In this interview, Vinita Navadgi, Sr. Director, Digital Patient Suite, IQVIA, shares a practical, thought-leading view on AI-driven electronic consent (eConsent). She explains how AI can transform consent into a dynamic, participant-centered process without losing the indispensable human connection that underpins trust in healthcare. The discussion covers governance across authoring, multimedia delivery, and comprehension assessment, the realities of change management in a regulated, human-centric industry, and regional considerations that shape global adoption. It’s a deep dive into balancing speed, ethics, and regulatory rigor while keeping patient understanding front and center.
Moe: I frame AI-enabled eConsent as a shift toward dynamic comprehension. From your experience, how does this shape real patient sessions, especially for older adults or kids?
Vinita: The core insight isn’t that AI replaces empathy; it’s that it reinforces its centrality. The baseline is the human connection. Even with the most engaging explanations, visuals, or multimedia—and even with advances like AR/VR—patients ultimately want to sit with a clinician they trust and voice their concerns. In practical terms, that means AI should augment the clinician’s ability to tailor information and surface concerns in real time, not replace the opportunity for a human conversation. For instance, an elderly patient may need additional time, plain-language explanations, and a calm environment; a pediatric context might require age-appropriate framing and reassurance about safety. The human in the loop remains vital across all stages from authoring to final interaction, and regulators expect this embedded approach. Personalization must be paired with a trusted clinician’s guidance to preserve the therapeutic relationship and ensure understanding isn’t sacrificed for speed.
Moe: AI enables hyper-personalization, but automation complacency is a risk. How do you prevent overtrusting AI while regulators still require documented, human-reviewed consent?
Vinita: That’s a critical point. Automation complacency—the risk of staff over-trusting AI recommendations—can undermine both ethics and regulatory compliance if not managed properly. To counter this, we implement transparent, “open-box” AI models that work alongside robust governance frameworks. These governance frameworks explicitly define oversight roles and responsibilities at every stage of the consent process, ensuring human judgment is always central.
Quality assurance should be embedded throughout the workflow by requiring mandatory human reviews and interventions at key, predetermined checkpoints—whether during the design, deployment, or post-delivery evaluation of consent materials. This guarantees that a ‘human in the loop’ is present at all critical stages, reinforcing the commitment to ethical standards and regulatory requirements.
Strong governance also means continuously documenting every AI output, decision point, and the rationale behind each recommendation. We can’t just rely on the technology; teams must be empowered with comprehensive AI literacy training so they’re equipped to critically assess and validate AI-generated suggestions rather than accept them at face value. This approach maintains efficiency while anchoring the entire consent process in ethics, safety, and accountability.
With these safeguards, regulators can audit the full decision-making chain at any point, maintaining the integrity of the consent process. Ultimately, this structure allows us to responsibly scale hyper-personalized consent materials—leveraging AI’s efficiencies without sacrificing patient trust or compliance.
Moe: In the consent lifecycle, where should AI be most impactful, and how do you keep governance consistent as it touches authoring, delivery, and assessment? How do you reconcile rapid content with IRB stability?
Vinita: Certainly. AI offers significant acceleration at nearly every stage of the eConsent process. To begin with, AI excels at drafting Informed Consent Forms (ICFs), serving as an efficient assistant that can rapidly generate initial versions. This capability streamlines the authoring phase considerably, allowing teams to move forward with timely and well-structured consent materials.
In addition, AI greatly facilitates the translation of consent documents We can pre-create content tailored to serve all varying demographics—age groups, ethnicity, health literacy, and regional needs—and then embed governance gates so nothing proceeds without human sign-off. This includes enhanced accessibility options tailored to diverse participant groups, addressing varying needs related to age, literacy, and region. AI also enables the conversion of consent information into multimedia formats—such as videos and graphics—which can be customized to effectively engage participants based on their demographic profiles.
Beyond content creation, AI tools can assess participant reactions after the presentation of consent materials. These systems help staff determine whether information has been understood or if confusion is present, enabling timely intervention when necessary. Knowledge checks powered by AI further confirm participant comprehension, ensuring that informed consent is genuinely achieved and trigger clinician intervention if confusion arises.
However, it is essential to maintain robust safeguards throughout. Importantly, I insist on explainable AI: the system must provide an auditable rationale for each suggestion or assessment so quality teams, regulators and IRBs can understand why a presentation or cue was flagged. This balance—AI-assisted authoring with human oversight—lets us retain speed and personalization without complacency. Crucially, every AI-generated output—whether a new format or translation—undergoes human review and approval to meet regulatory standards prior to use. We also foresee a continuous feedback loop with patient and site staff input to refine models. This approach ensures that while we benefit from increased efficiency, we remain fully compliant with ethical standards and local regulations.
Consistency across the process is fundamental. This involves standardized data capture, meticulous documentation of every decision, and clear protocols for site staff intervention. We conduct regular audits, solicit cross-functional feedback, and require appropriate sign-offs at each workflow stage. In summary, our integration of AI in eConsent enhances clarity and speed, but never at the expense of ethics or regulatory compliance, which remain our top priorities.
Moe: Could sentiment analysis or real-time cues help predict dropout risk or measure empathy? How might these signals become KPIs without compromising consent integrity?
Vinita: Multimodal signals can illuminate participant perception, helping identify confusing sections or empathy gaps without altering consent itself. Facial cues, voice, and even physiological data can guide staff responses and documentation. I see this as enabling more proactive support, not coercion, and it requires transparent, opt-in data handling and clear limits on automated decisions. We can establish empathy- and engagement-related KPIs alongside traditional measures, with post-interaction surveys to validate AI inferences. The AI’s role is to inform and support staff, not replace judgment, and regulators must understand how cues influence care and disclosure. We’ll ensure explainability and an auditable trail, so that human oversight remains central and patient autonomy is preserved.
Moe: Change management is critical. What resistance patterns have you seen, and what should leaders do to foster adoption?
Vinita: Resistance centers on fear of displacement and loss of human touch. I emphasize that AI augments, not replaces, and that roles will shift toward QA, oversight, and governance. Leaders should communicate a clear growth path and involve staff early in pilots to demonstrate safer, faster outcomes. Training should focus on interpreting AI signals, knowing when to intervene, and maintaining patient-centric care. A phased approach with measurable ROI and continuous feedback helps teams see value and reduce fear. The end goal is a culture where professionals collaborate with AI to deliver higher-quality consent experiences.
Moe: Regional differences shape AI adoption. What regulatory challenges stand out in the US vs Europe, and how do you navigate them for global consistency?
Vinita: eConsent systems are legal and are increasingly accepted worldwide but how IRBs/ethics committees (ECs) review and what documentation they require vary a lot by jurisdiction. Typically, the differences revolve around the data-privacy, e-signature standards, validation processes, audit trail expectations and whether reviews are conducted centrally or locally. I believe that AI will add another layer to regulatory review with new documentation layers included in the submissions process. While we incorporate AI we will have to design systems and processes with these regional variations in mind and prepare multiple content flavors per locale. Within a country, regional differences exist; we must stay adaptable. Our global deployment must use modular, regulatory-aligned content, robust explainability, and auditable data trails. This enables consistent patient-centric consent while respecting local rules and ensuring regulatory integrity.
Moe: Any final guidance for leaders integrating AI into eConsent, given its human-centric and regulated nature?
Vinita: I view AI-enabled eConsent as a disciplined, human-centered field built on explainable AI, multimodal data, and strong governance frameworks. Leaders should prepare multilingual content and rapid demographic-based adaptation for care settings. Maintain transparency with regulators and IRBs, establish robust human-in-the-loop oversight, and invest in training and change management. The potential gains in speed and understanding come with ethical and regulatory duties that must guide implementation.
Moe Alsumidaie is Chief Editor of The Clinical Trial Vanguard. Moe holds decades of experience in the clinical trials industry. Moe also serves as Head of Research at CliniBiz and Chief Data Scientist at Annex Clinical Corporation.
