AI does not just make clinical trials faster; it makes them smarter

What are the major advancements in drug development research? What are the opportunities for India in the current landscape? 

Drug development research has made remarkable progress globally, with India emerging as a key contributor. According to Deloitte, India’s pharmaceutical research and development (R&D) market is estimated to expand to $130 billion by 2030, and the country is considered the third-largest global producer of drugs by volume (1). 

The biggest shift we are seeing in drug development is the introduction and increasing adoption of advanced technologies, such as artificial intelligence (AI), data analytics, telemedicine, and wearables and sensor technologies. While still relatively in the early stages in India compared to other markets, we are already seeing great appetite on the ground and tremendous opportunities to revolutionise drug development research. Benefits of AI include accelerating insights by processing colossal amounts of data within seconds, a number that would take humans years to process, while telemedicine and wearables enable hybrid and decentralised trials which make them more accessible to participants.

What are the challenges in the current clinical trial landscape in India? How can they be tackled?

While the talent pool has grown, the industry still grapples with skill gaps and high attrition rates of around 30 per cent, higher than the global average of 12 per cent (1). One way to tackle attrition is through the incorporation of technology that will enable talent to do their work more efficiently, thereby helping to mitigate stress and burnout. For example, AI can help automate time-consuming and traditionally manual tasks such as the collection, organisation, and analysis of data, minimising human error and providing real-time insights.

Another challenge is patient diversity, locally in India and globally. Despite its large population, India contributed about 4 per cent to global clinical trials annually from 2010 to 2022, with just 3 per cent of global trial participants coming from the country, compared to 30 per cent in the US (2). There is also a significant patient population in tier 2 and tier 3 cities in India (1)  which remains untapped when it comes to clinical research. 

The reason patient recruitment has been challenging is because of barriers such as limited awareness of clinical trials, fear of side effects, and concerns about exploitation and trust in the healthcare system (3). This must be addressed, as participants in clinical trials should ideally be representative of the groups of people who will use the therapy, as drugs can react differently to genetics and biological factors.  

To help overcome some of these barriers, sponsors and contract research organisations (CROs), including hospitals, should prioritise trial designs that create more welcoming experiences for all patients by selecting study sites trusted by underrepresented communities. Local doctors should also play a role in helping to raise awareness of clinical trials to their patients, as well as educating them and alleviating any fears, concerns or misconceptions they might have. 

Technology and AI can also be part of the solution. On one hand, they empower researchers to identify and select sites that are more likely to enroll diverse patients, including those from underserved communities, while on the other, they allow patients to join trials remotely, improving convenience and accessibility, especially for those in the smaller cities.

How can AI help accelerate drug development? And at which stages of a clinical trial can it be particularly useful? How can AI integration bring improved efficiencies into the whole process?

Drug development has traditionally been a long, complex, and costly process, often taking over a decade to bring a new therapy to market. However, AI is reshaping this landscape by boosting efficiency and accelerating every stage of the clinical trial process from early drug discovery to data analysis and decision-making. The integration of AI and machine learning could lead to the discovery of 50 novel treatments within the next decade, according to Morgan Stanley Research, and AI could potentially reduce the time required for identifying viable drug candidates to just one to two years.

One of the most time-consuming aspects of clinical trials is patient recruitment. AI holds transformative potential in clinical trial recruitment by utilising Real-World Data (RWD) to identify qualified participants (4). By analysing huge datasets, including historical clinical trial data and electronic health records, AI can significantly shorten recruitment timelines by uncovering patterns that are difficult to detect manually and through targeted outreach. Often seen as a virtual assistant, it can also analyse site-specific recruitment rates from past trials to predict which sites are most likely to recruit effectively for future studies.

There are multiple areas where AI is used during clinical trials to improve efficiencies and outcomes, from remote patient monitoring to accelerating insights, and predictive modelling. In the context of clinical trial imaging, for instance – with over 50 per cent of trials using medical imaging and 95 per cent of all oncology trials using it – AI has proven essential in efficiently handling large volumes of data and managing high levels of complexity. AI does not just make clinical trials faster; it makes them smarter, enabling more targeted therapies and improving patient outcomes.

Medidata has entered into strategic partnerships such as the tie-up with Cognizant, and the collaboration with NIHR. How will these tie-ups translate into added value for its partners?

We have strong collaborations with partners around the world to enhance clinical trial efficiency and quality at every stage, ultimately helping to bring new treatments to the market faster. For instance, in January 2025, we expanded our agreement with Cognizant to enhance responsiveness and improve user satisfaction for life sciences organisations that rely on Medidata’s technology services for clinical trial development. This month, we also announced that we have integrated data from the UK interactive Costing Tool (iCT), hosted by the National Institute for Health and Care Research (NIHR), into Medidata Grants Manager, creating a first-of-its-kind offering in the life sciences space. This will consolidate study budgeting with consistent costs, coding, and processes.

Such collaboration with stakeholders is crucial for India clinical trials as they grow larger, more complex, and globally distributed. To help our partners and customers keep up with the evolving landscape, we provide comprehensive solutions that break down silos and provide real-time, data-driven insights. 

As emerging technologies transform the clinical research landscape, how should professionals in this sphere keep pace with these advancements? How can Medidata help create a pool of skilled professionals for clinical research?

Emerging technologies, such as AI, are transforming clinical trials. However, it also comes with uncertainties and fears, including around data privacy and security, which can be a barrier to adoption. 

Our recommendation to help professionals keep pace with such advancements is to build a foundation of clarity and understanding through a four-pillar upskilling approach. When it comes to AI, it could include the following: 

• Increase literacy: To increase literacy in and understanding of AI, provide teams with learning programs and a knowledge base infrastructure, interactive learning with examples, an understanding of the iterative nature of AI, and a way to identify new business cases and support evolution. 
• Put clinical data managers in the loop: To make sure that clinical data management teams feel like they’re in control of how AI is used, ensure that feedback expectations are clear, that the impact of the AI feedback loop is understood, and that advantages and shortcomings are understood (e.g., insufficient or low quality of data and bias). 
• Manage change: To achieve the successful adoption of AI, focus on changing mindsets, eliminating fears of the unknown and job losses, increasing comfort levels through training, and building confidence by selecting champions. 
• Validation: To reduce the potential bias in AI, emphasise the need for human-in-the-loop adjudication, test across multiple subsets of data, perform sensitivity tests, use real-world evidence for validation, and work with regulators. 

We believe that transformation through AI, once embraced, takes clinical trials to a whole new level. Beyond developing innovations, we also provide training courses and certifications to upskill and empower customers and partners to keep up with industry and technology advancements. 

What are Medidata’s business growth plans for India? Are there any plans for collaborations on the horizon?

In India, we have tailored our strategy to capitalise on unique local opportunities, such as growing investments in R&D and the increased adoption of advanced technologies such as AI-enabled healthcare solutions and tools (5).

To support our local customers, we are committed to offering comprehensive and integrated solutions across every step of a trial, from start to finish. For instance, our cloud-based Medidata Platform enables timely and informed decision-making, improving trial outcomes, while our AI solutions and advanced wearable and sensor technologies meet the increasing need to go digital, patient centricity and personalised study designs. Our culture of innovation also enables us to quickly adapt to a rapidly evolving healthcare environment, while still focusing on digitalising and accelerating clinical trials.

While India’s technology-powered drug discovery and development market is growing, it also faces challenges that must be addressed. This includes building trust in technology and AI, and addressing data privacy and cybersecurity concerns, which requires a whole-of-industry approach. We work with customers and partners to move towards a patient-centric approach and to find creative new ways to develop trust so that patients are increasingly willing to share their healthcare data with researchers to create an ecosystem of new discovery that is mutually beneficial to both. 

 

References

•  Deloitte (2024), Positioning India as a Pharmaceutical Innovation Hub, https://www2.deloitte.com/content/dam/Deloitte/in/Documents/life-sciences-health-care/in-lshc-positioning-india-as-a-pharmaceutical-innovation-hub-noexp.pdf

• PwC (2023), Clinical Trial Opportunities in India https://www.pwc.in/assets/pdfs/consulting/management-consulting/clinical-trial-opportunities-in-india.pdf

•  Journal of Clinical and Diagnostic Research (2024), Bridging the Divide: Tackling Recruitment Challenges in Indian Clinical Trials: A Narrative Review  https://www.jcdr.net/articles/PDF/19608/70716_CE%5bRa1%5d_F(IS)_QC(RD_RDW_IS)_PF1(AKA_SL_OM)_PFA(AKA_KM)_PN(KM).pdf

• Forbes (2024), How The Healthcare Industry Can Use AI In Clinical Trial Recruitment https://www.forbes.com/councils/forbesbusinesscouncil/2024/09/30/how-the-healthcare-industry-can-use-ai-in-clinical-trial-recruitment/

•  Deloitte (2024), Positioning India as a Pharmaceutical Innovation Hub https://www2.deloitte.com/content/dam/Deloitte/in/Documents/life-sciences-health-care/in-lshc-positioning-india-as-a-pharmaceutical-innovation-hub-noexp.pdf

kavita.jani@expressindia.com 

kavitanjani@gmail.com