Published By: Newsweek, 1/26/2017
A team of researchers at Stanford University has developed an artificial intelligence (AI) algorithm that can identify early symptoms of skin cancer. The researchers trained the algorithm with a large data set of images that had already been identified as cancerous or benign. The algorithm identifies skin lesions with the same accuracy as board-certified dermatologists.
Flesch-Kincaid Grade Level of Article: 11.8
Extended Discussion Questions
- How might the introduction of this new technology influence the way patients interact with their primary-care practitioners?
- The article quotes the app creators as suggesting that this type of technology “could replace primary healthcare”.
- Do you think this might happen?
- Could this kind of technology be detrimental to basic healthcare?
- What could be some benefits?
- Do you have any concerns about the AI algorithm being used to identify skin cancer? Would you trust the algorithm’s reliability? Why or why not?
- How could this technology impact healthcare spending? Could it reduce spending? Increase it?
- If students saw the video: Did you notice anything about the pictures they used as examples? If the examples are representative of the training data, do you think the algorithm would do an equally good job for all patients?
Relating This Story to the CSP Curriculum Framework
Global Impact Learning Objectives:
- LO 7.2.1 Explain how computing has impacted innovations in other fields.
- LO 7.3.1 Analyze the beneficial and harmful effects of computing.
- LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.
Global Impact Essential Knowledge:
- EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.
- EK 7.3.1A Innovations enabled by computing raise legal and ethical concerns.
Other CSP Big Ideas:
- Idea 4 Algorithms
Banner Image: “Network Visualization – Violet – Offset Crop”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0