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Critical appraisal of a machine learning paper: A guide for the neurologist
- Source :
- Annals of Indian Academy of Neurology, Vol 24, Iss 4, Pp 481-489 (2021), Annals of Indian Academy of Neurology
- Publication Year :
- 2021
- Publisher :
- Medknow, 2021.
-
Abstract
- Machine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studies. The superlative performance metrics of ML algorithms often hide the opaque nature of its inner workings. Questions regarding ML model's interpretability and reproducibility of its results in real-world scenarios, need emphasis. Given an abundance of time and information, the expert clinician should be able to deliver comparable predictions to ML models, a useful benchmark while evaluating its performance. Predictive performance metrics of ML models should not be confused with causal inference between its input and output. ML and clinical gestalt should compete in a randomized controlled trial before they can complement each other for screening, triaging, providing second opinions and modifying treatment.
- Subjects :
- Artificial neural network
business.industry
Deep learning
deep learning
AIAN Review
neural networks
Machine learning
computer.software_genre
critical appraisal
Critical appraisal
machine learning
Causal inference
Benchmark (computing)
Gestalt psychology
Medicine
Neurology. Diseases of the nervous system
Neurology (clinical)
Artificial intelligence
RC346-429
business
computer
Interpretability
Complement (set theory)
Subjects
Details
- ISSN :
- 09722327
- Database :
- OpenAIRE
- Journal :
- Annals of Indian Academy of Neurology
- Accession number :
- edsair.doi.dedup.....e49fc6f0e27fbcfe84d4b1cbaa02c598