Artificial Intelligence and Machine Learning Archives

Agentic Autonomy: How Multi-Agent Systems Could Orchestrate the Future of Clinical Development

In recent years, artificial intelligence has evolved beyond basic pattern matching to become capable of autonomous reasoning, multi-step planning, and even delegation. This transition — from passive tools to goal-driven, reasoning agents — marks the rise of agentic AI.

For the life sciences sector, and especially clinical development, this evolution arrives at a critical time. Clinical trials are increasingly complex, cross-functional, and data-intensive. Agentic AI offers not just faster tools, but the possibility of autonomous collaboration — teams of agents working in harmony to reduce burden, increase efficiency, and shorten timelines.

Here we explore the evolution of agentic AI and how higher levels of autonomy could transform clinical development from reactive execution to proactive, intelligent orchestration.

The evolution of agentic AI

Agentic AI evolves through distinct levels of capability. Each stage unlocks new functionality — from static models to ecosystems of communicating agents. Here’s a clear breakdown of the five major levels:

Each level builds toward intelligent autonomy. The transition from Level 3 to Levels 4 and 5 introduces intentional behavior, goal-setting, and inter-agent collaboration — the foundations of autonomous operations in clinical development.

Agentic AI in clinical development: A new operating model

Clinical development is not just complex — it’s interdependent. Every milestone relies on the seamless handoff and integration of data, code, documents, and decisions. Agentic AI, particularly at Levels 4 and 5, promises to re-architect this model.

Level 4: Planning and reasoning agents

These agents can independently break down goals, design execution paths, and adapt to changing environments. Here’s how they can drive value:

Medical writing agents
- What they do: Generate drafts for protocols, CSRs, and patient narratives.
- How they help: Understand document structures, integrate real-time data, and adapt language for regulatory or clinical audiences.
- Outcome: Faster document turnaround, reduced rework, and scalable writing support.

Statistical programming agents
- What they do: Develop and validate analysis code in SAS, R, or Python.
- How they help: Plan logical sequences, debug outputs, and dynamically update based on protocol amendments.
- Outcome: Accelerated code generation with built-in quality assurance.

Information synthesis agents
- What they do: Retrieve and synthesize information from multiple domains — scientific literature, regulatory guidelines, real-world data, health system policies, and reports on unmet medical needs.
- How they help: Prioritize and contextualize inputs to support clinical design, indication selection, and risk-benefit assessments.
- Outcome: Broader strategic alignment and better-informed cross-functional planning.

Level 5: Multi-agent systems

At this level, clinical development becomes an ecosystem of agents, each with a specialized role, working under the coordination of orchestrator agents that function like project managers.

Orchestrator agents
- What they do: Assign tasks, monitor progress, and realign workflows in real time.
- How they help: Adjust deliverables dynamically as inputs change or downstream agents complete their tasks.
- Outcome: Continuously managed, self-optimizing trial execution.

Agent networks
- Example: A data management agent processes raw datasets and hands outputs to a statistical agent, which triggers a writing agent to draft updated narratives — all autonomously.
- Value: End-to-end automation with minimal human handoffs.
- Outcome: Real-time trial updates and agility under pressure.

The benefits of the agent ecosystem

From automation to autonomy

Agentic AI reflects an evolution from “AI that assists” to “AI that takes initiative” — supporting actions, learning from experience, and extending expertise across domains. In clinical development, where complexity continues to rise and efficiency is critical, this shift offers a meaningful opportunity rather than just an advantage.

As we look toward Levels 4 and 5, we can imagine a future where trials increasingly manage themselves, where teams are supported by networks of intelligent agents, and where human professionals gain more space to focus on innovation, thoughtful oversight, and meaningful patient outcomes.

Meet with us at ISPOR 2025!

Manuel Cossio will be in Glasgow for ISPOR Europe 2025! Click the link below to book a meeting, or stop by Booth #1024 to connect with our experts:

Redefining Clinical Documentation in the Age of Intelligent Collaboration: The Rise of the AI-Assisted Medical Writing Strategist

The introduction of AI into medical writing workflows marks a pivotal turning point in clinical development. As life sciences companies deploy AI agents to generate clinical documents — from clinical study protocols (CSPs) together with the Statistical Analysis Plan (SAP) to clinical study reports (CSRs) — a new role is emerging: the AI-assisted medical writing strategist.

This role represents a shift in mindset and skillset. No longer is the medical writer just a document author; they are becoming a strategic orchestrator of AI tools, data-driven narratives, and regulatory precision.

What is an AI-assisted medical writing strategist?

An AI-assisted medical writing strategist is a clinical and regulatory expert who partners with AI systems to accelerate and optimize the development of clinical documents. They bring together deep scientific understanding, regulatory knowledge, and technical fluency to co-create documents that are not only accurate and compliant but also delivered at unprecedented speed.

They are not just reviewing AI outputs — they are shaping the way AI generates those outputs, continuously fine-tuning the interaction between human judgment and machine efficiency.

Core pillars of the strategist role

The AI-assisted medical writing strategist role is defined by the following five key pillars:

1. AI orchestration, not just review

At the heart of the strategist’s work is the ability to guide AI systems toward producing high-quality, usable first drafts. This means:

Designing intelligent prompts based on document type and trial context.
Structuring modular content frameworks that AI can populate and iterate on.
Embedding company-specific style guides, preferred language, and regulatory templates into AI workflows.

2. Scientific and regulatory oversight

Even with AI generating drafts, clinical development demands nuanced, evidence-based interpretation. The strategist ensures:

Scientific rigor in efficacy and safety narratives.
Consistency in data interpretation across documents.
Adherence to ICH, FDA, EMA, and country-specific requirements.

AI might know the rules, but the strategist knows the exceptions, the subtleties, and the evolving guidance that govern every submission.

3. Training the AI with human expertise

AI systems improve through feedback. Strategists:

Curate and label high-quality training datasets (e.g., past CSRs, protocols).
Correct and comment on AI-generated drafts to reinforce preferred structures and content styles.
Continuously evaluate model performance and guide retraining cycles.

They act as domain-informed teachers, helping the AI become a better writing partner over time.

4. Cross-functional bridge builder

Medical writing is inherently collaborative. The strategist aligns AI output with expectations from:

Clinical, data management, and statistical teams.
Regulatory affairs and quality assurance.
Legal, ethical, and patient advocacy groups.

In doing so, they help organizations reimagine review cycles, moving from linear drafting to agile co-creation.

5. Champion of ethics and transparency

AI is powerful — but it must be used responsibly. Strategists play a leading role in:

Ensuring AI doesn’t fabricate data or misrepresent study outcomes.
Clarifying where automation was used in document creation.
Promoting transparency, reproducibility, and compliance in every AI-assisted process.

Why this role matters

The volume and complexity of clinical documentation are only increasing. At the same time, timelines are shrinking, budgets are tightening, and regulatory scrutiny is rising. AI offers a way forward — but only when guided by human intelligence.

The AI-Assisted Medical Writing Strategist ensures that automation enhances human value rather than diminishing it. They unlock:

Faster turnaround times for key deliverables.
More consistent documentation across global studies.
Greater focus on high-value tasks like interpretation, innovation, and communication.

How to prepare for this role

Transitioning into this role requires new capabilities:

AI literacy: Understanding how large language models (LLMs) work, how they’re trained, and where they fall short.
Prompt engineering: Knowing how to ask the right questions and frame the right context for AI tools.
Regulatory acumen: Staying current with guidance on AI use in regulated document environments.
Change leadership: Helping others adopt AI tools confidently and responsibly.

Final thoughts

The AI-assisted medical writing strategist is more than a job title — it’s a vision for the future of clinical documentation. As the life sciences industry embraces digital transformation, this role becomes essential to ensure that automation is paired with accountability, speed with accuracy, and efficiency with empathy.

By stepping into this role, medical writers don’t just adapt to the AI era — they lead it.

Streamlining Data Management and Improving Statistical Accuracy in Clinical Trials with AI

As clinical trials grow increasingly complex, the need for smarter, faster, and more efficient data processes and analysis is in demand. Artificial intelligence (AI) emerges as a powerful tool, especially in programming and data management. For clinical trial professionals, AI offers the promise of streamlining workflows, improving data quality, and reducing time to database lock.

The evolving role of AI in clinical data programming

AI is not replacing clinical programmers; it’s augmenting them. AI should be considered a tool to use within clinical trials, just as EDC and SAS are commonly used tools. Automation tools driven by machine learning can now handle routine, rules-based programming tasks such as edit check generation, derivation logic, and data transformation. This allows programmers to focus on more strategic activities like validating statistical code or optimizing data pipelines. AI needs the expertise of our clinical trial professionals.

Natural Language Processing (NLP) is also making great progress. For instance, NLP can interpret free-text protocol documents to auto-generate specifications, electronic case report form (eCRF) templates, or even suggest initial SDTM mappings, significantly reducing manual effort.

AI in data cleaning and quality oversight

Traditionally, data cleaning has been labor-intensive, with data managers manually reviewing queries, data listings, and edit checks across multiple data sources and systems. AI tools can now proactively flag anomalies or data trends that human review might miss, such as unexpected patterns in lab values, inconsistencies across visits, or possible fraudulent data across participants and sites.

Predictive models can help identify study participants at high risk of dropout or noncompliance, enabling earlier intervention. This not only improves data completeness but also enhances trial efficiency and participant retention. The effort and cost of replacing clinical trial participants is significant and felt across all stakeholders. Improving the patient’s experience would be a significant way to save time, money, and accelerating progress.

AI in statistical programming: From code automation to advanced insights

Statistical programming is central to clinical trial analysis from producing tables, listings, and figures (TLFs) to preparing submission-ready datasets. Traditionally reliant on manual coding in SAS or R, this work is now gaining speed, consistency, and quality through AI augmentation.

Where AI adds value in statistical programming

Automated code generation: AI models trained on historical programming logic can produce initial SAS macros or R scripts for common TLFs and dataset derivations. These drafts accelerate development by up to 40–60%, freeing programmers and biostatisticians to focus on complex analyses and interpretation.
Code review and validation: AI-assisted tools can scan code for logic errors, inefficiencies, redundant steps, and deviations from programming standards. Acting as a “second reviewer,” they flag potential issues early and suggest optimizations.
Dynamic statistical modeling: AI algorithms can rapidly explore large trial datasets to detect subgroup effects, anomalies, or emerging trends. When guided by statistical oversight, these insights can refine analysis plans and support adaptive trial decisions.

The aim is not to replace human judgment, but to boost productivity, reproducibility, and the speed of insight generation, without compromising scientific rigor.

AI in biostatistics: Powering smarter, more adaptive clinical trials

Biostatistics remains the foundation of evidence generation in clinical trials, providing the methodological rigor to transform raw data into reliable conclusions. In the context of AI, biostatisticians play a dual role: safeguarding scientific validity while leveraging new computational tools to enhance insight generation. This requires a careful balance between deep domain knowledge and technical proficiency in emerging AI-driven methodologies. From applying knowledge graphs (KGs) to map complex biomedical relationships, to developing predictive models that anticipate trial outcomes, biostatistics is evolving into a more dynamic and interconnected discipline.

Where AI adds value in biostatistics

Balanced expertise: Integrating statistical theory with AI/ML techniques to ensure robust, interpretable results.
Knowledge graph applications: Using KGs to uncover hidden relationships between biomarkers, treatments, and outcomes, supporting hypothesis generation and trial design.
Early prediction tools: Building predictive models for recruitment success, dropout risk, and endpoint achievement.
Segmentation and personalization: Identifying patient subgroups most likely to benefit from a therapy, improving trial efficiency and precision medicine strategies.
Support for registrational trials: Leveraging AI to optimize trial design, stratify patient populations, and run simulations that ensure the study is powered and structured for regulatory success.

Regulatory readiness and caution

Despite its promise, AI must be implemented thoughtfully. Regulatory agencies like the FDA are increasingly open to the use of advanced technologies but expect transparency, traceability, and validation. AI-based tools must be auditable and explainable, especially when used in clinical data workflows that feed into regulatory submissions.

What’s next?

As AI becomes more embedded in clinical trial ecosystems, we can expect increased integration with EDC systems, CDISC standards, and statistical programming tools. The goal isn’t to eliminate human oversight but to enhance it, allowing clinical data professionals to make faster, better-informed decisions.

Final takeaways

AI is reshaping programming and data management in clinical trials. For clinical trial professionals, now is the time to become familiar with these tools, understand their capabilities and limitations, and engage with cross-functional teams to ensure responsible and impactful implementation. Ultimately our goal is to shorten drug development timelines and improve patient outcomes. With AI, we can be part of the solution to provide improved treatments for patients.

Interested in learning more?

Join Steven Thacker, Sheree King, Kunal Sanghavi, and Juan Pablo Garcia Martinez for their upcoming webinar, “How AI Enhances Biometrics Services: Streamlining Data Management and Improving Statistical Accuracy in Clinical Trials” on Thursday, August 28 at 10 am ET:

Trustworthy AI in Action: Predicting Stroke Risk Transparently with Claims-Based Machine Learning

In recent years, deep learning and large neural networks have garnered most of the attention in the machine learning (ML) community. Their ability to model complex, high-dimensional data is indeed impressive. But in healthcare — where decisions can have serious consequences and interpretability is paramount — simpler, transparent models like logistic regression still have an important role to play.

Not every problem requires a black box. When it comes to predicting disease risk using structured data, such as insurance claims, traditional models can offer accuracy and insight.

Claims databases: An untapped resource for disease risk prediction

Claims databases are an increasingly valuable source of real-world data (RWD). Unlike clinical trial data, which is highly controlled but limited in scale and scope, administrative claims datasets cover millions of lives over multiple years, reflecting real patient behavior and care patterns.

These databases include information on diagnoses, procedures, prescriptions, and demographics — elements that, while lacking granular clinical detail, can still reveal important patterns in disease progression and risk. The scale of these datasets allows for robust statistical modeling, even for rare outcomes.

The case for explainable machine learning in claims-based risk prediction

When working with claims data, models like logistic regression, Lasso, or Ridge regression are not just sufficient — they are often ideal. These models:

Produce coefficients that quantify the relationship between features and outcomes.
Allow for transparent understanding of why a prediction was made.
Are easier to validate and communicate to clinicians, payers, and regulators.

In contrast, deep learning models often deliver slightly higher accuracy at the cost of interpretability — a trade-off that may not be acceptable in regulated healthcare environments.

A real-world example: Predicting stroke risk with claims data

In a recent study, Cytel used data from over 2.5 million insured individuals to predict the risk of stroke hospitalization. Using only claims-based features such as age, medication use, comorbidities (e.g., diabetes, hypertension), and health service utilization, we compared the performance of several models, including:

Logistic Regression
Regularized linear models (Lasso and Ridge)
XGBoost (a state-of-the-art ML algorithm)

The results? All models achieved similar predictive performance, with area under the ROC curve (AUC) values around 0.81. Logistic regression — simple, explainable, and well-established — performed on par with XGBoost, demonstrating that advanced complexity wasn’t necessary to achieve meaningful predictive power.

Transparency enables trust and action

What sets models like logistic regression apart is their explainability. Stakeholders can see precisely how risk factors like atrial fibrillation, hypercholesterolemia, or age contribute to predicted stroke risk. This level of clarity is essential not only for clinicians making decisions, but also for data governance, compliance, and patient communication.

In a time when “black box” AI models are under increasing scrutiny, explainable models offer a pragmatic path forward — especially when paired with large-scale real-world datasets like claims data.

Keep it simple, keep it transparent

Healthcare doesn’t just need powerful algorithms — it needs trustworthy ones. As our study shows, standard machine learning models remain highly relevant, especially when applied to well-structured real-world data. Claims databases, in particular, offer a rich foundation for developing these models and making preventive healthcare smarter, earlier, and more accessible.

Blending Power and Flexibility: How AI-Generated R Code is Reshaping Clinical Trial Design

In today’s fast-evolving clinical research landscape, designing robust and efficient trials is more critical than ever. As statistical designs grow in sophistication, biostatisticians are increasingly relying on both commercial platforms and open-source tools to meet unique modeling needs. But this hybrid approach also comes with challenges, particularly for those new to advanced simulation software or lacking programming experience.

At Cytel, we’ve been exploring how artificial intelligence (AI) can help bridge this gap. At the 2025 Joint Statistical Meetings (JSM), we will present on our latest innovation: AI-powered R code generation for clinical trial design, a feature embedded in our East Horizon™ platform. This assistant, called RCACTS (R Coding Assistant for Clinical Trial Simulation), represents a significant step forward in making custom trial design faster, more accessible, and more reliable.

Why talk about this now? The open-source imperative

While commercial clinical trial design software offers rapid design development through validated and user-friendly workflows, it doesn’t always address the full complexity of real-world problems. Trial statisticians often face challenges in areas such as oncology, rare diseases, and adaptive designs that require tailored statistical tests, unique outcome generation models, or alternative randomization techniques.

This is where open-source tools like R become invaluable. R allows statisticians to write custom code to simulate complex trial designs, perform Bayesian analyses, or integrate evolving regulatory guidance. Over the years, a vibrant ecosystem of R packages has emerged, offering a high degree of transparency, flexibility, and academic rigor.

Yet this flexibility comes with trade-offs: code development can be time-consuming, error-prone, and requires significant programming expertise. As a result, many biostatisticians find themselves switching between validated commercial workflows and custom R functions, leading to a process that is often fragmented and inefficient.

Recognizing this, Cytel’s East Horizon platform has introduced R integration points, enabling users to inject custom code directly into validated simulation workflows. This integration delivers the best of both worlds: the speed and structure of commercial software with the creativity and control of open-source.

Enter AI: Speed, simplicity, and smarter coding

Our next logical question was: can AI make this process even easier?

The answer, increasingly, is yes. With recent advances in generative AI, particularly large language models (LLMs), it’s now possible to assist in the generation of R code for simulation-based design tasks. At Cytel, we’ve harnessed OpenAI’s GPT-4o via API, securely deployed within Microsoft Azure, to create RCACTS, a coding assistant purpose-built for biostatisticians.

Unlike generic AI tools that produce standalone R scripts, RCACTS generates R code specifically tailored for the East Horizon simulation engine. It ensures that the generated functions:

Match expected input/output structures,
Include pre-defined parameters as shown in our internal statistical package CyneRgy,
Are immediately ready for integration and testing within a live trial design workflow.

With RCACTS, users can simply describe what they want in plain English and receive functioning R code that can be integrated into East Horizon.

Who benefits? Everyone from newcomers to experts

One of the major advantages of this AI-enhanced workflow is lowering the barrier to entry. For a new user unfamiliar with Cytel’s R integration or syntax requirements, writing compatible code from scratch can be daunting. RCACTS significantly reduces the learning curve by providing validated function templates, sensible defaults, and clear parameterization, all supported by generative AI.

At the same time, experienced statisticians benefit by spending less time on repetitive coding tasks, debugging, or remembering function signatures. This allows them to focus on higher-level design questions, such as: What analysis method is most robust? How sensitive is the design to different outcome distributions? What dropout patterns pose the greatest risk?

Our assistant supports a wide range of trial design elements:

Simulating patient responses: Binary, Continuous, Time-to-event, and Repeated-measure endpoints.
Analyzing simulated data: Statistical analysis for these endpoints.
Randomization: Flexible randomization of patients across treatment groups.
Enrollment and dropout modeling: Custom mechanisms for realistic patient enrollment and dropout scenarios.
Treatment selection: Supporting multi-arm multi-stage (MAMS) trial designs.

Balancing innovation with responsibility

Of course, like any AI solution, there are caveats. AI-generated code must be carefully reviewed for correctness, appropriateness, and regulatory readiness. RCACTS includes a built-in testing functionality to flag structural or syntactic errors, but statistical validation remains the user’s responsibility. Also note that all data interactions adhere to Azure OpenAI’s stringent data protection policies to ensure security and compliance.

There’s also a broader concern: will over-reliance on AI limit the creativity and deep statistical thinking that define our profession? At Cytel, we view AI not as a replacement for expertise, but as a tool to amplify it. Our goal is to give statisticians more time and mental space to explore, iterate, and innovate rather than reduce them to prompt engineers.

Looking ahead

The future of clinical trial design lies in intelligent integration: combining the strengths of validated commercial tools, flexible open-source frameworks, and AI-powered coding assistance. With East Horizon and RCACTS, we believe we’re building the blueprint for this future, with a platform that supports both scientific rigor and operational speed.

As the field continues to evolve, biostatisticians will need tools that not only keep up with complexity but also support creativity, collaboration, and efficiency. AI-generated R code, embedded within a powerful simulation engine, is one such tool and is already transforming how we approach design flexibility in clinical trials.

Catch us at JSM 2025 to learn more about how AI is transforming the future of clinical trial design within Cytel.

Leveraging Mobile and Wearable Technology for Outcomes Research in Depression

As mobile and wearable technologies become increasingly integrated into daily life, their applications have expanded far beyond convenience and lifestyle. In the field of outcomes research — particularly within mental health — these technologies are opening new frontiers for understanding and monitoring clinical endpoints. A notable case is depression, where continuous digital monitoring can provide rich insights into both the course of illness and treatment impact.

This post draws on our findings from a recent systematic review and poster presentation to examine how mobile and wearable tools are currently deployed in depression monitoring and how this aligns with broader outcomes research goals.

Digital monitoring as a tool for mental health outcomes

Over the past five to six years, depression has seen a marked rise across youth and adult populations globally, underscoring the need for scalable and effective monitoring strategies. In parallel, smartphones and wearables have become ubiquitous, capable of capturing passive, longitudinal health data. These digital tools offer unprecedented potential for outcomes research by providing real-time behavioral and physiological markers relevant to depression.

To map the current landscape, we conducted a comprehensive literature review focused on how smartphones and wearables are used to monitor depression in research contexts. This synthesis aimed to highlight prevailing methods, feature usage, and the extent to which demographic variability is accounted for — critical considerations in health outcomes analysis.

Key findings from the literature

We reviewed 140 studies and identified 22 that met our inclusion criteria. The following themes emerged:

Study characteristics

Recency: Most studies were published in 2024, reflecting the field’s rapid acceleration.
Geography: The U.S. and Pakistan emerged as leading contributors.
Sample Size: Studies included an average of 465 participants, suggesting moderately powered observational designs.

Demographic reporting

Gender and age: Captured in 20 of the 22 studies.
Ethnicity: Reported in just 9 studies.
Education and marital status: Only 4 studies reported these variables — yet both are key social determinants of health and influence depression outcomes.

Monitoring technologies and features

Smartphones were used in 20 of the 22 studies, highlighting their dominance.
Key features monitored included:
- Mood tracking: 20 studies
- Movement (accelerometer data): 10 studies
- Heart Rate Variability (HRV): 5 studies
- Word usage tracking: 4 studies
- Sleep patterns: 2 studies

Clinical assessment tools

Self-reported clinical scales were commonly used as outcome anchors:

PHQ-9 (Patient Health Questionnaire-9): 6 studies
GAD-7 (Generalized Anxiety Disorder-7): 7 studies

(See our original poster for a visual breakdown of these features and tools.)

Implications for outcomes research

From an outcomes research perspective, these technologies offer compelling advantages:

Continuous and passive monitoring: Enables longitudinal capture of clinically relevant endpoints like mood, behavior, and sleep — reducing bias from intermittent self-reporting.
Scalability and reach: Mobile-based data collection can extend to underserved and geographically dispersed populations, improving study generalizability.
Early signal detection: Passive data streams can flag deterioration or improvement earlier than clinical visits alone, offering potential for timely interventions.

However, a consistent limitation observed in the literature is the underreporting of demographic variables — especially education and marital status. This omission constrains subgroup analysis and limits insights into how different populations experience depression and respond to interventions. In outcomes research, such data are essential for contextualizing and stratifying results across socioeconomic or cultural dimensions.

The path forward

As wearable and mobile sensors become more refined, their integration into real-world data frameworks will likely become standard practice in outcomes research. But to truly capitalize on this potential, researchers must enhance demographic reporting and examine interactions between digital phenotypes and traditional health indicators across diverse populations.

These tools not only offer more granular tracking of mental health status — they also help researchers and health systems better understand the dynamics of treatment effectiveness, burden of illness, and quality of life over time.

Interested in learning more?

This blog summarizes findings from the poster presentation, “Exploring Mobile and Wearable Technology for Early Depression Detection and Monitoring,” presented by Lyuboslav Ivanov and Manuel Cossio at Cytel and Universitat de Barcelona.

Smartwatches Are Transforming Clinical Trials: Insights from Digital Primary Endpoints

The landscape of clinical research is continually evolving, with a growing emphasis on leveraging digital technologies to enhance efficiency and data quality. Among these innovations, wearable devices like the Apple Watch have emerged as promising tools for continuous and remote patient monitoring.

We recently analyzed the current application of smartwatches in clinical trials, focusing on their role in capturing digital primary endpoints across a variety of therapeutic areas. Here, I share some of our key findings, including major application areas as well as the benefits and challenges associated with their wider adoption in clinical research.

Digital primary endpoints

One way smartwatches are being used in clinical trials is to collect digital primary endpoints — sensor-generated data often collected outside a clinical setting.

To understand the potential impact of smartwatches in this context, we analyzed 87 completed or terminated clinical trials listed on ClinicalTrials.gov that used Apple Watch technology, examining key variables such as therapeutic focus, endpoint types, geographic distribution, and study design. Here is what we found:

Key Findings

High completion rate: 93.1% of the trials were completed successfully.
Top therapeutic areas: Cardiology led with 28.7% of studies, followed by neurology (21.8%) and oncology (11.5%).
Common endpoints: ECG changes (18.4%), heart rate variability (12%), and oxygen saturation (10%) were the most frequently measured.
Study design: Interventional trials dominated (64%), with high recruitment rates across the board.
Geographic trends: North America hosted the majority of trials (55%), followed by Europe (30%).

Importantly, validation studies confirmed the diagnostic accuracy of these devices, supporting their potential for regulatory approval.

Cossio, M. & Gilardino, R. (2025, May 15). Leveraging Consumer-Grade Wearables in Clinical Trials: Insights From Digital Primary Endpoints [Conference presentation]. ISPOR 2025, Montreal, Canada.

Why wearables matter in clinical trials

In clinical trials, smartwatches offer several unique advantages:

Continuous, remote monitoring: Smartwatches enable continuous, remote monitoring of patients, reducing the need for in-person visits and enhancing data collection.
Scalability: Smartwatch use is ideal for decentralized or hybrid trials, where flexibility and patient engagement are key, enabling participation across wide geographies.
Reduced costs: Smartwatches can also help reduce trial costs by requiring fewer site visits, enabling decentralized trials, providing real-time data collection and automated uploads, and so on.
Improved patient adherence and engagement: Smartwatches often include reminders, notifications, and user-friendly interfaces that help patients stay compliant with treatment schedules, data input, and study protocols.
Objective, high-frequency data: Smartwatches gather physiological metrics (e.g., heart rate, activity levels, sleep patterns) with high frequency and objectivity, reducing reliance on subjective self-reporting.
Increased accessibility and inclusivity:
Smartwatches can broaden trial access for populations who may face barriers to travel or mobility, thereby enhancing demographic diversity and generalizability of trial findings.

The growing use of wearables and the future of clinical trials

The growing use of wearables in clinical trials signals a shift toward more scalable, cost-effective, and patient-friendly research models. However, challenges remain — particularly around technical reliability and patient adherence. Future research should focus on integrating wearables into value-based healthcare and global trial frameworks.

The Use of AI in Clinical Trial Data Management

Clinical data managers play a key role in clinical trials, ensuring the integrity of the clinical data and bearing ultimate responsibility for preparing the data for statistical analysis. As clinical studies evolve, data management is becoming more complex with the use of multiple data sources, as well as the increased volume of data through case report forms, patient reported outcomes, laboratory data, electronic health records, imaging reports, and more.

Here, we discuss various ways artificial intelligence has the potential to accelerate clinical trial data management as well as some of the benefits and challenges of using these groundbreaking tools.

Transforming data cleaning in clinical trials

The traditional method of data cleaning involves manual checks and review of data listings. Data that falls outside the expected results is queried, which leads to time-consuming communications that are often prone to error. Additionally, a significant amount of time is spent programming and validating data checks and listings.

AI has the potential to transform data cleaning: AI tools can quickly spot outliers, inconsistencies, and errors in datasets that may be missed with traditional methods. An example of this is an exception listing, which compares elevated laboratory parameters with adverse events. This listing would seek to strike a correlation between laboratory values and concurrent adverse events. AI can also detect possible missing or duplicate data. Ultimately, AI can lead to faster data availability by improving clinical trial data analysis and cleaning.

Furthermore, AI can be used to detect fraudulent data. As data managers review one patient data at a time, AI tools can look at data at a site collectively and look for potential fraudulent data (for example, dosing for all patients at the site is on the same day and time).

Improving database development in clinical trials

The use of AI is becoming more prevalent in the software industry: electronic data capture (EDC) companies are using AI technology to translate plain text for edit check requirements (e.g., temperature should be within 36–40 Celsius) into the programmed edit checks. This will significantly improve the timelines for database development within the clinical trial space.

Shortening database lock timelines

AI can also shorten database lock timelines. Data managers are often tasked with manually reviewing data to ensure data accuracy as well as verifying whether there is missing data. An example of this is considering a study participant with colon cancer, but there is no prior therapy within the timeframe of the condition. Data managers require a significant amount of time to perform such reviews. AI can examine the data holistically, flag possible discrepancies, and reduce manual effort and human error, allowing for a cleaner database at a much faster rate.

The need for human involvement

While AI can process large amounts of data, identify suspicious patterns, and analyze images, human involvement is required to validate the AI functionality. Humans will also be required to perform ongoing reviews of the output and adjust the AI tools as the amount of clinical trial data increases. With the use of AI, individuals involved in the clinical trial can focus their skills and time on evaluating complex factors and decision-making.

Benefits and challenges of AI

The use of AI in clinical trial data management and EDC development has several benefits:

AI can automate time-consuming manual tasks for a faster and more accurate database.
AI can reduce the possibility of human error while continuously monitoring clinical trial data. This allows data issues to be detected quickly, which can improve data accuracy and safety for trial participants.

However, challenges remain:

Maintaining participant confidentiality while using AI tools can be a significant challenge.
AI systems must be configured and monitored to ensure there is no risk of accessing unauthorized or unblinded data.
Large amounts of data are required for the AI to be properly trained to deliver high-quality results.

Ultimately, the decision must be made of whether the benefits of AI outweigh the challenges.

The pharmaceutical industry is a heavily regulated industry. Validity of AI tools must be established before these tools are deployed for database development and data cleaning. This will ensure the sensitivity and specificity with which the AI tool is expected to perform with a negligible error rate.

Final takeaways

AI will play an important role in many aspects of clinical trials, including and beyond data management. From identifying potential compounds to automating routine tasks, innovating statistical programming, streamlining medical writing, and creating digital twins, we will only continue to see advancements in AI tools in the coming years. AI can be a groundbreaking tool to shorten drug development timelines and improve patient outcomes.

The Ethics of Artificial Intelligence in Clinical Development

If nothing else, the concept of artificial intelligence is polarizing — opinions on the topic tend to be very strong. While there are many subtleties in the viewpoints, they generally fall into two camps. The first consists of early adopters and technophiles who are practically buzzing with excitement about AI’s potential impact. The second camp is, to put it mildly, a bit more hesitant. Due to myriad reasons, they are reluctant — often antagonistically so — to place their trust in a computer system.

As someone who would place themselves in a more neutral position, I can’t help but feel that both sides are correct, at least to a certain degree.

Rapidly evolving AI tools and their potential impact on the industry

AI and other predictive modeling and analytics tools have reached a point of sophistication where it is obvious they have the potential to provide tremendous value. At the same time, we have seen the potential for technology used poorly to have a catastrophic impact where it was originally intended to help. Given our roles within the life sciences and healthcare industries, we must proceed with care — every action, or inaction, carries real consequences to the health and well-being of people worldwide.

As technology becomes more accessible and affordable, the drive for adoption will only grow. At the same time, we are seeing change and growth at an unprecedented rate, with advances emerging faster than most of us can realistically grasp. The reality is that we may never have a simple answer that will guide our actions, in fact the questions will likely only become more complex and challenging as we move forward.

This doesn’t mean we don’t have an obligation to ask these questions, nor does it allow us to walk away simply because it’s too difficult. Instead, we must challenge ourselves to be more thoughtful, responsible, and foster an open dialogue about the path forward for our industry. Regardless of your opinion, it is important we all engage on this topic. Each of us brings unique perspectives and value — whether by raising overlooked concerns or clarifying terms like AI, which often become loaded with a connotation outside their form.

Critical conversations on the future of clinical development

Considering all of this, I invite you to join me on March 25, 2025, at 10 am ET for what I hope will be the first of many conversations about the ways data and analytics are disrupting our industry. In this live discussion, I will be joined by Allie DeLonay from the Data Ethics Practice at the SAS Institute to discuss the ethical use of artificial intelligence — both broadly and considering some of the nuances unique to clinical development.

Harnessing AI-Powered Tools for Clinical Trial Design Coding

The global move towards AI-powered tools is sweeping across the life sciences industry. In particular, the roles biostatisticians play in both clinical trial design and programming lend themselves to AI-based innovations.

Earlier this month, Cytel launched its first AI-driven solution for clinical trial design code generation and joined the artificial intelligence revolution. This innovation is predicated on several years of research and development, coupled with the recent maturing of AI-focused service providers. The solution is designed for optimal functioning within the East Horizon platform and intended to enhance R integration functionalities that are now embedded within our software.

What makes Cytel’s AI-powered R coding assistant unique?

The coding assistant generates R code with required parameters for East Horizon. Unlike generic AI-based coding tools that generate standalone R scripts, this solution ensures the generated code includes function templates, expected arguments, and input variable names; is structured for direct integration into East Horizon’s simulation engines; and is aligned with industry best practices for regulatory-compliant clinical trials. In addition, the coding assistant is purpose-built for biostatistics and clinical trial design.

General-purpose AI tools do not innately relate to adaptive trial designs, survival analyses, or clinical trial randomization. Cytel’s AI-powered R coding assistant allows biostatisticians to generate custom statistical tests beyond software-native options; perform advanced patient data modeling such as Quasi-Poisson, longitudinal outcomes, etc.; and allows for alternative randomization and drop out modeling methods.

Finally, the coding assistant is embedded within an industry-standard solution for trial design. The solution ensures compatibility with East Horizon’s statistical engine, generating code that is formatted correctly and validation-ready.

How does the solution work?

Users interested in augmenting their trial design simulation work can select the R integration features within the software and gain access to the coding assistant. Users then enter prompts in natural language to illicit a response. The user can review the response, iterate and refine with additional queries, and modify the code to fit the task at hand. Once refined, the code can be employed in simulation runs for additional validation and debugging.

Why does this matter?

The AI-powered R coding assistant in East Horizon enables biostatisticians to generate complex R code instantly; customize trial simulations with precise statistical methods; and reduce manual coding errors and speed up model validation.

Custom R coding for oncology designs

The ability to augment design characteristics with custom R code is especially relevant to the ever-evolving oncology area of study. As regulatory guidelines are routinely adjusted to comply with clinical practice and current research, oncology studies often require specific analysis approaches and/or patient outcome data generation methods to conform to changing evidence thresholds. For example, the testing method and analysis type chosen for a specific design can be highly sensitive to the underlying distribution of the data. Therefore, simulating designs with a variety of analysis types can help design studies that are robust to a variety of possible data distributions.

With this in mind, using commercial software to generate patient outcome data through simulation takes full advantage of the software’s native workflows and computing power. These data are then analyzed against a variety of analysis types using R code augmentation. This approach to analysis variation also lends itself to advanced Bayesian tests, affording biostatisticians maximum flexibility.

Want to learn more?

In our recent webinar, “Evaluating Different Analysis Options for Your Oncology Study Design by Combining East Horizon and R,” J. Kyle Wathen and Valeria Mazzanti discuss clinical trial design using a combination of R coding and Cytel’s proprietary statistical software, with a focus on analysis testing variations:

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