Precision Healthcare: The Convergence of Human Expertise and Artificial Intelligence in Personalized Medicine

This article explores the transformative confluence of mortal moxie and artificial intelligence in advancing individualized drugs. It examines the paradigm shift from formalized to perfection healthcare, pressing AI's part in assessing complex genomic data, prognosticating treatment responses, and optimizing remedial interventions. The article shows computational approaches to inheritable analysis, illustrating how machine literacy enhances mutation identification and threat assessment across medical specialties. It shows prophetic analytics in treatment selection, demonstrating how AI integrates multi-modal case data to produce substantiated remedial strategies and dosing rules. The article develops a cooperative frame for mortal-AI commerce in clinical settings, balancing technological capabilities with clinician moxie while addressing ethical considerations in participatory decision-making. Eventually, it discusses perpetration challenges, including evolving nonsupervisory geographies, structural conditions, and the educational requirements of healthcare professionals, furnishing a comprehensive examination of how mortal-AI hookups are reshaping healthcare delivery toward more precise, effective, and case-centered approaches.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-08-20
DOI
https://doi.org/10.52783/jisem.v10i58s.12579
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Precision Healthcare: The Convergence of Human Expertise and Artificial Intelligence in Personalized Medicine

Siva Sai Kumar Yachamaneni
Journal of Information Systems Engineering & Management
Machine Learning in Healthcare
article

Precision Healthcare: The Convergence of Human Expertise and Artificial Intelligence in Personalized Medicine

Siva Sai Kumar Yachamaneni
article en

Abstract

This article explores the transformative confluence of mortal moxie and artificial intelligence in advancing individualized drugs. It examines the paradigm shift from formalized to perfection healthcare, pressing AI's part in assessing complex genomic data, prognosticating treatment responses, and optimizing remedial interventions. The article shows computational approaches to inheritable analysis, illustrating how machine literacy enhances mutation identification and threat assessment across medical specialties. It shows prophetic analytics in treatment selection, demonstrating how AI integrates multi-modal case data to produce substantiated remedial strategies and dosing rules. The article develops a cooperative frame for mortal-AI commerce in clinical settings, balancing technological capabilities with clinician moxie while addressing ethical considerations in participatory decision-making. Eventually, it discusses perpetration challenges, including evolving nonsupervisory geographies, structural conditions, and the educational requirements of healthcare professionals, furnishing a comprehensive examination of how mortal-AI hookups are reshaping healthcare delivery toward more precise, effective, and case-centered approaches.

Journal of Information Systems Engineering & ManagementVol. 10(58s)
Openalex Percentile: Top 89%
Machine Learning in Healthcare
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