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Current Landscape of AI trends in Clinical Trials

In the last 10 years, Artificial Intelligence (AI) has emerged as a technological marvel which is expected to have a transformational impact on clinical research. Technological advances in recent years and especially the interest in AI, ML, etc., have created a new wave in the drug development areas, and are helping the pharma companies in using the information to develop drugs faster and with a greater chance of success in the early stages of drug development. The use of AI in healthcare has increased rapidly over the past few years, but much of the attention has been focused on the application of AI in healthcare, and applications of AI or ML, etc., in clinical research, are less frequently discussed.


As clinical research is a broad subject and although essential to enhancing healthcare and outcomes, clinical research that is now conducted is intricate, labor-intensive, and expensive. These factors might occasionally jeopardize the application, acceptance, and implementation of clinical research.Due to the confluence of numerous new digital data sources and the computational ability to find significant patterns in the data using effective artificial intelligence, the future of clinical development is about to undergo a significant transformation. Clinical trial efficiency, generalizability, and success could all be enhanced by artificial intelligence (AI). Numerous professionals in the field are still unsure of the nature and operation of these technologies. Many people are uncertain of how to gobeyond the obstacles in the way of utilizing these technologies.

AI Comprehension in Clinical Research

AI is defined as “an entity (or collective set of cooperative entities), capable of receiving inputs from the environment, understanding and learning from such inputs, and exhibiting related and flexible behaviors and actions that support the entity’s achievement of a specific desired outcome over time.” Artificial intelligence (AI) comprises several approaches, including machine learning (ML), deep learning (DL), natural language processing (NLP), and optical character recognition (OCR). To generate predictions or choices, AI is frequently utilized in the pharmaceutical sector to develop data analysis algorithms and mathematical models that extract characteristics from sample data. CROs are employing a range of AI technologies to bolster their place in the global market amid the “artificial intelligence (AI) revolution” in the pharmaceutical and healthcare industries.
To improve trial success rates and reduce the cost of pharmaceutical R&D, AI has the ability to revolutionize critical stages of clinical trial design, from study conception through execution.

Protocol Development and Study Design Optimization

The success of the clinical trial is significantly influenced by its design. The cost, effectiveness, and likelihood of success of clinical trials are all severely impacted by poor research design. In order to develop trial procedures that address scientific concerns, researchers must consider factors such as population size, duration of the study, and eligibility requirements. Historically, trial design has been entirely dependent on clinical trial designers’ prior expertise. AI algorithms may aid to automate trial design and play a role in maximizing trial success and efficiency during the planning phase by using simulation approaches to vast volumes of data from earlier trials to assist trial protocol designing.AI algorithms may extract structured data from trial papers, forecast trial results using multimodal trial-related real-world data (RWD), and assess how each trial protocol element affects trial results. The development of AI has made it possible to use multimodal RWD for precise trial result forecasts at the preliminary stages. Using data from clinical trials, trial outcome predictors offer a general success likelihood.

Real-world data, such as clinical trial procedures or academic articles describing clinical trial findings, is essential for streamlining study design. Trial records, however, are semi-structured documents that contain both structured and unstructured text as well as result tables. When semi-structured data is not yet converted into structured forms like SQL queries, most AI models are unable to handle it. AI technologies called Natural Language Processing (NLP) techniques are used to extract crucial information from unstructured materials. NLP may be used to better develop study procedures by analyzing databases of clinical trials and real-world data. Since improved research design resulted in more predictable outcomes, shorter protocol development cycles, fewer protocol modifications, and increased study efficiency overall.

AI’s role in clinical trial participant management: Increasing the speed of patient enrollment and recruiting
Clinical trial participant management entails selecting target patient populations, recruiting patients, and retaining participants. Ironically, even though substantial resources are often spent on participant management, comprising time, preparation, and trial coordinator efforts, patient drop-out and non-adherence frequently lead trials to go over budget, take longer than expected, or fail to yield data that can be used. Patient cohort selection and recruitment processes that fall short in bringing the most suitable patients to a study on time, as well as a lack of technological infrastructure to handle the complexity of operating a trial, are two of the major reasons why a clinical trial fails. Patient recruitment is one of the most difficult aspects of clinical trials, but by utilizing artificial intelligence (AI), researchers might greatly increase efficacy and safety while lowering expenses. Patients are usually made aware of study options by healthcare professionals, such as clinical trial coordinators. However, the time necessary for clinical personnel to analyze medical records, treatment histories, imaging investigations, and laboratory data to find appropriate clinical trials for patients can be a substantial barrier to clinical trial enrollment. Problems with subject matching and enrollment lead to extensions of recruitment deadlines, which postpone the submission of trial protocols for regulatory clearances and, as a result, the product’s introduction to the market beyond the dates originally anticipated. Furthermore, because underrepresented communities might not respond positively to the intervention, selection bias might produce results that aren’t generalizable.

The Food and Drug Administration (FDA) recognized the following ways in which AI models and methodologies might improve patient cohort selection: (i) by lowering population heterogeneity; (ii) by selecting subjects who are more expected to have a clinical endpoint that can be measured, also known as “prognostic enrichment,” and (iii) by finding a population that is more likely to react to therapy, also known as “predictive enrichment.”

Patient Monitoring, Adherence, and Retention

Recruiting suitable patients for a clinical trial requires a significant time and financial commitment. Only by successfully completing the trial the return on investment can be achieved. Therefore, it is crucial that participants in the study continue to do so, abide by the trial’s policies and guidelines, and ensure that all data needed to track the effects of the tested medicine are accurately and effectively recorded. Patients must maintain thorough records of their medicine consumption as well as several other data points concerning their bodily functions, drug reaction, and daily routines to meet adherence standards. Dropouts resulting from noncompliance with trial protocols need further recruiting efforts, which lengthen trial times and incur significant extra expenditures. The burden of adherence may be reduced, endpoint detection can be done more quickly, and dropout and non-adherence rates can be decreased by using enhanced patient care and coaching techniques throughout ongoing studies. With the use of ML, two general strategies may be used to increase subject retention and protocol adherence. The first is to utilize AI to gather and analyze massive quantities of data to detect and intervene in participants who are at high risk of not complying with the research. The second strategy involves applying AI to lessen the workload associated with participant studies and enhance participant experiences. AI techniques combined with wearable technology provide new avenues for constructing such power-efficient, mobile, real-time, and tailored patient monitoring systems. By regularly retraining the underlying analytical DL models with new measurement data, this method enables the creation of disease diaries that are patient-specific and adaptable to any changes in illness manifestation and patient behavior. These illness diaries might be used as proof of adherence or a lack thereof. They would also collect data points for endpoint detection more effectively and reliably than existing patient-driven self-monitoring techniques since little or no human patient input is needed.

Data collection and management

The methods needed for data collection, management, and analysis may alter as a result of the application of AI in clinical trials. However, some of the challenges related to gathering real-world data and dealing with missing data can be addressed with the use of AI techniques. In some cases, patient-generated health data through wearable and other mobile/electronic devices can augment or even replace research visits and related traditional data gathering. Wearables and other technologies may make it possible to validate and apply novel, patient-centered biomarkers. An appealing application of AI, specifically natural language processing (NLP), to study data management is to automate data collection into case report forms, reducing the time, expense, and potential for error associated with human data extraction, whether in prospective trials or retrospective reviews.AI may also be used to process data. Semiautomated endpoint identification and adjudication have the potential to reduce time, cost, and complexity when compared to the current approach of manual event adjudication by a committee of clinicians, because, whereas endpoint adjudication has traditionally been a labor-intensive process, sorting and classifying events falls well within the capabilities of ML. AI may also be utilized to solve the problem of missing data in a variety of ways, including several sources of data missingness, data-related assumptions and goals, and data collecting and intended analytic methodologies. A clinical trial, registry, and clinical practice data are rich sources for hypothesis creation, risk modelling, and counterfactual simulation, and ML is ideally suited for these endeavors.

Clinical Trials in the Future: Not Just AI

Artificial intelligence (AI) won’t take the place of human ingenuity. AI cannot conduct trials independently or make scientific discoveries; it is not a panacea. This technology cannot, and is not intended to, replace scientists. It only exists to assist drug development specialists by offering an understanding of both the queries and responses that the technology facilitates us to ask. On the one hand, AI and machine learning will need to play a significant role if we are sincere about transforming how we discover and develop new drugs. As more individuals utilize these technologies to improve their lives, AI and machine learning will become more and more significant in not only clinical trial research and development but also other facets of health and wellness.

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