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REVIEW

An overview of CRISPR-artificial intelligence theranostics: Current and emerging applications

Prerna Vats1# ,  Bhavika Baweja1# ,  Chainsee Saini1 ,  Atar Singh Kushwah2 ,  Ashok Kumar3 ,  Sandeep K. Srivastava1 ,  Rajeev Nema1*
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1 Department of Biosciences, Manipal University Jaipur, Dehmi Kalan, Jaipur-Ajmer Expressway, Jaipur, Rajasthan, India
2 Women’s Biomedical Research Institute, Icahn School of Medicine at Mount Sinai, New York, United States of America
3 Department of Biochemistry, All India Institute of Medical Sciences, Bhopal, Saket Nagar, Bhopal, Madhya Pradesh, India
BMT, 106 https://doi.org/10.12336/bmt.25.00106
Submitted: 22 July 2025 | Revised: 25 August 2025 | Accepted: 28 August 2025 | Published: 28 October 2025
© 2025 by the Author(s). Licensee Biomaterials Translational, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0) (https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
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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. 

Keywords
CRISPR-Cas
Biosensing
Gene editing
Diagnostics
Precision medicine
Funding
The authors would like to acknowledge funding support from Manipal University Jaipur for the Enhanced Seed Grant under the Endowment Fund (No. E3/2023-24/QE-04-05), as well as the Department of Science & Technology - Fund for Improvement of S&T Infrastructure project (DST/2022/1012) from the Government of India, awarded to the Department of Biosciences, Manipal University Jaipur.
Conflict of interest
The authors declare that they have no competing interests.
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