In an era where healthcare is rapidly evolving through digital transformation, innovations in artificial intelligence are propelling the Life Sciences industry toward more equitable, ethical, and sustainable healthcare solutions. In this interview, Partha Anbil, Michael Wong, and Gabriele Ricci discussed how AI and digital technologies reshape healthcare landscapes, accelerate drug discovery, and enhance patient-centered experiences. They delve into the delicate balance between innovation, regulatory compliance, and ethical responsibility, shedding light on pioneering approaches to improve healthcare’s future trajectory.
AI and Digital Transformation: Pioneering Ethical and Sustainable Healthcare Innovations
Partha Anbil & Mike Wong engaged Gabriele Ricci in a thought-provoking discussion on the role of AI and digital transformation in healthcare. Partha’s questions explored how these technologies can reshape patient care, accelerate drug discovery, and navigate regulatory challenges, emphasizing the importance of ethical and equitable innovation in the Life Sciences industry.
How do you envision AI reshaping the healthcare landscape? What innovative measures are essential to build and maintain trust in AI-driven healthcare systems, ensuring these technologies are transformative and ethically sound?
Gabriele Ricci: Absolutely, I believe you’re right. Last year, we witnessed a rapid and widespread introduction of generative AI, creating massive awareness and adoption within healthcare and across various sectors globally. I had the opportunity to attend the World Economic Forum in Davos last year, where AI was omnipresent. It was discussed everywhere, paired alongside another crucial term: trust. This combination of “trust” and “AI” represented, perhaps for the first time, a collective recognition of their intrinsic link. Much like the internet has permeated every aspect of our lives, AI is poised to reshape healthcare, similarly, profoundly transforming everything from medicine discovery and clinical trials to disease diagnosis and production methods.
In healthcare, AI’s potential is vast, with examples of AI enhancing the effectiveness and efficiency of our processes and operations. I view AI as the “modern stethoscope.”
Like the stethoscope—a simple, cost-effective, and precise tool widely adopted by doctors—AI will become an indispensable instrument in healthcare. I envision AI being accessible, accurate, and trusted, serving as a daily utility for care providers in much the same way.
However, this transformation won’t happen overnight; trust in AI grows incrementally through responsible use and proven accuracy. At Takeda, we treat AI similarly to a digital intern: it’s first assigned simpler tasks, with oversight and validation of each output. As confidence in its accuracy grows, we assign more complex responsibilities, supervising the AI’s evolution and capabilities just as a human intern advances in their career. Eventually, we foresee AI seamlessly integrating into our workflow, but always with robust oversight to ensure alignment with our corporate values.
The trust-building process also involves a critical assessment of data quality. For instance, using datasets with an 80% accuracy rate may suffice for day-to-day business tasks, but it’s wholly insufficient for clinical applications. This underscores the importance of building internal sensitivity to the risks and implications associated with AI usage.
We must commit to responsible and transparent AI development and use to establish and maintain trust in AI-driven systems, particularly in a highly regulated industry like pharmaceuticals. Transparency is fundamental; our systems must clearly explain how they make decisions and process data, ensuring users understand the process and the rationale. Transparency also extends to accountability; as an organization, we take responsibility for the actions and outcomes of our AI systems, ensuring they operate ethically and in compliance with legal and regulatory requirements. Accountability is deeply ingrained in our framework, as demonstrated by Takeda’s enduring commitment to patient trust, business integrity, and a strong values-based culture over its 243-year history.
Beyond accountability, robustness is crucial. AI systems must be reliable and resilient, capable of handling unexpected situations and minimizing errors and biases. In a regulated industry, privacy and security remain paramount; protecting patient data and maintaining compliance with swiftly evolving privacy regulations across countries is essential to our commitment.
Finally, we emphasize fairness and bias mitigation, actively working to ensure our AI systems are equitable and non-discriminatory, delivering fair outcomes to all individuals. To reinforce these principles, we have embedded them within an ethical AI framework and established a unique role in the industry, the Digital Trust Officer. This role is instrumental in embedding our ethical AI principles throughout the lifecycle of each data-driven and digital product, reinforcing a culture of responsible AI use.
In this approach, human oversight is kept at the core of AI processes, ensuring alignment with our transparency, accountability, robustness, privacy, and fairness principles. By integrating these values into AI design, we build systems that earn trust over time, creating an ecosystem where AI serves responsibly and effectively.
How do you see the role of data and digital technologies in accelerating drug discovery and development? Can you share any innovative examples where these advancements have significantly impacted timelines or outcomes in the Life Sciences industry, particularly in increasing the success rates of clinical trials or expediting the journey from discovery to market?
Gabriele Ricci: The transformative potential of data and digital technologies in drug discovery and development is immense. These innovations are already enabling us to reimagine operations and improve both the speed and success rates of clinical trials, making the journey from discovery to market faster and more reliable. When I consider the impact of AI in life sciences, I see several key areas where it significantly reshapes outcomes.
- The first major impact area is operational efficiency. By leveraging advanced AI and automation, we can streamline processes and increase productivity while reducing manual intervention. For instance, at Takeda, we use AI tools to draft preliminary reports for studies, which human experts refine. This approach allows us to accelerate the process significantly without compromising quality.
- Additionally, generative AI enhances our research workflows by extracting and summarizing vast scientific data from multiple sources, which would be prohibitively time-consuming if done manually. AI is also optimizing our clinical trial processes; by analyzing global data on disease prevalence, we can more accurately identify optimal trial sites, enhancing recruitment speed and efficiency.
- AI’s role in scientific discovery is equally groundbreaking. In drug discovery, we apply AI to assess therapeutic targets and aid biomarker discovery, potentially increasing the likelihood of success in clinical trials. This precision in target identification and biomarker discovery means that we may design compounds with a higher probability of efficacy from the outset. A specific example from our narcolepsy clinical trials, such as TAC861, involves leveraging wearable devices to capture biomarker data related to sleep patterns, which allows us to develop predictive models and optimize patient engagement strategies.
- Another transformative use of AI is in manufacturing and supply chain management. Our collaboration with MIT, which resulted in multiple publications and patents, underscored AI’s potential to revolutionize the production and distribution of therapies. For instance, we’ve implemented AI-driven visual inspection systems for injectable products, achieving higher precision, compliance, and efficiency than manual inspections alone. Tasks that used to take hours can now be completed in milliseconds with unprecedented accuracy, reducing resource demands and enhancing our quality control.
- AI is also instrumental in improving market access. In the case of our dengue vaccine, QDenga, we use AI-driven predictive models that analyze environmental and public health data to forecast dengue outbreaks. This enables us to pre-position vaccine supplies in vulnerable areas, ensuring timely access to critical vaccines. This strategic use of data for inventory management allows us to respond dynamically to disease patterns, especially in regions with limited vaccine availability.
In your podcast with Deloitte, you emphasized the importance of digital transformation in creating a customer-centric mindset. Digital transformation is creating this mindset and this DNA. It’s putting the customer at the center. How can companies in the Life Sciences industry balance this focus on customer experience with the technical and regulatory challenges unique to the sector?
Gabriele Ricci: Balancing a solid focus on customer experience with the technical and regulatory demands of the life sciences industry and the sciences industry’s stringent technical and regulatory demands requires a multifaceted approach. At the core of it is a steadfast commitment to being truly customer-centric. This involves deeply understanding customers’ needs, expectations, and preferences and aligning our organizational objectives to deliver tailored products and services, particularly in the digital space.
One critical aspect of this balance is embedding regulatory compliance within the customer experience framework. This means ensuring every customer interaction, communication, and data-handling process fully complies with the evolving regulatory landscape. The complexities here are immense, as regulatory requirements vary significantly across regions and can change rapidly. A robust, compliance-driven foundation within the customer journey is essential to address this.
In parallel, investing in digital infrastructure and becoming a data-driven organization is paramount. For us, this has meant adopting cutting-edge technologies and developing internal competencies that enable self-sufficiency and future readiness. We’ve established in-house innovation capability centers serving as our digital factories. These centers allow us to build and scale digital solutions internally, driven by teams who understand our organizational values and the regulatory intricacies of our industry.
By cultivating these digital and innovation “muscles” in-house, retain control over critical processes, and maintain a consistent, compliant approach to customer engagement.
Ongoing training also plays a crucial role in balancing customer focus with compliance. We regularly train our teams on technical aspects and customer service skills, bridging the gap between regulatory requirements and the high customer experience standards we aim to achieve. This training is continuous and essential, especially in large organizations like ours, where alignment across all levels is necessary for seamless customer interactions.
Lastly, cross-functional collaboration is indispensable. Encouraging alignment and partnership between technical, regulatory, and customer-facing teams helps unify our approach. We create a more cohesive strategy that balances regulatory rigor with customer-centered innovation by bringing together these diverse perspectives.
These initiatives are woven into our organizational framework, forming the critical capabilities needed to be ready in the future. This approach supports regulatory compliance and ensures that our commitment to exceptional customer experience remains uncompromised.
Wrapping Up
Digital innovation and data-driven approaches are reshaping every aspect of drug development, clinical trials, and patient engagement. Insights from this discussion underscore the immense potential of AI and digital technologies to accelerate timelines, improve precision, and enhance patient outcomes. However, achieving a balance between innovation and regulatory compliance is crucial. As businesses increasingly invest in customer-centric strategies, the focus remains on building in-house digital capabilities, fostering cross-functional collaboration, and continuously training teams to bridge compliance with customer expectations.
Through these efforts, the industry can deliver transformative, compliant, and accessible healthcare solutions, paving the way for future advancements that meet and exceed the evolving needs of patients and healthcare systems worldwide.
Bios
Gabriele Ricci
Gabriele Ricci is a seasoned professional with more than 25 years of experience in Information Technology and Engineering within the Life Sciences sector, encompassing Pharmaceuticals, Vaccines, Diagnostics, Medical Devices, and Generics. Gabriele has held increasingly challenging roles, advancing from site-related activities to global system architecture and applications management. With an MBA, along with various certifications, he combines solid technical engineering fundamentals with entrepreneurial skills. A strong team player and change management advocate, Gabriele excels in multicultural environments across regions such as India, China, ASPAC, and LATAM. Areas of expertise include logistics processes, technology management, enterprise systems, and project/program management, with a focus on Digital Health, compliance, and process optimization methodologies, as well as workforce digital dexterity.
Partha Anbil is a Contributing Writer for the MIT Sloan Career Development Office and an alum of MIT Sloan. Besides being VP of Programs of the MIT Club of Delaware Valley, Partha is a long-time life sciences consulting industry veteran, currently with an NYSE-listed WNS, a digital-led business transformation company, as Senior Vice President and Practice Leader for their Life Sciences practice.
Michael Wong is a Contributing Writer for the MIT Sloan Career Development Office and an Emeritus Co-President and board member of the Harvard Business School Healthcare Alumni Association. Michael is a Part-time Lecturer for the Wharton Communication Program at the University of Pennsylvania and his ideas have been shared in the MIT Sloan Management Review and Harvard Business Review.
