Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions
By J. Helm, A. Swiergosz, Heather S. Haeberle, J. Karnuta, J. Schaffer, V. Krebs, A. Spitzer, P. Ramkumar. Current Reviews in Musculoskeletal Medicine, 2020
499 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Health Informatics
View Paper: https://www.semanticscholar.org/paper/299c31b1969074c5d089d3bdd6b97846c5327579
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wattanapong Kurdthongmee
- Dimitrios Kateris
- Remigio Berruto
- Georgios Dolias
- Arsanchai Sukkuea
- Dionysis D. Bochtis
- Anindya Nag
- Subhas Chandra Mukhopadhyay
In Biomedical Engineering
Citations in the corpus
- Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “To that end, Machine Learning (ML) has emerged, which is a subset of artificial intelligence [11], by taking advantage of the exponential computational power capacity growth.”
- Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends (2023, background): “The ML algorithms comprise supervised learning, unsupervised learning, reinforcement learning, and deep learning, each with advantages and disadvantages, and can be applied to diverse domains, including image and speech recognition, as well as predictive analytics [113,114].”
- Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “Machine learning, a subset of AI, can potentially assist clinicians in interpreting complex data in a relatively short period using specialized algorithms (Helm et al., 2020; Krittanawong et al., 2022).”