By Artem Trotsyuk, operating partner, LongeVC
The integration of artificial intelligence (AI) into clinical trials is transforming the landscape of personal health management. By leveraging a range of AI-powered tools—including wearables, health applications, and chatbots—clinical trials are enhancing data collection and fostering greater patient engagement. These innovations enable continuous monitoring of patient health between medical visits, aligning with FDA guidelines that emphasize the importance of secure and validated data capture from personal devices.
In contrast to traditional trials that depend on sporadic site visits and manual record-keeping, AI-integrated tools facilitate decentralized trials. This evolution allows for remote data collection and offers real-time feedback to participants, enhancing convenience and accessibility. Such advancements create more inclusive trials by reaching patients in remote locations and significantly improving the overall patient experience.
AI-driven personal health technologies are revolutionizing various facets of trial operations, including patient compliance, symptom reporting, protocol adherence, communication, and retention. However, this shift also brings operational challenges related to data quality, interpretation, and regulatory compliance that clinical operations leaders must navigate in this AI-centric age.
Boosting Patient Compliance and Protocol Adherence
Real-time monitoring and reminders: AI-driven platforms, such as electronic clinical outcome assessment (eCOA) systems, utilize automated reminders and user-friendly mobile interfaces to encourage patients to complete tasks on schedule. Wearable devices provide adherence data—such as whether a patient is wearing the device or is active—streaming this information to researchers in near real-time. Early identification of noncompliance is possible, reducing missed doses or incomplete logs and helping participants stay within protocol guidelines.
Higher adherence rates: Research demonstrates that digital tools can significantly enhance adherence. Many trials employing wearable technology have reported participant adherence rates soaring between 70% and 80%, far exceeding those achieved through traditional methods. Similarly, trials applying modern eCOA platforms have observed patient diary compliance nearing 100% in certain cases. AI-powered chatbots designed for patient assistance are also yielding promising results by offering real-time guidance and check-ins, ensuring data completeness and expediting study timelines by minimizing the need for repeated measures or extensions.
Personalized coaching: Conversational AI tools act as virtual coaches, guiding patients through complex protocols in a conversational and user-friendly manner. By clarifying instructions, providing instant responses, and issuing personalized reminders—such as timely notifications for medication—it demystifies procedures and motivates participants to adhere to their treatment plans. Patients receive prompts from a trusted source rather than an automated alert, which enhances long-term adherence.
Improving Symptom Reporting and Data Collection
AI-enhanced symptom reporting tools are revolutionizing how patient experiences are documented during trials. Instead of relying on retrospective accounts during clinic visits, participants can now record symptoms in real-time using electronic patient-reported outcome (ePRO) applications or wearable-connected diaries. This immediacy minimizes recall bias, offering a much clearer picture of patient reactions to treatment. Moreover, predefined thresholds can trigger alerts to the study team, facilitating quicker intervention for emerging safety signals or protocol discrepancies.
Wearable devices contribute a layer of continuous, objective measurement that complements subjective patient-reported data. Metrics such as activity patterns, sleep quality, heart rate variability, and respiratory markers can be passively tracked in real-world settings. This dual approach provides valuable context often overlooked in episodic site-based assessments. Over time, the synthesis of subjective and objective data generates richer datasets, revealing trends and fluctuations that may otherwise remain hidden. With effective management, this continuous data capture enhances both safety monitoring and efficacy analysis.
Strengthening Patient Engagement and Communication
Beyond data collection, AI-powered tools are reinventing daily interactions between participants and trials. Conversational interfaces and messaging systems facilitate ongoing communication, allowing patients to ask questions, express concerns, or seek clarification without the wait for their next visit. This uninterrupted access helps alleviate uncertainty and anxiety while addressing many routine inquiries that would typically fall to site staff.
Real-time feedback loops further amplify engagement. When patients perceive that their feedback is valued—through confirmation messages, progress indicators, or prompts related to abnormal readings—they are more likely to remain engaged and compliant. Simultaneously, study teams gain early insights into potential issues, enabling proactive outreach before minor worries escalate into participant withdrawals. This continuous sense of connection can lead to enhanced engagement and reduced dropout rates, particularly in decentralized or hybrid trial models.
Reducing Dropout Rates and Improving Retention
Participant dropout remains a significant challenge in clinical trials, with participation burden frequently cited as a primary factor. AI-enabled remote monitoring lessens the need for frequent site visits by shifting many assessments into the patient’s everyday environment. When trials align with participants’ lifestyles, retention rates improve. However, mere convenience isn’t the single solution.
Continuous digital engagement allows trial teams to monitor early warning signs of disengagement. Missed entries, diminishing interaction levels, or behavioral changes can be quickly flagged, allowing coordinators to intervene before participants are lost. This feedback mechanism supports both patients and site staff, replacing reactive retention strategies with more proactive, targeted assistance. Trials that prioritize participant experience see higher completion rates and more reliable data.
New Operational Risks and Challenges
Though AI-powered tools have numerous benefits, they also introduce significant operational complexities. The abundance of continuous data can raise concerns about validation, standardization, and interpretability. Different devices might capture similar metrics inconsistently, and without early alignment on data definitions and validation criteria, comparisons may become unreliable. As a result, sponsors must increasingly regard digital tools as clinical instruments, deserving of the same rigorous evaluation as conventional endpoints.
There’s also the issue of signal overload. Continuous monitoring can generate more data and alerts than clinical teams can effectively process. Without comprehensive filtering and escalation protocols, essential signals might become lost amid irrelevant noise. Establishing clear protocols for alert thresholds, review responsibilities, and response actions is critical to preventing confusion and missed opportunities, especially outside typical working hours.
Privacy, security, and regulatory compliance further complicate the landscape. Patient-derived health data is inherently sensitive, and its collection must adhere to evolving regulatory frameworks across jurisdictions. While many patients are open to sharing data when its intended use is clearly communicated, sponsors still bear the responsibility of ensuring secure handling, transparent consent, and appropriate oversight. Additionally, regulators stress that AI should enhance—not replace—clinical judgment, emphasizing the necessity for human oversight and comprehensible systems.
Finally, the issue of accessibility must be systematically addressed. Not all participants possess smartphones, adequate connectivity, or digital skills. Without proactive measures—such as providing devices, training, and technical assistance—trials risk excluding the very populations they aim to serve. Therefore, inclusive design and thoughtful operational planning are essential to ensure that digital transformation fosters diversity rather than diminishes it.
Building the Next Generation of Trial Workflows
AI-enabled personal health tools are evolving into a fundamental component of contemporary clinical trial operations. When thoughtfully implemented, they enhance compliance, improve data quality, boost engagement, and heighten retention—creating a more patient-centric design for trials. The real question for sponsors and clinical operations leaders is not whether to embrace these tools but rather how to do so responsibly.
The future depends on striking a balance: combining innovation with validation, automation with oversight, and convenience with rigor. As regulatory guidance continues to evolve and best practices emerge, AI-driven tools are expected to transition from experimental additions to standard components of trial infrastructure. Those trials that successfully navigate this transition will be better equipped to generate high-quality evidence, support participants throughout the research journey, and ultimately expedite the delivery of new therapies to patients.
About The Author:
Artem A. Trotsyuk serves as the operating partner at LongeVC, and his background is in bioengineering and computer science. His expertise centers on early-stage investments (pre-seed, seed, up to Series A), helping entrepreneurs turn their visions into successful ventures. Artem also teaches bio-entrepreneurship at Stanford University and has earned a Ph.D. in bioengineering and a master’s in computer science specializing in AI from Stanford, under the guidance of Geoffrey Gurtner, MD. His research interests include bioengineering, gene editing, wearables, CRISPR therapy, regenerative medicine, and the ethical use of data in drug development.
