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Critical appraisal of a machine learning paper: A guide for the neurologist
Pulikottil W Vinny1, Rahul Garg2, MV Padma Srivastava3, Vivek Lal4, Venugoapalan Y Vishnu5
1 Neurology, Indian Naval Hospital Ship Asvini, Mumbai, India
2 Department of Computer Science and Engineering, Indian Institute of Technology, Delhi, India
3 Neurology, and Chief of Neurosciences Centre, All India Institute of Medical Sciences, New Delhi, India
4 Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
5 Neurology, AIIMS New Delhi, India
Correspondence Address:
Venugoapalan Y Vishnu,
Assistant Professor (Neurology), Room No. 704, CN Centre, Seventh Floor, All India Institute of Medical Sciences, New Delhi
India
 Source of Support: None, Conflict of Interest: None DOI: 10.4103/aian.AIAN_1120_20
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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.
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