Automated decision support systems for healthcare: An AI-powered approach

Artificial Intelligence (AI) has become a cornerstone of modern healthcare transformation, particularly through the development of Automated Decision Support Systems (DSS). These systems harness clinical data to assist in diagnosis, prognosis, and treatment planning. This review explores the evolution, implementation, and evaluation of AI-powered DSS across multiple clinical domains including radiology, emergency medicine, oncology, and mental health. Drawing on key studies and technological advancements, we present a theoretical model, benchmark comparative experiments, and discuss deployment challenges such as explainability, trust, and data governance. This article offers a roadmap for scaling AI in clinical environments while preserving safety, transparency, and human oversight.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-07-30
DOI
https://doi.org/10.30574/wjaets.2025.16.1.1240
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Automated decision support systems for healthcare: An AI-powered approach

Bhanuvardhan Nune
World Journal of Advanced Engineering Technology and Sciences
Artificial Intelligence in Healthcare and Education
article

Automated decision support systems for healthcare: An AI-powered approach

Bhanuvardhan Nune
article en

Abstract

Artificial Intelligence (AI) has become a cornerstone of modern healthcare transformation, particularly through the development of Automated Decision Support Systems (DSS). These systems harness clinical data to assist in diagnosis, prognosis, and treatment planning. This review explores the evolution, implementation, and evaluation of AI-powered DSS across multiple clinical domains including radiology, emergency medicine, oncology, and mental health. Drawing on key studies and technological advancements, we present a theoretical model, benchmark comparative experiments, and discuss deployment challenges such as explainability, trust, and data governance. This article offers a roadmap for scaling AI in clinical environments while preserving safety, transparency, and human oversight.

World Journal of Advanced Engineering Technology and SciencesVol. 16(1)
Openalex Percentile: Top 87%
Artificial Intelligence in Healthcare and Education
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