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Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.

Daniel Velazquez-DiazJuan Eloy ArcoAndrés OrtizVeronica Perez-CabezasAlvaro Alba-RuedaJose Antonio Moral-MunozAlejandro Galan-Mercant
Published in: Journal of medical Internet research (2023)
The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.
Keyphrases
  • artificial intelligence
  • big data
  • machine learning
  • deep learning
  • clinical practice
  • electronic health record
  • physical activity
  • health information
  • current status
  • social media