Molecular Diagnostics – Advancements in AI in Enhancing Diagnostics Speed and Accuracy

Since the term ‘molecular disease’ was first coined in 1949,¹ the field of molecular diagnostics has been developing rapidly. Molecular diagnostic testing techniques such as polymerase chain reaction (PCR), isothermal amplification, microarray technology and high-throughput sequencing have been increasingly used to detect infectious disease pathogens.² Now, artificial intelligence (AI) and machine learning (ML) are poised to revolutionize the molecular diagnostics sector. Let’s explore the applications of AI in molecular diagnostics and other areas of medicine.

What is molecular diagnostics?

Molecular diagnostics involves analyzing DNA or RNA sequences to identify specific markers or signals that may indicate the presence or risk of certain diseases. For example, if a newborn baby is found to have the gene mutations associated with cystic fibrosis, the baby is likely to have the condition and can be treated at an early stage, leading to better outcomes. Additionally, molecular testing can reveal whether a patient has developed resistance to a particular medication, helping guide necessary adjustments to their treatment plan.^3,3a

AI in molecular diagnostics: Enhancing speed and accuracy

The impact of AI is being felt in many different areas of molecular diagnostics. 

AI-supported protein engineering

Protein engineering – using genetic technology to improve protein functions or create new ones – allows scientists to produce proteins with properties specifically beneficial for a range of applications, including ecology, food security, drug discovery and diagnostic medicine.^4,5 

AI models, including ML algorithms and deep learning (DL) frameworks, are currently being used to:^5a 

• predict protein structures; 
• analyze protein sequences; and 
• guide the engineering process.

But protein engineering also improves molecular diagnostics speed and accuracy:^3b

• Activity: Protein engineering can make proteins more active, meaning they do their job faster. Theoretically, because the enzymes are more active, fewer of them are needed to achieve the same results. This can lead to the development of miniaturized, point-of-care-like tests.
• Accuracy: Protein engineering can also make proteins more specific. They can be tailored to bind only to the exact target, such as a specific disease marker, reducing errors and improving reliability.

AI is transforming protein engineering by enabling rapid, precise design of enzymes like polymerases, which are critical for molecular diagnostics. In an era where even slight variations can affect test outcomes and treatment decisions, AI helps identify and optimize polymerase variants that perform reliably across diverse testing conditions. Medix Biochemica’s polymerase library exemplifies this innovation, offering a curated selection of enzyme mutants pre-screened for high performance, inhibitor resistance, and adaptability. Combined with expert buffer optimization and close collaboration with test developers, AI-driven protein engineering supports the creation of tailored, high-precision diagnostic tools that meet evolving healthcare demands.^5a

Machine learning is enhancing digital polymerase chain reaction

Digital PCR (dPCR) is a highly precise method for measuring DNA or RNA. It’s a novel and rapidly growing area which will see more use as molecular diagnostics continue to improve. dPCR partitions a sample into thousands of tiny droplets or wells, each of which undergoes a PCR reaction, and determines whether each partition is positive or negative for the target DNA sequence. dPCR provides absolute quantification of DNA or RNA, in contrast to quantitative polymerase chain reaction (qPCR), which requires a standard curve for relative quantification. dPCR is also more sensitive than qPCR for detecting low concentrations of nucleic acids.^3b,6

ML is being used to enhance dPCR by improving data interpretation and test optimization. The benefits of supporting digital PCR with AI include:^7,8

• Curve analysis: ML algorithms analyze amplification curves to distinguish between positive, negative and ambiguous reactions more accurately than manual methods do.
• Data reduction: ML reduces ‘noise’ and identifies trends in dPCR data, improving the detection of rare variants or subtle changes in the levels of nucleic acids.
• Predictive modeling: ML integrates environmental or clinical metadata with dPCR results to improve the prediction of outcomes or disease states.

Real-world applications: AI in early disease detection

Here are some examples of how AI diagnostics solutions are helping to diagnose diseases earlier and treat them more efficiently, for better patient outcomes.

• Cancer: AI is being used in oncology to enable personalized cancer treatments through genomic analysis, identifying mutations and tailoring therapies to each patient's unique cancer profile. This ‘precision oncology’ helps to improve treatment efficacy, reduce side effects and speed up drug discovery.⁹
• Multiplex assays: In multiplex assays, which analyze multiple genetic targets at once, AI algorithms quickly process complex data, identify subtle patterns and minimize the risk of human error.^3a
• Predictive biomarker discovery: By analyzing large-scale genomic, transcriptomic and proteomic datasets, AI can identify novel biomarkers associated with disease states or treatment responses.^3a 

Overall, research confirms that AI-enabled decision support systems, when used correctly, can improve patient safety by reducing errors and enhancing drug management.¹⁰

Common challenges in molecular diagnostic testing (and how AI can help)

Molecular diagnostics requires the handling of complex data and samples so there are several common challenges facing the industry, which AI can help to address. Here are some examples of how AI streamlines molecular diagnostics processes in a laboratory setting:¹¹

• Sample quality control: AI algorithms can provide automated quality control with high accuracy.
• Turnaround time: AI solutions can help improve efficiency by speeding up end-to-end workflows in a wet lab.
• Cost: AI can save time and resources, leading to reduced costs, e.g. it can reduce the need for outsourcing costs for pathologists and lab tech time. 
• Limited tissue availability: AI allows for better management of limited tissue resources by more efficiently targeting which downstream tests are required, improving the quality of that testing and replacing some of it entirely.
• Administrative load: AI can extract data from test reports or observations from hand-written notes to populate electronic records, reducing the need for lab staff to enter data manually.

While ML and AI diagnostics have the potential to be transformative, there are still some barriers to their implementation, e.g. if AI is not properly trained – using representative and unbiased data – it may produce unreliable or inaccurate results, with disastrous consequences in the healthcare space.¹¹ 

Implementing AI in a lab setting also introduces new vulnerabilities, as sensitive data becomes a target for cyberattacks. AI systems must be fully secure to ensure the protection of data and patient privacy.¹¹

Future directions: Harnessing AI for next-gen diagnostics

Quantum AI

More advanced AI technologies, including quantum AI (QAI), are being introduced into the research domain to accelerate the training process and improve rapid diagnostics models. Quantum computers have far more processing power than conventional computers, which could allow QAI algorithms to analyze enormous amounts of medical data in real-time, leading to more accurate and efficient diagnoses.¹²

ML in enzyme engineering and directed evolution

In protein engineering (or enzyme engineering) specifically, ML is set to play an increasingly important role in the coming years. Generative ML models have the potential to unlock new enzymes with a diverse range of functions, evolvabilities and folds. Supervised ML also offers a unique opportunity to accelerate protein fitness optimization by more efficiently selecting which protein variants to synthesize and screen. ML can even suggest protein variants that would not usually be considered.¹³

Directed evolution is a powerful enzyme engineering technique used to improve and tailor the functions of enzymes by mimicking natural evolutionary processes in the lab. AI is also being incorporated into directed evolution to significantly enhance the process’s efficiency, precision and outcomes. What’s more, AI can predict beneficial mutations, identifying which amino acid changes are likely to improve the protein's performance.^3a

These trends highlight the importance of adopting AI and precision enzyme engineering for future diagnostics.

Learn more about Medix Biochemica's MDx offerings and capabilities 

The case for AI in molecular diagnostics

As molecular diagnostics labs face increasingly large sample and data volumes, it makes sense to adopt new technologies like AI solutions to help automate processes and analyze huge amounts of data. Labs can improve the speed, efficiency and accuracy of their operations by harnessing AI-driven data analysis, workflow automation, quality control and more.

MedixMDx offers a comprehensive range of raw materials for molecular biology applications. Our range includes polymerases designed for end-point PCR, real-time PCR and isothermal applications, as well as lyo-ready and lyophilized polymerases. Thanks to our robust product offerings and unwavering commitment to quality, you can rely on us as a trusted partner to support you in developing high-quality, accurate molecular diagnostic assays.¹⁴

Subscribe to our latest articles and stay ahead of the curve in the in vitro diagnostics field

Contact us for more information

References: 

1. Timeline of molecular diagnostics advancements. Cardinal Health. Accessed May 12, 2025. https://www.cardinalhealth.com/en/product-solutions/medical/laboratory-products/molecular-diagnostics/articles/timeline-of-molecular-diagnostics-advancements.html.
2. Liu Q, Jin X, Cheng J, et al. Advances in the application of molecular diagnostic techniques for the detection of infectious disease pathogens (review). Molecular Medicine Reports. 2023;27(5):1-14. doi:10.3892/mmr.2023.12991.
3. Molecular diagnostics. Yale Medicine. Accessed May 12, 2025. https://www.yalemedicine.org/conditions/molecular-diagnostics. 
   3a. Expert opinion. Rob Thompson, Medix Biochemica. May 2025.
4. Protein engineering - an overview. ScienceDirect. Accessed May 12, 2025. https://www.sciencedirect.com/topics/nursing-and-health-professions/protein-engineering.
5. Qiu Y, Wei GW. Artificial intelligence-aided protein engineering: From topological data analysis to deep protein language models. ArXiv. arXiv:2307.14587v1.
   5a. Expert opinion. Anthony Austin, Medix Biochemica. May 2025.
6. dPCR vs qPCR. Qiagen. Accessed May 12, 2025. http://www.qiagen.com/us/applications/digital-pcr/beginners/dpcr-vs-qpcr.
7. Godmer A, Bigot J, Giai Gianetto Q, et al. Machine learning to improve the interpretation of intercalating dye-based quantitative PCR results. Sci Rep. 2022;12(1):16445. doi:10.1038/s41598-022-21010-z.
8. Burdukiewicz M, Spiess AN, Rafacz D, et al. PCRedux: A data mining and machine learning toolkit for qPCR experiments. 2021;4(1). doi:10.1101/2021.03.31.437921.
9. Dlamini Z, Skepu A, Kim N, et al. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. Informatics in Medicine Unlocked. 2022;31:100965. doi:10.1016/j.imu.2022.100965.
10. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Med Inform. 2020;8(7):e18599. doi:10.2196/18599.
11. Artificial intelligence in the laboratory. Lableaders. Diagnostics. Accessed May 22, 2025. https://lableaders.roche.com/global/en/articles/leveraging-artificial-intelligence-laboratory.html.
12. Al-Antari MA. Artificial intelligence for medical diagnostics—existing and future AI technology! Diagnostics (Basel). 2023;13(4):688. doi:10.3390/diagnostics13040688.
13. Yang J, Li FZ, Arnold FH. Opportunities and challenges for machine learning-assisted enzyme engineering. ACS Cent Sci. 2024;10(2):226-241. doi:10.1021/acscentsci.3c01275.
14. MedixMDx: Advanced molecular diagnostics. Medix Biochemica. Accessed May 13, 2025. https://www.medixbiochemica.com/mdx.