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Updated: Dec 27, 2025

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

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Interpretable Artificial Intelligence: Why and When.

Adarsh Ghosh1, Devasenathipathy Kandasamy1

  • 1Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.

AJR. American Journal of Roentgenology
|March 5, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in medicine faces an interpretability challenge. Future AI research must explain predictions to advance biological understanding, not just report accuracy.

Keywords:
biomedical researchdeep learningmachine learning

Related Experiment Videos

Last Updated: Dec 27, 2025

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

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Published on: September 26, 2025

654

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence (AI) algorithms are increasingly utilized in medical research.
  • The 'black box' nature of many AI algorithms poses challenges for clinical adoption.

Purpose of the Study:

  • To discuss the interpretability problem in medical AI.
  • To emphasize the necessity of AI-driven scientific discovery for analyzing medical big data.

Main Methods:

  • Discussion of the challenges posed by opaque AI algorithms in clinical settings.
  • Highlighting the limitations of relying solely on accuracy and sensitivity metrics.

Main Results:

  • The opacity of current AI algorithms presents a dilemma for their clinical implementation.
  • Clinical decision-making requires understanding the basis of AI predictions.

Conclusions:

  • AI research in medicine must move beyond performance metrics.
  • Explaining the reasoning behind AI predictions is crucial for enhancing biological knowledge and understanding.