Ideas Mined from Trustworthy Tech Dialogues

Healthcare innovation has long been a cornerstone of human progress, reflecting our collective pursuit of improving quality of life, advancing scientific research, and ensuring equitable access to care. Today, Artificial Intelligence (AI) holds the potential to transform this field with new capabilities to make care more personalized, predictive, and accessible. However, alongside the promise of AI integration in healthcare lies the critical responsibility of ensuring equity, transparency, and trust in these systems. This episode of Trustworthy Tech Dialogues examines how healthcare innovation can evolve responsibly in line with technological advancements, imagining a future where trust and technology shape a more inclusive and prosperous healthcare ecosystem.

In Conversation:

Maneesh Goyal, Chief Operating Officer of Mayo Clinic Platform, is leading the ambitious mission to transform healthcare with new knowledge, solutions, technologies and collaborative partnerships. With a multifaceted background spanning finance, engineering, product development and healthcare, Maneesh is operationalizing the promise of technological advancement into a reality for personalized healthcare solutions. His leadership exemplifies the importance of institutional collaborations in the research, design, and development of emerging technologies to address the complex challenges of modern healthcare and improve patient outcomes.

A Platform Approach to Healthcare Innovation

Mayo Clinic Platform is redefining healthcare delivery by adopting a platform-based model that emphasizes collaboration, scalability, and knowledge-sharing. Unlike traditional proprietary systems in healthcare, a platform business model builds ecosystems and uses technology to deliver care at scale. A platform approach embraces the ethos of openness and commits itself to sharing its data as part of a broader social contract. As Maneesh explains, Mayo Clinic Platform builds on the organization’s legacy of teaching and learning, extending its medical philosophy alongside the opportunities of technological advancement.

The Key to Futureproofing: Aligning Principles with Innovation

The rapid advancements in AI capabilities in the past five years alone have transformed the potential of the healthcare industry. In this fast-evolving landscape, a guiding set of principles is imperative to ensure an innovative organization is future-proof. Maneesh highlighted recent growth in the healthcare industry, citing the foundational value of security perimeters in cloud computing that maintain patient privacy while storing clinical data. Recent developments illustrate how principles-based alignment can guide the development and application of evolving technologies to achieve better healthcare outcomes and solutions.

 Patient privacy is a guiding principle in healthcare innovation, driving advancements in data science and solutioning around de-identification methods and diverse machine learning. For example, de-identifying patient data to make it safe and usable outside of an Institutional Review Board (IRB) process has led to development of new tools to de-identify complex unstructured datasets and images. Similarly, progress in federated learning – a collaborative machine learning technique that eliminates the need for the centralization of datasets – has enabled training models to learn from more diverse data sources. This approach allows healthcare systems to leverage data across all silos and segments of healthcare for better analysis, interoperability, and scalability. Given the increasing complexity and sensitivity of healthcare data, it offers a technical solution to a central challenge of balancing privacy and interoperability in the medical field.

Mayo Clinic Platform’s ‘Data Behind Glass’ approach also embodies this principles-driven strategy, designed to ensure robust data security and patient privacy while enabling advanced technological development. This method involves organizing and de-identifying data, with third-party certification to confirm its anonymity, and safeguarding it within a controlled ecosystem that restricts external data transfer. By focusing on knowledge extraction rather than data sharing, this approach supports compliance with global regulatory standards such as GDPR and HIPAA, while fostering the creation of trustworthy algorithms and predictive models.

Trustworthy AI Integration

AI integration in healthcare spans a spectrum of transformative applications, from scientific discovery to clinical use and administrative functions. Maneesh maintains that AI integration is not about replacing professionals but enhancing their ability to deliver higher quality care. AI in support functions, like administrative scheduling and coding, can reduce bureaucratic inefficiencies and allow staff to focus more on patient care and innovation.

To navigate the complexities of AI in more high-risk clinical and scientific contexts, a responsible AI framework becomes critical. Such a framework categorizes AI use cases and evaluates their associated risks, ensuring that tools are transparent, explainable, and free from bias. In clinical applications, where data privacy and ethical considerations are paramount, this approach helps build trust in AI systems. Mayo Clinic Platform plays a pivotal role as a trusted operator within the healthcare ecosystem, focused on establishing and upholding principles that prioritize the highest standards of fairness, safety, and accountability.

Building Consensus for Trustworthy AI in Healthcare

Developing a trusted approach to critical health questions that affect the lives of millions requires a diverse array of stakeholders to collaborate in driving the development, evaluation, and appropriate use of AI in healthcare. To achieve this, Mayo Clinic Platform led the establishment of the Coalition for Health AI (CHAI). This coalition brings together over diverse institutions to define the values, purpose, and practices necessary to ensure trustworthy AI in healthcare. At its core, the coalition exemplifies how shared principles, such as improving care quality, combined with an ethos of collaboration and openness, can drive meaningful progress while avoiding fragmentation or proprietary limitations. CHAI is not only a leader in the healthcare AI governance field, with the recent draft publication of Responsible Health AI Framework, but a leading example of forming multi-stakeholder consensus on emerging issues. Maneesh shares the central lessons of success in finding alignment with large, diverse, stakeholder groups. Through the “Coalition for Health AI, I think all of the partners agree that the betterment of care, care quality is the number one imperative…establishing a foundation that you can all agree on unabashedly is the start and then second, you get to you have to check your ego at the door.”

Trust as a Guiding Principle for Growth

The healthcare industry is comprised of a diverse range of stakeholders– including providers, pharmaceutical companies, medical device companies, health tech startups, patients, and payers. Amid this complexity, Maneesh outlines the vital role of Mayo Clinic Platform as a trusted operator through its unwavering commitment to multi-stakeholder consensus-building and aligned principles that guide its innovative growth towards a more prosperous healthcare system for all.

Maneesh underscores the importance of creating a foundation based on shared principles—such as the betterment of care quality—which can enable diverse stakeholders to effectively collaborate and mobilize towards collective action for the benefit of the broader ecosystem. In tackling the most crucial goals of our shared humanity, healthcare innovation remains a frontier of technological advancement for social good. Trust is the guiding force in advancing the field through technological progress to enable high-quality care and exceed the growing needs of healthcare innovation.