Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review

Overview

Journal Cureus

Specialty Biomedical Engineering

Date 2024 Jun 10

PMID 38854327

Authors

Diny Dixon

Hina Sattar

Natalia Moros

Srija Reddy Kesireddy

Huma Ahsan

Mohit Lakkimsetti

Madiha Fatima

Dhruvi Doshi

Kanwarpreet Sadhu

Muhammad Junaid Hassan

Affiliations

Soon will be listed here.

Abstract

This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.

Citing Articles

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