In the present era of digitally advanced technologies and algorithms, data safety has become important due to rising security breaches. With the growing influence of big data and AI, balancing huge datasets and adhering to security measures is quite challenging. This is applicable for the healthcare domain too, as it is a prime hub of patients’ data. From clinical trials to treatment planning, every procedure needs to keep track of an individual’s information for diagnosis and analysis purposes.
These data are highly sensitive and carry the risk of data breaches and unauthorized access. Managing the thin line between transparency and security is tough, as it advances with tech evolution, changing patient demographics and needs, and dynamic regulations. This can be achieved by instituting strong principles at an early stage and implementing extensive governance.
With this motive of ensuring transparency and protecting patients’ security, Brian M. Green ventured into the niche while aiming to implement ethical AI in healthcare! The turning point came in his life during the pandemic, when public trust in the pharmaceutical industry, healthcare systems, and science had plummeted to historic lows. Brian shares, “I realized that we were at a transformative moment in history, where AI could either radically improve healthcare or worsen disparities, amplify harms, and fail to drive systemic change. I decided to help promote a different vision and established Health-Vision.AI, LLC and Envision-Health.AI.” With the motto of developing governance-first approaches and patient-first strategies in AI design, these firms provide healthcare solutions to a wide class of individuals and organizations.
Health-Vision.AI and Envision-Health.AI view healthcare innovation and AI governance as both a social responsibility and a business imperative. The missions of these firms are aligned through AI risk and impact assessments, responsible AI KPIs, governance checkpoints, and community co-design sessions.
The Journey of Brian and His Mission
After decades of working in healthcare, health information management, and digital innovation, Brian has been at the leading edge of industry-transformational change. He has witnessed the evolution from classical data analysis to predictive AI, and most recently, the explosive advance of generative AI and conversational agents in 2023. Brian became concerned about who was included and excluded in these developments, especially in the healthcare sector.
Brian saw both the tremendous promise and the built-in dangers AI represented: the possibility to transform care and justice, or the risk of complicating inequalities, accelerating bias, and hindering genuine reform.
Inspired by a mission to ethical innovation and systemic change, he established Health-Vision.AI, LLC in January 2024 as the Chief AI Ethics Officer. Early in 2025, Brian introduced Envision-Health.AI with two collaborators to develop an AI-powered tool that helps and empowers patients and caregivers.
The Dual Challenge of AI Transparency
According to Brian, the primary challenge is balancing the explainability needed for AI with data privacy and cybersecurity, which he refers to as the “Transparency-Containment Paradox.” In healthcare, transparency promotes trust, but disclosing too much about an AI model, especially in its inference layers and outputs, risks exposing sensitive patient data, intellectual property, or inviting adversarial attacks. It is crucial to continually calibrate the level of disclosure, determining what to share, with whom, and when.
Shaping the Future of Rare Disease Care with Envision-Health.AI
Envision-Health.AI has recently emerged from stealth mode, receiving positive feedback as its developments are shared with various audiences. The company is committed to empowering patients through transparent, ethical, and personalized AI solutions. It specifically addresses a critical challenge for individuals with rare diseases by improving patient-provider communication and providing comprehensive support throughout their care journeys.
With decades of experience in healthcare information, access, and patient advocacy, this venture holds deep personal significance. Before establishing Envision-Health.AI, Brian invested his efforts in almost four years researching and developing the business case for rare disease initiatives. Now, Envision-Health.AI is directly tackling these challenges, aiming to transform the years of uncertainty and isolation that many rare disease patients and caregivers face. The company is dedicated to guiding patients on a personalized healthcare journey.
Ensuring Fairness of Technologies for AI Development
While discussing technologies developed under his leadership, Brian explains, “We begin with a governance-first design philosophy. This approach guides both our client engagements and internal AI development work.” He further highlights the core factors the company emphasizes while working on AI readiness, integrations, or tool development. These aspects include comprehensive bias audits with automated and human red-teaming cycles. Secondly, multi-stakeholder involvement and reviews, including patients or patient advocacy groups.
The third factor is simulation testing using different clinical scenarios and workflow insertion points. And lastly, transparency, observability, and explainability must form the foundation of AI governance in healthcare, ensuring that AI systems are not only effective but also accountable and understandable to all stakeholders.
Brian says, “Our process includes maintaining thorough documentation and implementing feedback loops, ensuring systems evolve responsibly with ethical AI as their foundation.”
A Delicate Balance of Transparency and Security
Brian states, “While transparency is critical to AI governance, it is equally important to safeguard patient data, proprietary information, and other critical assets through robust security measures and permissions. This balance is both challenging and dynamic, as it evolves with technological advancements, shifting patient populations and needs, and changing regulations.”
Effective AI governance must adapt in response to these changes. However, by establishing strong foundational principles early on and integrating comprehensive governance throughout the AI lifecycle from development to post-deployment evaluation and continuous improvement, ethical decision-making becomes more manageable.
Aligning the Company’s Mission with the Societal Impact of AI in Healthcare
Brian conducts AI risk and impact assessments focusing on health equity and social impact, helping to ensure that their innovations benefit diverse populations. Additionally, they prioritize community co-design sessions that involve patient advocates and other stakeholders so that their solutions are shaped by the needs and voices of those directly affected.
Brian further mentions, “We are committed to leading with innovation while upholding social responsibility, prioritizing AI governance, and placing patients at the center of the work we do.”
How AI Governance Will Define the Next Era of Tech
Brian shares, “In five years, I believe AI governance will be as integral to product development as cybersecurity is today. Regulatory frameworks will mature, alongside participatory ethics models that empower patients, caregivers, and clinicians to shape emerging technologies. Beyond healthcare, AI will revolutionize clinical trial processes and methodologies—driven by innovations like digital twin models for cell therapies and personalized medicine.”
AI’s role is evolving from a purely algorithmic tool to a true collaborative partner, augmenting human capabilities. This transition demands accountability, transparency, and continuous ethical dialogue. Most importantly, disciplines such as philosophy, social and cultural studies, and political economy must contribute to these discussions while also being reshaped by AI’s influence.
Leading Through Learning
While discussing the strategy to cultivate a supportive and creative culture, Brian shares, “Leading two organizations has deepened my appreciation for lifelong learning, particularly through actively engaging and empowering stakeholders and team members. I intentionally create spaces where ideas can be safely challenged, fostering a culture rooted in curiosity, growth, and continuous learning. As an early-stage entrepreneur bootstrapping these ventures, I’ve depended heavily on a diverse and trusted network of mentors, mentees, and collaborators, whom I view as a source of insight and potential partnership.”
Brian intentionally surrounds himself with resilient, ethical, and inquisitive individuals who share a commitment to learning. He also values constructive debate and engages with those who hold different perspectives, believing that it helps facilitate his learning.
Evaluating the Performance and Ethical Compliance of AI Systems
For Brian, the most important performance metrics come from client, customer, and patient community feedback. Hearing stories of how a product or solution has positively impacted the workflow, healthcare system, or the experiences of providers and patients exceeds all other KPIS and business metrics. He reveals, “When a patient or caregiver shares how their daily life or health has improved, it satisfies your soul and fuels your momentum forward.”
As someone who has always valued data-informed insights, Brian can get obsessed with data, but there’s something profound about our human sense of “I know something is ethical” or “I understand success” when he sees it. It is fundamentally a blend of empathy, emotional intelligence, and genuine human connections.
Advice for Emerging Innovators
While sharing a piece of wisdom for the next generation of innovators, Brian says, “Always start with the questions: Whom is this technology intended to serve, whom could it harm, and how can we ensure that it provides real value?”
He adds, “Incorporate a robust AI governance framework from the earliest phases of development and sustain it throughout the AI lifecycle. Waiting for regulation to dictate standards is insufficient, but proactive governance is essential. Surround yourself with diverse voices beyond your traditional sphere, including ethicists, business analysts, socio-technical experts, and patients. These perspectives are critical to identifying blind spots and ensuring inclusive innovation. Ethics must be understood not as a checklist for compliance, but as the foundation on which responsible, sustainable, and socially beneficial AI in healthcare is built
