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Review of research from 2014 to 2024 describes growing use of AI across healthcare

Study title: The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency

This peer-reviewed review from January 2025 examined how AI technologies including robotics, machine learning, deep learning, and natural language processing are being applied in healthcare. Drawing on Web of Science literature from 2014-2024 and case studies such as Google Health and IBM Watson Health, it reported growth in publications and use in patient interaction, predictive analytics, and remote monitoring.

Read the study abstract

Background and Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote m…

By the Truvace desk1 min read1 sourceEvidence: Emerging · 30/100 — Commentary, opinion, or narrative review · 1 independent sourcePeer-reviewedTRV-2026-0544· cite & verify
The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency
Hospital Universitari Doctor Peset, València 02 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0
Trace impact readingContested
P 47The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 45The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

The findings matter because they link AI adoption to tangible clinical and operational outcomes like enhanced diagnosis and more individualized treatment plans, while also documenting persistent barriers. Uncertainty remains about how to overcome data security, budget constraints, and ethical implementation challenges to achieve responsible mainstream use.

Main points

  1. Review analyzed Web of Science data from 2014-2024 and real-world case studies such as Google Health and IBM Watson Health.
  2. AI-related healthcare publications rose from 158 articles (3.54%) in 2014 to 731 articles (16.33%) by 2024.
  3. Core technologies examined include robotics, machine learning, deep learning, and natural language processing for patient interaction, predictive analytics, and remote monitoring.

The gain

AI integration in healthcare is enhancing medical professionals' diagnostic capabilities, enabling more individualized treatment plans, and improving operational effectiveness and patient involvement through applications like remote monitoring and predictive analytics.

The problem

Mainstream implementation of AI in healthcare is hindered by data security issues and budget and resource constraints.

The rundown

The review synthesized literature from Web of Science between 2014 and 2024 using keywords like AI, ML, and healthcare applications, and examined case studies including Google Health and IBM Watson Health.

It found a steady rise in publications and identified core uses in remote monitoring and predictive analytics, while also noting that successful use requires ethical and responsible use and ongoing research, instruction, and multidisciplinary cooperation.

What this doesn’t fix

Mainstream implementation is constrained by data security risks, budget and resource limitations, and the need for ethical and responsible use with ongoing multidisciplinary research.

Sources

  1. Peer-reviewedHealth Science Reports2025-01-01

The debate