uses a modified convolutional neural network and was initially trained on 5,000 cases of visually diagnosable penis pathologies, including Syphilis, Herpes Simplex Virus, and Human Papilloma Virus. The test showed high accuracy in ruling in and out Mpox symptoms. The paper also highlights the challenges faced during the development process, including data privacy and security concerns, lack of initial training data, and generalizability issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an AI-powered digital screening test for Monkeypox (Mpox) symptoms. They used a smartphone app to collect images of penises and trained their model on 5,000 cases. The test was tested and showed high accuracy in identifying Mpox symptoms. This tool can help healthcare professionals diagnose Mpox more quickly and accurately. |
Keywords
» Artificial intelligence » Neural network
