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Data Characterization for Reliable AI in Medicine
Sivaramakrishnan Rajaraman1, Ghada Zamzmi1, Feng Yang1
1Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA.
Recent Trends in Image Processing and Pattern Recognition : 5Th International Conference, RTIP2R 2022, Kingsville, TX, USA, December 01-02, 2022, Revised Selected Papers. International Conference on Recent Trends in Image Processing And
|February 13, 2023
Summary
Artificial Intelligence (AI) in medical computer vision shows promise for disease screening. Data characteristics like volume and veracity significantly impact AI algorithm reliability and patient care outcomes.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Machine Learning
Background:
- AI-powered medical computer vision algorithms offer potential advancements in disease screening, diagnosis, and patient care.
- The performance and reliability of these AI algorithms are critically dependent on the characteristics of the data they are trained on.
Purpose of the Study:
- To discuss key data characteristics impacting AI in medical computer vision.
- To explore the influence of data characteristics on the design, reliability, and evolution of machine learning models in this field.
Main Methods:
- Review of data characteristics: Volume, Veracity, Validity, Variety, and Velocity.
- Analysis of recent research from the authors' lab to understand these impacts.
Main Results:
- Data characteristics significantly influence the design and reliability of medical AI algorithms.
- Understanding these impacts is crucial for developing dependable AI-driven medical decision-making tools.
Conclusions:
- Addressing data characteristics is essential for advancing AI in medical computer vision.
- Reliable AI outcomes depend on careful consideration of data properties throughout algorithm development and deployment.
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