ReviewCurrent pharmaceutical design2025
Advances in Machine Learning Models for Healthcare Applications: A Precise and Patient-Centric Approach.
Review in Current pharmaceutical design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Intelligent Decision Support for Transcatheter Aortic Valve Replacement: Machine Learning Spans From Anatomical Assessment to Dynamic Risk Modeling.Reviews in cardiovascular medicine · 2026Review
- Quantum-enhanced deep learning for elastographic image-based characterization of cutaneous and subcutaneous masses.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealthcare is rapidly leveraging machine learning to enhance patient care, streamline operations, and address complex medical issues. Though ethical issues, model efficiency, and algorithmic bias exist, the COVID-19 pandemic highlighted its usefulness in disease outbreak prediction and treatment optimization.
aimThis article aims to discuss machine learning applications, benefits, and the ethical and practical challenges in healthcare. DISCUSSION: Machine learning assists in diagnosis, patient monitoring, and epidemic prediction but faces challenges like algorithmic bias and data quality. Overcoming these requires high-quality data, impartial algorithms, and model monitoring.
conclusionMachine learning might revolutionize healthcare by making it more efficient and better for patients. Full acceptance and the advancement of technologies to improve health outcomes on a global scale depend on resolving ethical, practical, and technological concerns.
Indexed as
Identifiers
39950456What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.