International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-driven clinical decision support systems for personalized treatment planning

Author(s) Srinivasa Kalyan Vangibhurathachhi
Country United States
Abstract AI-driven Clinical Decision Support Systems (CDSS) are transforming personalized treatment planning in healthcare. Traditional CDSS models which rely on rule-based algorithms are limited in providing individualized care as they offer generalized treatment recommendations. This research explores how AI-enhanced CDSS utilises machine learning models, natural language processing (NLP) and deep learning techniques to overcome these limitations by incorporating patient-specific data to realise personalized treatment planning in healthcare. The research also examines the effectiveness of AI-CDSS in improving patient outcomes, with a focus on oncology, cardiology, and chronic disease management.
Findings show that the integration of machine learning, natural language processing, and deep learning techniques into CDSS demonstrates the ability to optimize treatment outcomes in clinical areas like oncology, cardiology and chronic disease management. AI systems like IBM Watson for oncology and sepsis treatment algorithms illustrate potential of AI in improving diagnostic accuracy, reducing treatment-related risks and enhancing overall patient care. Despite these promising advancements, several challenges such as the need for high-quality, diverse datasets, overcoming clinician hesitancy and navigating regulatory and ethical concerns remain. Future research should focus on enhancing AI system transparency, improving data integration with emerging technologies like wearables and genomics, and developing comprehensive ethical frameworks for AI deployment in healthcare. These findings highlight the transformative potential of AI in clinical settings, ushering in a new era of personalized, data-driven healthcare.
Keywords AI, clinical decision support systems, personalised treatment planning, Natural Language Processing, machine learning, deep learning
Field Engineering
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-09-05
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.54998
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals