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Review surveys how AI is being used across healthcare, from imaging to drug discovery

Study title: Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives

Published September 23 2025 as a peer-reviewed review, the article surveys how AI is being applied across healthcare, from analyzing electronic health records and medical imaging to supporting drug discovery, predictive analytics, telemedicine and wearable biosensors, with emphasis on low-resource and remote settings.

Read the study abstract

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearab…

By the Truvace desk1 min read1 sourceEvidence: Emerging · 30/100 — Commentary, opinion, or narrative review · 1 independent sourcePeer-reviewedTRV-2026-0484· cite & verify
Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives
"Jaturawit Rodcheewan at Manila Doctors Hospital" by B20180 is marked with CC0 1.0. To view the terms, visit https://creativecommons.org/publicdomain/zero/1.0/deed.en/.
Trace impact readingContested
P 49The 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 52The 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

It matters because it links technical capabilities to patient-centered outcomes like reduced errors and expanded access, while making clear that benefits depend on solving privacy, bias, interpretability and oversight issues, leaving open how scalable, ethical and evidence-driven implementation will be achieved across different health systems.

Main points

  1. Review covers AI across disease detection, treatment planning, patient monitoring, drug discovery, predictive analytics, telemedicine and wearable health technologies.
  2. Leverages machine learning and deep learning to analyze electronic health records, medical imaging and genomic profiles for pattern recognition and treatment optimization.
  3. Highlights equity potential through mobile diagnostics, wearable biosensors and lightweight algorithms for low- and middle-income countries and remote settings.

The gain

AI analysis of electronic health records, medical imaging and genomic data can reduce clinical errors, optimize resources and improve patient outcomes while expanding access in low-resource settings.

The problem

Deployment of AI in healthcare is limited by risks of data privacy breaches, algorithmic bias, lack of model interpretability, and gaps in regulatory oversight and human clinical oversight.

The rundown

The review describes AI methods using machine learning and deep learning to process electronic health records, imaging and genomic data for disease detection, personalized care and drug discovery, and notes applications in telemedicine and wearables.

It frames implementation requirements as clinician training in AI literacy, adoption of resource-efficient tools, global collaboration and robust regulatory frameworks to ensure transparency, safety and accountability while keeping AI complementary to professionals.

What this doesn’t fix

Successful deployment remains constrained by unresolved issues of data privacy, algorithmic bias, model interpretability, regulatory oversight and need for human clinical oversight.

Sources

  1. Peer-reviewedEuropean Journal of Medical Research2025-09-23

The debate