ReviewACS omega2026
Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.
Review in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Smart wearable biosensors: a transformative synergy between diagnosis and treatment of disease.RSC advances · 2026Review
- Advances in Machine Learning-Assisted Optical Sensing Arrays for Disease Diagnosis.Biomimetics (Basel, Switzerland) · 2026Review
- Biosensors for Circular RNA Profiling in Biological Matrices.Analytical chemistry · 2026Article
- Flexible polymeric materials and wearable biosensors for smart diabetes mellitus diagnostics and monitoring.iScience · 2026Review
- Point-of-Care Electrochemical Diagnostic Developments for Multidrug-Resistant Bacteria: Role of Aptamers and Nanomaterials.Biosensors · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This perspective analyzes the substantial advantages of Artificial Intelligence (AI) and machine learning (ML) in improving the efficacy and precision of biosensors, facilitating accurate detection of diverse physiological signals. Moreover, it emphasizes contemporary developments in biosensor technology and their uses in medical diagnosis, stressing their ability for early disease detection and continuous monitoring. The study also addresses major barriers to more widespread use, such as the lack of high-quality data sets, data variability issues, and the restricted relevance of many artificial intelligence techniques. Ethical questions about data privacy and security are also addressed, as are legal difficulties resulting from the rapid technological development of artificial intelligence. By exploring innovative approaches to overcome challenges, this study emphasizes the possibility of AI-enhanced biosensing systems to significantly improve healthcare results and support individualized medicine.
Identifiers
What 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.