Has artificial intelligence left your industry undisrupted? What if we say no? While AI is drastically penetrating across diverse fields, remarkably turning innovations, the pharmaceutical space is also witnessing the best. Artificial intelligence in clinical trials has been contributing to stellar research outcomes in the last couple of years. When probed into recent studies, the talk-of-the-town technology has benefited predictive analysis by achieving 85% accuracy in forecasting trial outcomes. Source. Further, relevant research has also demonstrated that pacing up trial timelines has significantly reduced clinical trial costs.
As prodigious as it sounds, inventing artificial intelligence in clinical research requires expertise. In this blog, let’s look at how AI can be a part of clinical trial stages to achieve more impactful drug development and also increase patient safety.
What is Artificial Intelligence in Clinical Trials?
AI in clinical trials is deploying algorithms to read volumes of drug trial data sets, perform data analysis, and optimize trial designs. The use of artificial intelligence in the clinical research process leads to enhanced decision-making processes with drug development and reduces errors, leading to prudent trial outcomes.
Typically, in clinical trials, Artificial Intelligence is introduced at multiple phases.
AI Across the Clinical Trial Lifecycle:
Pre-Trial - Patient Recruitment, Trial Design & Protocol Optimization
In the pre-trial phase, a trial design that’s most likely to succeed is determined. This is achieved by simulating trial outcomes using machine learning models and AI. Adaptive trials can also lead to accelerating the trial process, giving precise outcomes. It is the protocol that defines each clinical research methodology, like patient selection, data collection, storage, and security procedures. AI streamlines these protocols by simulating the trials for a successful drug discovery. Further, AI-driven trial site selection has also resulted in achieving better patient demographics.
During the Trial - Real-time Monitoring & Data Capturing
Artificial Intelligence is used in clinical research during trials to monitor real-time patient data and predictive analytics for trend forecasts. Natural Language Processing (NLP) is used to capture data from complex sources, flag latencies, and ensure data compliance during the trial.
Post Trial - Analysis & Regulatory Reporting
Meeting regulatory expectations and patient safety in the post-trial phase is inevitable for successful clinical research. Here, pharma giants use AI in report generation and submission to make the trial faster, reliable, and safer.
Building the Data Backbone: How Data Pipelines Enable Smarter Clinical Operations
Across stages, data collection is rudimentary and builds a foundation for a successful AI application in clinical research. Data is derived from both structured and unstructured sources like electronic health records (EHRs), lab results, and doctors’ notes., AI in clinical data management uses predictive analysis and NLP to go over vast and unstructured data sets from these sources, and identify the right bunch of patients that meet the clinical trials criteria. This approach reduces the time taken for patient recruitment and matches them more precisely with trial requirements.
Managing the data and making sense of it is crucial for the system throughout the trial. Organizing these data pipelines using AI can lead to nifty data analysis when the new data comes in.
What is a Data Pipeline?
A data pipeline is a system that fetches and mobilizes data across stages. It takes data for storage, analysis, or reporting based on the need. The process starts with ingesting both structured and unstructured data from umpteen sources as mentioned above - EHRs, lab results, patient records, etc. These data are processed, filtered, and refined before being pushed into a data warehouse.
Challenges in Building a Data Pipeline
We just saw a use case around dealing with both structured and unstructured data during the pre-trial phase. For any pharma company to process terabytes of data, it takes a scalable infrastructure architecture with absolutely no compromise to performance.
When there are tons of data to be extracted and processed, ensuring data security goes out the window. Organizations have prioritized migrating to cloud pipelines due to a surge in cyber crimes in the healthcare industry, as the cost of responding to cyber attacks is sky-high. For instance, Change Healthcare Inc. spent nearly $3.1 billion earlier this year to respond to a data breach of 190 million people that happened in 2024. This emphasizes the need for stronger encryption and data security monitoring tools. Source.
Dealing with data latency might lead to errors during real-time processing that can affect the data pipeline performance.
Building robust data systems favors real-time monitoring that drives better clinical operations and decision-making based on reliable outcomes.
Best Practices to Build Effective Data Pipelines
Understanding your data requirements well and choosing the right tool or partner who will help build it is crucial. At Tredence, we’ve been a part of successful data and AI ecosystems for established healthcare and pharmaceutical companies. Choosing the right data engineering partner will solve your other data challenges like data quality and pipeline performance.
Predictive Modeling in Clinical Research: Enhancing Trial Efficiency and Outcome Forecasting
Predictive Modelling is an AI for clinical trials built using predictive analytics and machine learning to assess data continuously and predict outcomes.
Imagine a bunch of patients are taken into trial for testing a wearable that identifies and predicts high-risk conditions like diabetes, cancer, or cardiovascular diseases. A better outcome with such trials can reduce healthcare costs and sometimes save lives. By implementing this, a healthcare business can progress towards value-based care.
Within a trial setup, a predictive model allows researchers to modify and streamline the trial design, enrich data during the analysis process upon real-time monitoring, ultimately driving trial efficiency.
Key Applications of AI in Clinical Trials
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Integrating AI in modern medicine has been proven in a noble-prize Prize-winning discovery in 2024. Researchers Demis Hassabis, John Jumper from Google DeepMind, and David Baker from the University of Washington have been awarded the prestigious Nobel Prize in Chemistry for a neural network AI program called AlphaFold2. The discovery program was designed for predicting complex protein folding patterns and the creation of new proteins. Source.
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Treatment inefficiency is a common problem with healthcare. While multiple factors contribute to the lack of efficient treatment, predictive modelling can be used to influence a patient's biomarker correlation with treatment efficiency. In a cancer drug discovery, biomarkers are graded based on the protein presence in the enzymes, hormones, and antigens. Altering cancer genes through mutation can contribute to this categorization of biomarkers, aiding in a better understanding of the treatment. Implementing predictive modelling can determine how specific a treatment can be based on the patient's history and pathological conditions.
In the above scenario, in a pre-trial phase, patient selection, safety, and monitoring can be improved using predictive analytics and modelling. The use of AI is also advantageous in the post-trial phase when the drug is approved and hits the market for consumption.
Benefits of Artificial Intelligence Adoption in Clinical Trials: Speed, Precision, and Cost Efficiency
ML and AI for clinical trials have been revolutionary in the drug discovery space. Here are some of the benefits of adopting Artificial Intelligence in clinical research.
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Speed:
Finding the right set of patients and their data to perform a trial is fundamental to any clinical research. Increased trial efficiency across stages can be achieved, especially when there’s AI in the patient recruitment process. AI Algorithms have helped expedite this process by matching patient data with the trial requirements.
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Precision:
AI algorithms have been remarkably predicting malicious diseases like cancer with a 95% early detection rate. Skin cancer is one among them. Using deep learning that focuses on quantitative learning methods like Convolutional Neural Networks (CNN), researchers can enhance the accuracy of the study. Source
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Cost Efficiency:
With a high mortality rate, Hepatocellular Carcinoma (HCC) is the world's third most lethal cancer disease. For this type of cancer, Cirrhosis is a major contributing factor in 90% of the patients. Studies were conducted on improving the cost-effectiveness of HCC using AI in MRI and Morkov Model Decision Tree. The lifetime costs and Quality-Adjusted Life Years (QALY) have been simulated in liver cirrhosis patients affected by HCC. Post the usual care, the lifetime costs per 1000 patients have been reduced to 5000 Euros when compared to patients receiving care without an AI approach. Source
Ensuring Data Integrity, Security, and Compliance in AI-Driven Clinical Pipelines
Data integrity, security, and compliance stand at the forefront of AI-driven clinical pipelines. As predictive and machine learning models process vast datasets, ensuring the accuracy of patient data and its secure management is paramount.
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Data Integrity Measures:
Defining data quality standards, access controls, and roles in managing data sets ensures data integrity in clinical trial processes.
Constant auditing of historical data and data training can help prevent AI from bias mitigation. This is crucial as artificial intelligence uses real-time data monitoring in the clinical trial stages.
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Data Security Measures:
End-to-end Encryption, role-based authentication Control, and Multi-Factor Authentication are some of the fundamental security measures. Network-based security segmentation for isolated development, training, and deployment environments can limit the potential for intruders.
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Compliance
All AI data pipelines must adhere to both national and international regulations to avoid financial and legal penalties.
HIPAA (U.S.): Safeguard all Protected Health Information (PHI) with physical, technical, and administrative measures. Get a Business Associate Agreement (BAA) authorization from third-party vendors who can skillfully handle the PHI.
GDPR (European Union): Ensure lawful data processing with patient consent; Uphold data subject rights and implement purpose limitation principles around the data used in each trial.
FDA & EMA: Follow the Food & Drug Administration (FDA) and European Medicines Agency (EMA) guidelines to ensure the safety and quality of drugs and medical devices used and operated in clinical research.
Challenges in Scaling AI Across Clinical Development Programs
Integrating artificial intelligence into clinical trials development presents several hurdles.
Addressing these challenges is crucial for pharmaceutical companies aiming to harness AI’s powerful tools for successful trial outcomes.
The Future of Clinical Trials
The future of clinical trials is positioned to be interlaced with artificial intelligence. Highly predictive, automated, and data-centric ecosystems that are governing the research process across pre, during and post-trial stages can be expected to come to application. In terms of tech advancements, building digital twins for patient virtual models will be the next big leap in testing new drugs. Research has been progressing around having digital twins for randomized clinical trials (RCTs) to elevate the trial standard, safety, informed patient consent, and data privacy. Moreover, having digital twins can reduce animal use in trials, which again proves it to be expedient. Source.
Final Thoughts
The use of AI in clinical trials is evidently transforming the drug discovery process. Artificial intelligence adoption in the clinical trial process can be scaled by setting up seamless data pipelines, employing predictive analytics, and building predictive models. Embracing technology is key to staying ahead in the evolving landscape for pharmaceutical companies.
At Tredence, we’ve assisted healthcare and life sciences companies by building accelerators that address data quality checks and create data models for data flow journeys. Get in touch with us to enhance the use of Artificial Intelligence in clinical trials through data accelerators.
Frequently Asked Questions
What is artificial intelligence in clinical trials, and how does it improve the research process?
Artificial intelligence in clinical trials is deploying algorithms to read volumes of drug trial data sets, perform data analysis, and optimize trial designs. The use of AI in the clinical research process leads to enhanced decision-making processes with drug development and reduces errors, leading to accurate trial outcomes.
How is AI used across different stages of clinical trials?
AI is utilized at various stages of clinical trials - pre-trial, during the trial, and post-trial stages.
Pre-Trial - Patient Recruitment, Trial Design & Protocol Optimization
During the Trial - Real-time Monitoring & Data Capturing
Post Trial - Analysis & Regulatory Reporting
What are the main benefits of using AI in clinical research and drug development?
The key benefits of AI in clinical research and drug development are to speed up the trial process, achieve precision in trial outcomes, and improve cost efficiency. The secondary benefits from using AI in clinical trials vary, like analyzing volumes of data sets quickly, identifying the right patients and matching them with trial requirements, and reducing the risk of trial failure.
How do predictive models help improve trial accuracy and patient outcomes?
Predictive models use machine learning and predictive analytics to analyze vast amounts of data. This data analysis helps with patient recruitment, forecasting patient outcomes, identifying potential risks, and optimizing trial designs. As a result, trials become more accurate, and treatments can be better tailored to improve patient health.
What does the future of AI in clinical trials look like by 2026 and beyond?
The future of AI in clinical trials is stepping into more automation. A wider use of AI in predictive modelling and digital twins for simulation in trials can expedite the process, making outcomes more prudent and also increasing patient safety than before.
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Editorial Team
Tredence
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