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Digital technology is driving efficiency in clinical trials. Since the traditional clinical trial process is often time-consuming and costly, digital health technologies are driving efficiency, opine some experts.
As India is emerging as a hub for clinical trials, thanks to its large, diverse population, Indian clinical research companies are increasingly adopting high-tech solutions to enhance patient recruitment and improve outcome predictions. By leveraging advanced technologies such as artificial intelligence, machine learning, and big data analytics, these companies can target patient recruitment and machine learning models can forecast outcomes by analyzing historical data, helping researchers assess the likely success of clinical trials and optimize study designs.
Artificial intelligence and machine learning are being used to streamline patient recruitment, identify suitable candidates based on health data, and predict outcomes more accurately. Moreover, decentralized trials, enabled by remote monitoring tools, allow broader participation, reduce geographic barriers, and speed up the process, according to Nandita Saini, associate vice president, engineering, GlobalLogic.
As the country grapples with challenges of access, affordability, and infrastructure, innovative digital health solutions are emerging as game-changers poised to reshape the healthcare ecosystem. We recognize the pivotal role technology plays in addressing these challenges and unlocking new possibilities for enhanced patient outcomes, remote care, and more efficient clinical trials, Saini said.
Our country’s healthcare landscape is complex, characterized by a vast urban-rural divide in access to care, a shortage of medical professionals, and varying degrees of infrastructure readiness. Moreover, affordability remains a significant concern for most of the population. Yet, the integration of digital technologies like telemedicine, artificial intelligence and big data analytics is offering solutions to these pressing challenges, she said.
Even as telemedicine has gained significant traction, the AI-driven chatbots and virtual assistants enhance patient interactions, provide initial diagnostics, and offer advice, thereby reducing the load on healthcare professionals. AI can identify trends and predict potential health risks by analyzing vast amounts of patient data. For example, AI tools are being used to detect early signs of diseases such as diabetes and cardiovascular conditions, which are prevalent in India. Additionally, AI-driven diagnostic tools reduce the time needed for medical imaging analysis, enhance accuracy, and make critical diagnostic services more widely accessible, said Saini.
Big data analytics is also crucial in personalized medicine. By using genetic and lifestyle data, clinicians can develop more targeted and effective treatment plans tailored to individual patients, addressing a major gap in the one-size-fits-all approach to treatment.
The ability to provide remote care is not just a convenience but a necessity in a country where nearly 70 per cent of the population resides in rural areas. Mobile health (mHealth) applications, telehealth platforms, and connected devices are enabling healthcare providers to monitor patients’ conditions in real-time, reducing the need for frequent hospital visits and allowing for timely interventions. This shift is especially critical in managing chronic diseases, where ongoing monitoring is key to preventing complications. The integration of wearable technologies is further enhancing patient outcomes.
Looking forward, we see several emerging trends that will further shape the future of healthcare in India. AI-driven diagnostics will continue to evolve, offering even more precise and rapid analysis. Wearable tech will become more sophisticated, providing continuous monitoring and enabling predictive healthcare. Additionally, personalized medicine will become more widespread, leveraging AI and genetic data to tailor treatments to individual patients, said Saini adding that GlobalLogic too is ensuring that these advancements are not only accessible but also transformative for the Indian healthcare ecosystem.
Novel technologies reshaping clinical trial landscape
Adoption of novel technologies like virtual and decentralized trials, artificial intelligence (AI), and real-world data are reshaping the India’s clinical trials landscape, said Sanjay Vyas, executive vice president and managing director, Parexel.
Decentralized trials are leveraging digital technologies to enhance patient access and participation, enabling remote monitoring, telemedicine consultations, and electronic consent processes, he added.
There is an increasing focus on oncology clinical trials due to rising cancer prevalence and the need for innovative therapies. Also, private hospital networks are diversifying into tier 2/3 cities, providing broad access to patients, he said.
The geopolitical landscape is shifting in a way that could make India an increasingly attractive destination for pharmaceutical companies and researchers worldwide. Government initiatives like the Foreign Direct Investment (FDI) and production linked incentive (PLI) plan are creating opportunities for India, Vyas said.
India has emerged as a significant player in the global clinical trials landscape, with its market valued at US$ 2.05 billion in 2024 and projected to grow at a compound annual growth rate (CAGR) of 8.64 per cent through 2030. This growth is fuelled by several factors that make India an attractive destination for pharmaceutical companies and researchers worldwide. The vast and diverse patient population provides an ideal environment for clinical research. The demographic diversity allows for more comprehensive and representative studies, improving the generalizability of results. Additionally, the country’s cost-effective operational environment and large pool of skilled medical professionals contribute significantly to its appeal. The pool of potential participants also makes India an attractive destination for various therapeutic areas, particularly oncology, diabetes, and infectious diseases, he noted.
There is significant growth in phase 2 and 3 trials which are growing 15-18 per cent annually. There is also a growing prevalence of lifestyle diseases like diabetes and cardiovascular diseases driving demand for clinical trials. Regulatory advancements through amendments to the New Drugs and Clinical Trials (NDCT) Rules in 2019 are streamlining approval processes and introducing compensation provisions.
Ensuring ethical concerns and patient safety, particularly regarding informed consent and the protection of vulnerable populations, is critical for maintaining public trust and research integrity. Here we are seeing that India’s clinical trials industry faces several interconnected challenges that impact its growth and effectiveness.
A significant lack of public awareness about clinical trials hampers recruitment; many potential participants are unaware of their rights or the benefits of participation in India. Educational initiatives are essential to enhance understanding and encourage involvement, said Vyas.
Furthermore, certain regions suffer from infrastructure and skill deficits, necessitating strategic investments in healthcare facilities and capacity-building programs. While regulatory improvements have been made, consistent compliance across diverse research settings remains a challenge. As a final point, maintaining high standards of data quality and integrity across various trial sites is crucial for the credibility of research outcomes. Addressing these challenges holistically will be vital for the advancement of clinical trials in India, said the Parexel India chief.
NIH-developed AI algorithm matches potential volunteers
Meanwhile according to a report, researchers from the National Institutes of Health (NIH) have developed an artificial intelligence (AI) algorithm to help speed up the process of matching potential volunteers to relevant clinical research trials listed on ClinicalTrials.gov.
A study published in Nature Communications found that the AI algorithm, called TrialGPT, could successfully identify relevant clinical trials for which a person is eligible and provide a summary that clearly explains how that person meets the criteria for study enrolment. The researchers concluded that this tool could help clinicians navigate the vast and ever-changing range of clinical trials available to their patients, which may lead to improved clinical trial enrolment and faster progress in medical research.
A team of researchers from NIH’s National Library of Medicine (NLM) and National Cancer Institute harnessed the power of large language models (LLMs) to develop an innovative framework for TrialGPT to streamline the clinical trial matching process. TrialGPT first processes a patient summary, which contains relevant medical and demographic information. The algorithm then identifies relevant clinical trials from ClinicalTrials.gov for which a patient is eligible and excludes trials for which they are ineligible. TrialGPT then explains how the person meets the study enrolment criteria. The final output is an annotated list of clinical trials—ranked by relevance and eligibility—that clinicians can use to discuss clinical trial opportunities with their patient.
“Machine learning and AI technology have held promise in matching patients with clinical trials, but their practical application across diverse populations still needed exploration,” said NLM Acting Director. “This study shows we can responsibly leverage AI technology so physicians can connect their patients to a relevant clinical trial that may be of interest to them with even more speed and efficiency.”
To assess how well TrialGPT predicted if a patient met a specific requirement for a clinical trial, the researchers compared TrialGPT’s results to those of three human clinicians who assessed over 1,000 patient-criterion pairs. They found that TrialGPT achieved nearly the same level of accuracy as the clinicians.
Additionally, the researchers conducted a pilot user study, where they asked two human clinicians to review six anonymous patient summaries and match them to six clinical trials. For each patient and trial pair, one clinician was asked to manually review the patient summaries, check if the person was eligible, and decide if the patient might qualify for the trial. For the same patient-trial pair, another clinician used TrialGPT to assess the patient’s eligibility. The researchers found that when clinicians use TrialGPT, they spent 40 per cent less time screening patients but maintained the same level of accuracy.
Clinical trials uncover important medical discoveries that improve health, and potential participants often learn about these opportunities through their clinicians. However, finding the right clinical trial for interested participants is a time-consuming and resource-intensive process, which can slow down important medical research.
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