Summary of Artificial Intelligence-based Decision Support Systems For Precision and Digital Health, by Nina Deliu and Bibhas Chakraborty
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health
by Nina Deliu, Bibhas Chakraborty
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 Machine learning educators will appreciate the potential of reinforcement learning (RL) to transform healthcare domains like precision medicine and digital health. The authors discuss how AI-powered RL can improve diagnosis, treatment, and monitoring for both clinical populations and the general public. By leveraging machine learning, RL has shown promise in dynamic problems like dynamic treatment regimes and just-in-time adaptive interventions. This methodological survey of RL methods highlights their potential applications in precision and digital health, including illustrative case studies on using RL in real-world practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is changing the way we care for people. AI can help doctors diagnose and treat patients more accurately. A type of AI called reinforcement learning (RL) is especially useful for making decisions about treatments that need to be changed over time. For example, RL can be used to develop personalized treatment plans for patients with chronic diseases. In this paper, the authors explore how AI-powered RL can improve healthcare outcomes and make it more efficient. |
Keywords
* Artificial intelligence * Machine learning * Precision * Reinforcement learning
