ReviewBiomaterials translational2026
An overview of CRISPR-artificial intelligence theranostics: Current and emerging applications.
Review in Biomaterials translational, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Decoding and Overcoming Temozolomide Resistance Through CRISPR/Cas Technologies.Molecular diagnosis & therapy · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostics are revolutionizing precision medicine by enabling highly sensitive detection of nucleic acid and protein biomarkers. Building on these capabilities, CRISPR-based theranostics now aim to unify real-time disease detection with targeted therapeutic interventions. However, traditional CRISPR diagnostics face several limitations, including restricted multiplexing, off-target effects, and challenges in delivery efficiency. To overcome these issues, artificial intelligence (AI) has significantly enhanced CRISPR platforms by enabling intelligent guide RNA (gRNA) design, interpretation of complex biosensor outputs, and facilitation of rapid clinical decision-making. Machine learning tools such as DeepCRISPR, Azimuth 2.0, DeepHF, and CRISPRpred support the development of highly specific gRNAs, reduce off-target events, and personalize genome-editing strategies based on individual genomic profiles. Recently, by combining CRISPR systems with nanomaterials, fluorescence-based detection, and electrochemical sensing, researchers have developed advanced biosensors capable of detecting a broad spectrum of disease biomarkers, from cancer-associated nucleic acids to viral and genetic disorders. These advances support both diagnostics and gene therapy, enabling accurate, low-cost testing at home, in point-of-care settings, and in resource-limited environments. Together, the integration of AI and CRISPR is accelerating biomarker discovery and the development of intelligent, adaptive therapeutic platforms. New point-of-care diagnostic tests (POCTs) based on CRISPR-AI are essential for early screening of high-mortality diseases, and CRISPR-based diagnostic assays have emerged as powerful, versatile alternatives to traditional nucleic acid tests, offering rapid, programmable, and portable diagnostic solutions. This review explores the evolution of CRISPR-AI theranostic systems, current and emerging POCT applications. It highlights the technological, clinical, and ethical challenges shaping their translation into next-generation precision diagnostics.
Indexed as
Identifiers
What Socratic 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 Socratic graph.