Intelligent Clinical Trials 2024

guidelines and frameworks that balance the need for transparency with the protection of sensitive information. Public–private consortia should prioritize the creation of advanced data security measures to protect sensitive health data. This includes adopting encryption, secure access protocols and regular audits to prevent data breaches and ensure compliance with data protection regulations.3. Foster a culture of innovation: All stakeholders should drive the creation of public–private consortia that create shared objectives and guidelines for AI innovation in R&D throughout the healthcare system – and then create mechanisms for holding participants accountable. Recommendations by stakeholder TABLE 1 Government – Create standards for data collection and sharing – Build centralized or federated data hubs – Create incentives to drive networked data sharing – Advocate for transparency in data usageRegulators – Drive policies enforcing data standards – Establish regulatory sandboxes – Promote adaptive trial designs – Create AI-specific regulatory frameworks – Balance innovation and compliance Clinical development leaders/pharma – Use Gen AI to improve incomplete or low-quality datasets where possible – Implement AI-driven adaptive trial designs – Participate in data-sharing initiatives – Combat innovation inertia – Address workforce skills gapsHealthcare providers – Support data-sharing frameworks – Promote data-driven innovationAI and technology leaders – Develop AI solutions tailored for clinical settings – Integrate diverse data sourcesPublic–private consortia – Establish shared AI innovation goals – Implement advanced data security protocols – Spearhead data-sharing initiatives Intelligent Clinical Trials: Using Generative AI to Fast-Track Therapeutic Innovations 14