A Dual-Part Theoretical Framework for AI in Complex Neurology Cases (P5-2.001)

This study aims to develop and explore the implementation of a dual-part theoretical framework that integrates artificial intelligence (AI) with interdisciplinary medical approaches. The framework focuses on enhancing care for patients with complex neurological conditions, addressing both physiological and psychological needs.

Authors

Minhan L. Dinh
Emily Koenig
Kyungmin Kim (ORCID: https://orcid.org/0000-0002-6874-2583)
Tiffany Harmanian
Rexhina Ago

Institutions

A.T. Still University (US)

Publication Details

Journal: Neurology
Published: 2025-04-07
DOI: https://doi.org/10.1212/wnl.0000000000211549
Primary Topic: Brain Tumor Detection and Classification
Type: article
Field-Weighted Citation Impact: 0.00

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article

A Dual-Part Theoretical Framework for AI in Complex Neurology Cases (P5-2.001)

Minhan L. Dinh, Emily Koenig, Kyungmin Kim, Tiffany Harmanian et al.

Apr 7, 2025

Neurology

Brain Tumor Detection and Classification

article

A Dual-Part Theoretical Framework for AI in Complex Neurology Cases (P5-2.001)

Minhan L. Dinh, Emily Koenig, Kyungmin Kim, Tiffany Harmanian, Rexhina Ago

April 7, 2025article en

Abstract

NeurologyVol. 104(7_Supplement_1)

A.T. Still University (US)

Openalex Percentile: Top 91%

Brain Tumor Detection and Classification

DOI Source OpenAlex

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A Dual-Part Theoretical Framework for AI in Complex Neurology Cases (P5-2.001) — Minhan L. Dinh, Emily Koenig, et al. · Neurology (2025) | TGRS Research Map | TGRS