An agenda built through deep collaboration with our community to ensure every session delivers substance over soundbites. You’ll hear from true subject matter leaders via candid case studies, collaborative roundtables, and varied panel discussions —providing clarity on what’s driving success across the clinical development landscape
• Strengthen pipeline value and trial execution by developing end-to-end drug development capabilities and leveraging innovative development matrices, integrated protocols, and long-acting treatment approaches to better address complex patient needs.
• Enable faster, more collaborative clinical development by forming strategic partnerships where speed, shared expertise, and patient impact are embedded as core drivers of success.
• Accelerate protocol design and site selection decisions by deploying agentic AI to continuously synthesise feasibility, recruitment, and monitoring data into prioritized, actionable recommendations
• Maintain regulatory compliance and patient safety by embedding human oversight checkpoints and governance frameworks that ensure agentic AI operates transparently within defined boundaries
• Strengthen portfolio resilience by leveraging AI-driven insights to optimize trial site selection and operating models to evolving regulation and patient access needs
• Scale agile, inclusive trial portfolios by embedding decentralized approaches and AI-enabled tools to reduce access barriers and support long-term clinical development priorities
• Deliver consistently high-quality trial data by building continuous feedback loops with sites to identify risks in real time and enable rapid, targeted intervention
• Maintain regulatory compliance and patient safety by embedding human oversight checkpoints and governance frameworks that ensure agentic AI operates transparently within defined boundaries
• Improve trial performance by building continuous feedback loops with sites to identify risks in real time and enable rapid, targeted intervention.
• Inform smarter portfolio and operational decisions by using site-level insights to guide where, when, and how resources are deployed across studies.
Quality by design, oversight by intent: building end-to-end frameworks that drive safer, smarter trial delivery
• Strengthen sponsor oversight by embedding quality by design principles and central monitoring at site level to trigger targeted interventions when risk thresholds are met
• Drive proactive improvement through a collaborative RBQM framework that connects sponsors, study teams and sites, creating ongoing feedback loops that surface risks early and support more informed, responsive decision-making
Get hands-on in interactive sessions designed to translate ideas into practical, real-world solutions.
Session One: Revolutionizing patient recruitment in the digital world: harnessing digital marketing, social media, and patient advocacy to improve efficiency.
Session Two:Â Balancing innovation and risk: how far should AI shape trial design and RWE?
Session Three: Patient-centricity in clinical trials: moving beyond the buzzword to evaluate real-world application in trial design, conduct and outcomes
• Improve trial feasibility and recruitment outcomes by creating structured feedback loops with patients that translate real-world insights on awareness, trust, and participation experience into more accessible and better-designed protocols.
• Strengthen patient retention via continuously capturing and acting on patient perspectives around data transparency, AI use, and communication preferences to shape personalized engagement strategies.
• Accelerate enrollment timelines by deploying site-centric development models that expand access to underserved patient populations in competitive, multi-sponsor study environments.
• Build scalable recruitment models by equipping sites and participants with integrated digital tools and standardized workflows needed to replace one-off tactics with repeatable, data-driven patient access strategies.
• Improve recruitment rates and strengthen retention by building meaningful, insight-led engagement with patient communities from the earliest stages of enrollment planning.
• Create more diverse and accessible recruitment pathways via combining digital tools with direct community involvement to build trust, expand reach, and strengthen patient participation.
• Remove the hidden bottleneck in clinical innovation by moving beyond document repositories and use AI to create structured evidence assets
• Support protocol design, competitive intelligence, indication strategy and evidence generation planning with AI-backed, human reviewed evidence intelligence
• Reduce the resource cost of data cleaning and query resolution by applying AI-powered automation to identify inconsistencies, anomalies, and missing data points across datasets in real time.
• Build clearer and more compelling evidence packages with AI-driven analysis to help surface meaningful patterns across complex, multi-source datasets.
Get hands-on in interactive sessions designed to translate ideas into practical, real-world solutions.
Session One: Evolving Trial Oversight: Are Risk-Based Approaches Enough to Safeguard Quality, or Must New Models Take the Lead?
Session Two: Rethinking Site Selection: What Factors Should Truly Drive Selection in Modern Trials?
Session Three: Can the Industry Align Global Regulation Without Slowing Innovation?
• Drive the organizational change needed to turn innovation investment into frontline impact by breaking down silos, realigning incentives, and embedding patient-centric accountability across clinical teams
• Scale innovation beyond pilots by transforming partnerships and technology into enterprise-wide capability through the right infrastructure, governance, and ways of working
• Improve trial monitoring by deploying agentic AI to continuously scan multiple diverse data sets for delivery risks and recommend next best operational action.
• Accelerate execution and avoid delays via streamlining data workflows with AI agents that automate routine reviews and route insights to the right teams for rapid intervention.
• Sharpen pipeline decision-making and resource allocation by moving beyond traditional trial metrics and adopting outcome frameworks that better reflect smaller, more targeted patient populations, where conventional success measures no longer tell the full story.
• Future-proof your evidence generation strategy by aligning trial design, endpoint selection, and data approaches to the evolving and often competing expectations of patients, payers, and regulators.
• Reduce the cost of portfolio attrition by using data-driven, agile protocol design to identify and act on early efficacy and safety signals before they reach expensive Phase II and III stages.
• Maximize ROI by integrating flexible, modular trial frameworks that allow rapid reallocation of resources across programs in response to emerging data.
• Expand access to underrepresented and geographically dispersed patient populations by removing logistical barriers through remote participation, telehealth visits, and local healthcare provider integration.
• Generate richer, more diverse evidence across your portfolio by leveraging real-time data capture and continuous remote monitoring to deepen the quality and breadth of findings across trial programs.
• Reduce feasibility risk and strengthen the relevance of your evidence base by using patient registry, EHR, and claims data to validate inclusion/exclusion criteria against the realities of how patients present and progress in clinical practice.
• Shorten recruitment timelines and produce more generalizable findings by mapping real world patient pathways to identify the right sites, populations, and touchpoints before a trial launches.
• Strengthen pipeline value and trial execution by developing end-to-end drug development capabilities and leveraging innovative development matrices, integrated protocols, and long-acting treatment approaches to better address complex patient needs.
• Enable faster, more collaborative clinical development by forming strategic partnerships where speed, shared expertise, and patient impact are embedded as core drivers of success.
• Accelerate protocol design and site selection decisions by deploying agentic AI to continuously synthesise feasibility, recruitment, and monitoring data into prioritized, actionable recommendations
• Maintain regulatory compliance and patient safety by embedding human oversight checkpoints and governance frameworks that ensure agentic AI operates transparently within defined boundaries
• Strengthen portfolio resilience by leveraging AI-driven insights to optimize trial site selection and operating models to evolving regulation and patient access needs
• Scale agile, inclusive trial portfolios by embedding decentralized approaches and AI-enabled tools to reduce access barriers and support long-term clinical development priorities
• Accelerate protocol design and site selection decisions by deploying agentic AI to continuously synthesise feasibility, recruitment, and monitoring data into prioritized, actionable recommendations
• Maintain regulatory compliance and patient safety by embedding human oversight checkpoints and governance frameworks that ensure agentic AI operates transparently within defined boundaries
• Deliver consistently high-quality trial data by building continuous feedback loops with sites to identify risks in real time and enable rapid, targeted intervention
• Inform smarter portfolio and operational decisions by using site-level insights to guide where, when, and how resources are deployed across studies.
Quality by design, oversight by intent: building end-to-end frameworks that drive safer, smarter trial delivery
• Strengthen sponsor oversight by embedding quality by design principles and central monitoring at site level to trigger targeted interventions when risk thresholds are met
• Drive proactive improvement through a collaborative RBQM framework that connects sponsors, study teams and sites, creating ongoing feedback loops that surface risks early and support more informed, responsive decision-making
• Improve trial feasibility and recruitment outcomes by creating structured feedback loops with patients that translate real-world insights on awareness, trust, and participation experience into more accessible and better-designed protocols.
• Strengthen patient retention via continuously capturing and acting on patient perspectives around data transparency, AI use, and communication preferences to shape personalized engagement strategies.
• Accelerate enrollment timelines by deploying site-centric development models that expand access to underserved patient populations in competitive, multi-sponsor study environments.
• Build scalable recruitment models by equipping sites and participants with integrated digital tools and standardized workflows needed to replace one-off tactics with repeatable, data-driven patient access strategies.
• Improve recruitment rates and strengthen retention by building meaningful, insight-led engagement with patient communities from the earliest stages of enrollment planning.
• Create more diverse and accessible recruitment pathways via combining digital tools with direct community involvement to build trust, expand reach, and strengthen patient participation.
• Remove the hidden bottleneck in clinical innovation by moving beyond document repositories and use AI to create structured evidence assets
• Support protocol design, competitive intelligence, indication strategy and evidence generation planning with AI-backed, human reviewed evidence intelligence
• Reduce the resource cost of data cleaning and query resolution by applying AI-powered automation to identify inconsistencies, anomalies, and missing data points across datasets in real time.
• Build clearer and more compelling evidence packages with AI-driven analysis to help surface meaningful patterns across complex, multi-source datasets.
Get hands-on in interactive sessions designed to translate ideas into practical, real-world solutions.
Session One: Evolving Trial Oversight: Are Risk-Based Approaches Enough to Safeguard Quality, or Must New Models Take the Lead?
Evolving trial oversight: are risk-based approaches enough to safeguard quality, or must new models take the lead?
Session Two: Rethinking Site Selection: What Factors Should Truly Drive Selection in Modern Trials?
Rethinking site selection: what factors should truly drive selection in modern trials?
Session Three: Can the Industry Align Global Regulation Without Slowing Innovation?
Can the industry align global regulation without slowing innovation?
• Drive the organizational change needed to turn innovation investment into frontline impact by breaking down silos, realigning incentives, and embedding patient-centric accountability across clinical teams
• Scale innovation beyond pilots by transforming partnerships and technology into enterprise-wide capability through the right infrastructure, governance, and ways of working
• Improve trial monitoring by deploying agentic AI to continuously scan multiple diverse data sets for delivery risks and recommend next best operational action.
• Accelerate execution and avoid delays via streamlining data workflows with AI agents that automate routine reviews and route insights to the right teams for rapid intervention.
• Sharpen pipeline decision-making and resource allocation by moving beyond traditional trial metrics and adopting outcome frameworks that better reflect smaller, more targeted patient populations, where conventional success measures no longer tell the full story.
• Future-proof your evidence generation strategy by aligning trial design, endpoint selection, and data approaches to the evolving and often competing expectations of patients, payers, and regulators.
• Reduce the cost of portfolio attrition by using data-driven, agile protocol design to identify and act on early efficacy and safety signals before they reach expensive Phase II and III stages.
• Maximize ROI by integrating flexible, modular trial frameworks that allow rapid reallocation of resources across programs in response to emerging data.
• Expand access to underrepresented and geographically dispersed patient populations by removing logistical barriers through remote participation, telehealth visits, and local healthcare provider integration.
• Generate richer, more diverse evidence across your portfolio by leveraging real-time data capture and continuous remote monitoring to deepen the quality and breadth of findings across trial programs.
• Reduce feasibility risk and strengthen the relevance of your evidence base by using patient registry, EHR, and claims data to validate inclusion/exclusion criteria against the realities of how patients present and progress in clinical practice.
• Shorten recruitment timelines and produce more generalizable findings by mapping real world patient pathways to identify the right sites, populations, and touchpoints before a trial launches.
