Insights & Perspectives
Exploring the intersection of digital health, AI, and clinical innovation. Here are my latest thoughts and findings from the field.
This episode moves beyond conceptual discussions of Artificial Intelligence (AI) and focuses entirely on execution, delivering a clear, actionable roadmap for successfully implementing AI strategies. The mission is to shift the focus from small pilot projects toward full-scale operational integration across the entire drug development life cycle. To succeed this decade, executives must prioritize three immediate strategic mandates:
Speed (rapid acceleration in clinical execution),
Assurance (meeting regulatory requirements through rigorous validation and data quality), and
Differentiation (using AI to drive superior product standing in the market).
The key strategic win lies in embracing AI not as pure automation, but as a powerful tool for augmenting human expertise across scientific inquiry and clinical decision-making
Key Takeaways:
Accelerate clinical execution by embedding optimization engines into trial design workflows to simulate millions of potential scenarios and reduce planning delays.
Ensure regulatory assurance by prioritizing rigorous cross-cohort model validation against independent real-world data sets to prove generalizability and meet regulatory hurdles.
Achieve product differentiation through tailored patient experiences and microintervention frameworks that address specific behavioral concepts like "discounting the future".
Unlock pipeline bottlenecks by investing in and rigorously validating patient-centric tools, such as Trial Specific Patient Decision Aids (TPDAs), to significantly boost comprehension in complex trials.
Prioritize strategies that genuinely augment human expertise (57% of AI use) by integrating AI into scientific inquiry and clinical decision-making, rather than just automating administrative tasks
Show Notes:
[0:00 - 1:00] R&D leaders must shift from AI concepts to execution, focusing on three strategic mandates: speed, assurance, and product differentiation.
[1:00 - 2:00] AI drives acceleration by operational streamlining, embedding optimization engines like Phase V's Trial Optimizer to simulate scenarios and reduce planning delays.
[2:00 - 3:00] Acceleration is maximized by virtual clinical trials, which use sophisticated modeling and real-world data (RWD) to predict results and implement cost-saving synthetic arms.
[3:00 - 4:00] The second mandate, Assurance, requires rigorous model validation strategies, such as cross-cohort validation using independent data sets like those employed for the complex Deli2M LLM.
[4:00 - 5:00] Ensuring trustworthy patient-facing AI requires interdisciplinary teams and techniques like RLHF to shape communication, minimize jargon, and actively prioritize patient comprehension.
[5:00 - 6:00] Differentiation is achieved via personalized, tailored experiences using microintervention software technology (DMIS) to address patient behavioral tendencies and boost program effectiveness.
[6:00 - 7:00] Patient decision aids (TPDAs) are a vital product iteration for R&D, significantly boosting patient comprehension and unlocking clinical pipeline bottlenecks during complex trial recruitment.
[7:00 - End] The overarching strategic takeaway is that AI's primary role is augmentation (57%), necessitating strategies that empower teams and integrate AI into scientific inquiry rather than focusing only on administrative automation.
Podcast generated with the help of NotebookLM
Source Articles:
Beyond the shopping list: Why flexible deal making fills biotech’s cart
Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations(Anthropic Working Paper)
eHealth Self-Management Interventions for Patients With Liver Cirrhosis: Scoping Review
Nature Article on Generative Transformer Architectures for Disease Modeling
HT4LL-20250916
Hey there,
Everyone's fixated on the price tag of new AI models, but they're completely missing the iceberg beneath the surface.
As an R&D leader, you're constantly pushed to innovate faster and bring down the costs of clinical trials. AI seems like the silver bullet, but the conversation often stalls at the software license fee. This narrow focus is exactly why so many AI projects underdeliver, run over budget, or create more problems than they solve. The truth is, the technology itself is often the cheapest part of the equation. The real investment, and the real risk, lies in the foundational infrastructure that determines whether these powerful tools will actually work in our highly regulated, human-centric world.
So today, we're going to break down the true cost of building a successful AI program. We'll cover:
The hidden price of data readiness and regulatory compliance.
Why your team's mindset is a critical budget line item.
The overlooked cost of making new tech work with old systems.
If you're trying to build a clear, defensible business case for AI in your R&D organization, then here are the resources you need to dig into to understand the full picture:
Weekly Resource List:
AI Anxiety and Its Impact on Work Passion (11 min read)
Summary: This academic study dives into how fears of job replacement and the pressure to constantly learn new AI tools can diminish employee passion and lead to emotional exhaustion.
Key Takeaway: Don't treat your team's well-being as a "soft" cost. Budgeting for service-oriented leadership training and programs that foster a learning culture is a hard infrastructure investment. Ignoring AI-related anxiety in your R&D teams will directly undermine the ROI of your technology spend.
A Multimodal Dataset for Head and Neck Cancer Research (6 min read)
Summary: This paper introduces a large, high-quality, multi-center dataset for training AI models in oncology. It highlights the immense effort required to collect, standardize, and annotate data from various sources.
Key Takeaway: High-quality, diverse data is the most critical and expensive piece of AI infrastructure. Your budget needs to reflect that. Prioritizing investment in data acquisition, curation, governance, and standardization is non-negotiable for building AI models that are accurate and generalizable.
Navigating the AI Regulatory Maze (5 min read)
Summary: An IQVIA report breaking down the complex and fragmented global AI regulatory landscape, contrasting the U.S. innovation-first approach with Europe's stringent, risk-based frameworks like the EU AI Act.
Key Takeaway: Compliance is a major infrastructure cost, not an afterthought. You must proactively budget for navigating different regional regulations. Investing in "smart regulatory intelligence" systems can prevent costly delays and ensure your AI-driven products can actually make it to market.
Study on Hurdles in Transforming NHS Health Care with AI (4 min read)
Summary: This UK study reveals that integrating AI into hospitals is far more complex and time-consuming than expected. Major hurdles include integrating with legacy IT systems, lengthy contracting, and significant staff skepticism.
Key Takeaway: Budget realistically for integration and change management. The cost of connecting a new AI tool to your existing systems and getting your team to actually use it can dwarf the cost of the tool itself. Dedicated project management isn't a luxury; it's essential.
A Personal Health Agent Multi-Agent AI Framework (40+ min read)
Summary: This paper details a sophisticated multi-agent AI designed for personalized health coaching. While highly effective, the authors explicitly note that its "substantial computational cost" and latency are significant barriers to scaling the solution.
Key Takeaway: The costs don't stop after implementation. Complex AI models are expensive to run. When evaluating vendors, you must analyze the total cost of ownership, including the ongoing computational resources required to operate the AI at scale.
3 Pillars to Budget For to Successfully Implement AI Even if Your Resources Are Limited
To de-risk your AI investment and actually see the efficiency gains you're after, you need to think far beyond the software license. A successful, scalable AI strategy requires you to strategically fund three core pillars that are often overlooked in initial budget conversations.
Here’s how to frame your investment:
1. Your People Infrastructure
The first thing you need is a budget for your team's readiness. This isn't a "soft" HR initiative; it's the foundational layer of your AI infrastructure.
As we saw in the studies on AI anxiety and the NHS rollout, the most sophisticated algorithm is worthless if your team is too skeptical, confused, or burned out to use it effectively. Resistance and low adoption are the fastest ways to kill your ROI. You must allocate dedicated funds for comprehensive training that goes beyond how to use a tool and explains why it's being used. Invest in leadership development for your managers so they can guide their teams through change, and create clear career paths that show scientists and researchers how AI enhances their roles, rather than replaces them.
2. Your Process Infrastructure
Next, you need to fund the invisible but essential processes that make AI work: data governance and regulatory compliance.
The HECKTOR dataset paper makes it clear: high-quality, standardized data is the lifeblood of effective AI. Garbage in, garbage out. Before you even think about an algorithm, you need a budget to get your data house in order. This means investing in curation, standardization across trial sites, and robust governance frameworks. Simultaneously, the IQVIA report shows that navigating the global regulatory maze is a mission-critical cost. Budget for dedicated expertise or AI-powered compliance tools to ensure your innovations aren't dead on arrival due to a regulatory misstep.
3. Your Platform Infrastructure
Finally, you need to budget for the technical realities of integration and computation.
The NHS study is a stark reminder that new AI tools rarely work "out of the box" with legacy IT systems. Integration is often a complex and costly project in its own right and must be a separate line item in your budget. Furthermore, as the Personal Health Agent paper highlights, the more powerful an AI model is, the more computationally expensive it is to run. Don't just look at the purchase price; evaluate the total cost of ownership (TCO), including the ongoing cloud and processing fees required to operate the model at scale.
PS...If you're enjoying Healthtech for Lifescience Leaders, please consider referring this edition to a friend.
And whenever you are ready, here are 2 ways I can help you:
AI Readiness Workshop: Let's get your team together for a half-day session to demystify AI and build a strategic roadmap for your R&D goals. Want a sneak peak, schedule a free Personal GenAI Strategic Roadmap session.
Strategic Advisory Call: Book a 1:1 call with me to stress-test your current AI strategy and identify low-risk, high-impact opportunities.
In today’s podcast, we deep dive into the intersection of artificial intelligence and healthcare, to explore concrete data initiatives, innovative AI architectures, and the human elements shaping pharmaceutical R&D. We discuss actionable strategies for executives to navigate complexities, capitalize on AI opportunities, and drive patient outcomes through strategic investments, empowering teams, and fostering innovation. The discussion covers the full innovation ecosystem, from foundational data and managing AI anxiety to the development of personalized multi-agent health AI.
Key Takeaways:
• Foundational to AI breakthroughs is the strategic investment in robust, multimodal data infrastructures, which mitigates bias, ensures generalizability, and derisks regulatory pathways for new diagnostics and treatments.
• Addressing AI anxiety within R&D teams through human-centric Service-Oriented Leadership and fostering a Learning Goal Orientation is crucial for accelerating clinical trials, boosting work passion, and achieving product differentiation.
• Cutting-edge multi-agent AI frameworks, exemplified by the Personal Health Agent (PHA), offer truly personalized health recommendations and dynamic patient engagement, presenting a significant opportunity for enhancing patient adherence, real-time monitoring, and generating robust real-world evidence.
• Successfully integrating AI in pharma R&D requires a strategic roadmap that thoughtfully combines investment in robust multimodal data, human-centric leadership, and sophisticated AI frameworks to transform patient care and maximize ROI.
Show Notes:
• [0:00 - 0:55] Explore data initiatives, AI architectures, and the human elements shaping pharma R&D, focusing on strategic investments and patient outcomes.
• [0:55 - 1:50] Highlighting a groundbreaking multimodal cancer dataset, managing AI anxiety, and developing personal multi-agent health AI.
• [1:50 - 2:45] Blueprint for data quality and scale, mitigating biases and ensuring AI models perform well across diverse patient groups.
• [2:45 - 3:40] Dose distribution, and comprehensive clinical metadata, critical for developing genuinely robust and generalizable AI models.
• [3:40 - 4:35] High-quality, diverse data crucial for derisking regulatory pathways and boosting confidence in clinical utility.
• [4:35 - 5:30] Rigorous data quality assurance and standardized annotation processes upfront help save millions.
• [5:30 - 6:25] AI anxiety, manifesting as Job Replacement Anxiety (JRA) and Learning Anxiety (LA).
• [6:25 - 7:20] Adopting Service-Oriented Leadership (SOL) is a critical strategy to empower teams.
• [7:20 - 8:15] Cultivating a Learning Goal Orientation (LGO) in employees helps them view stress and failure as learning opportunities.
• [8:15 - 9:10] The Personal Health Agent (PHA), a cutting-edge multi-agent framework using robust large language models (LLMs).
• [9:10 - 10:05] The PHA's sophisticated, modular architecture includes three specialist sub-agents
• [10:05 - 11:00] PHA agents significantly outperformed baseline general-purpose LLMs in evaluations.
• [11:00 - 11:55] PHA is explicitly designed to support clinical expertise, not replace it.
• [11:55 - 12:50] AI frameworks to transform patient care from broad strokes to precise, individualized support.
• [12:50 - 13:45] Leaders in pharma R&D must drive both clinical innovation and employee well-being.
Podcast created with NotebookLM
Source Articles:
A Multimodal Head and Neck Cancer Dataset for AI-Driven Precision Oncology
Navigating the AI regulatory maze: Expert perspectives from healthcare executives - Part 1 - IQVIA
Study sheds light on hurdles faced in transforming NHS health care with AI
The impact of AI anxiety on employees' work passion: A moderated mediated effect model
HT4LL-20250909
Hey there,
Most pharma R&D teams are treating AI like a science project instead of the most powerful competitive weapon we've seen in decades.
We’re all facing the same relentless pressures: clinical trials that are too slow and expensive, a deluge of complex data we can’t make sense of, and the constant demand to deliver differentiated products in a crowded market. Many leaders I speak with are stuck in an endless loop of pilots and proofs-of-concept, paralyzed by concerns over AI’s reliability, data privacy, and a lack of in-house understanding. The critical mistake isn't a lack of interest in AI; it's a lack of strategic deployment that separates the leaders from the laggards.
Today, we’re going to talk about how to move from AI experimentation to true competitive dominance. We'll explore how to:
Build trust in your AI, not just chase accuracy metrics.
Design for real-world scale, not just clean lab data.
Solve a single, high-value problem to accelerate your entire pipeline.
If you're an R&D executive trying to cut through the hype and find a pragmatic path to leveraging AI for faster, more cost-effective trials, then here are the resources you need to dig into to gain a real competitive advantage.
Weekly Resource List:
Explainable machine learning models for early Alzheimer’s disease detection (Approx. 15 min read)
Summary: This study showcases an AI model that detects early Alzheimer's with 95% accuracy by analyzing a wide range of patient data. Critically, it uses Explainable AI (XAI) techniques, moving beyond the "black-box" to show clinicians why it made a specific prediction. The model highlights factors like functional assessments and memory complaints as key predictors, making the AI’s output transparent and trustworthy for clinical use.
Key Takeaways: To gain an edge, integrate XAI frameworks to build trust and accelerate clinical adoption. Prioritize developing models that use comprehensive, multimodal data for higher accuracy. And most importantly, validate all AI-driven insights with clinical experts to ensure your tools are reliable and truly useful in practice.
An artificial intelligence cloud platform for OCT-based retinal anomalies screening (Approx. 13 min read)
Summary: This paper details AI-PORAS, a cloud-based AI platform that screens for 15 different retinal anomalies with the accuracy of a trained ophthalmologist. Deployed across over 200 medical institutions, it has already diagnosed more than 116,000 patients remotely, proving its immense scalability and clinical value, especially in areas with a shortage of specialists. The key to its success is its ability to adapt to data from various types of scanners and evolving diagnostic criteria.
Key Takeaways: Invest in cloud-based platforms to scale your AI solutions globally and reach underserved markets. Engineer your models for "domain adaptation" so they remain robust when faced with diverse real-world data. Use the data collected from these deployments to continuously refine your models and gather insights on disease patterns.
From Hype to Impact: Unlocking AI's Full Potential in Asia-Pacific Pharma (Approx. 2 min read)
Summary: This IQVIA white paper argues that to stay competitive, pharma companies must move beyond isolated AI projects and integrate "agentic AI" across the entire value chain—from discovery to manufacturing and medical affairs. It stresses that overcoming legacy systems and talent gaps is crucial for using AI to manage complex trials, enhance supply chain resilience, and ultimately deliver precision medicine.
Key Takeaways: A competitive advantage comes from a holistic strategy. Deploy AI across the entire R&D and commercialization pipeline. Invest heavily in scaling your digital infrastructure and, just as importantly, in training your teams to build AI literacy and data science expertise.
How Cleveland Clinic Is Speeding Up Clinical Trial Recruitment (Approx. 7 min read)
Summary: Cleveland Clinic has partnered with AI startup Dyania Health to tackle one of the industry's biggest bottlenecks: patient recruitment. Their AI platform uses medically trained LLMs to scan both structured and unstructured EHR data. In a pilot, the AI identified an eligible patient in just 2.5 minutes with 96% accuracy, a task that took a nurse over 400 minutes to achieve with similar accuracy.
Key Takeaways: Deploy specialized LLMs to crush critical bottlenecks like trial recruitment. The real power lies in harnessing unstructured data (like doctor’s notes) to find patients that traditional methods miss. Foster strategic partnerships with AI startups to gain a first-mover advantage.
Moving Beyond the Model: Our Perspective on Meaningful AI Research in Cardiovascular Care (Approx. 6 min read)
Summary: This editorial from a leading cardiology journal (JACC) provides a crucial framework for AI development. It argues that the research community—and by extension, industry—must move beyond simply reporting high accuracy scores. To be meaningful, AI research must demonstrate a clear path to clinical implementation, prove its real-world value, and be built on a foundation of transparency, interpretability, and reproducibility.
Key Takeaways: For a durable competitive advantage, link every AI project to an unmet clinical need and a clear implementation plan. Emphasize transparency and interpretability to build trust with clinicians and regulators. Use comprehensive performance metrics that reflect actual clinical impact, not just technical prowess.
3 Ways To Gain a Competitive AI Edge With Deeper Insights Even If You're Starting Cautiously
To turn your AI initiatives from costly experiments into true competitive differentiators, you're going to need a strategic shift in focus. It's less about having the most complex models and more about having the smartest deployment strategy.
Here’s where to start.
1. Build for Trust, Not Just Accuracy
The first thing you need is a commitment to explainability. Your clinicians and regulatory bodies will not adopt a "black box," no matter how accurate it is. The fear of the unknown is one of the biggest hurdles to adoption.
This is where Explainable AI (XAI) comes in. As seen in the Alzheimer’s study, XAI frameworks provide a window into the model's "thinking," showing which data points (like MMSE scores or patient-reported memory issues) most influenced its conclusion. This transparency does two things: First, it allows clinical experts to validate the model's logic against their own knowledge, building essential trust. Second, it de-risks the technology by making it auditable and easier to troubleshoot. A model that is 95% accurate and 100% transparent is infinitely more valuable than one that is 99% accurate and 0% transparent.
2. Design for Scale, Not a Sterile Lab
Next, you need to build for the messy, unpredictable reality of global healthcare. A model that only works on perfectly curated data from a single machine is a lab pet, not a workhorse.
The competitive advantage lies in building robust, scalable systems like the AI-PORAS ophthalmology platform. This means prioritizing two things from day one: a cloud-based architecture and domain adaptation. A cloud platform allows you to deploy your solution anywhere in the world, breaking down geographical barriers. Domain adaptation techniques ensure your AI performs reliably even when fed data from different hospitals, older equipment, or diverse patient populations. By planning for this heterogeneity from the start, you build a solution that is ready for the real world, giving you a massive advantage in speed-to-market and reach.
3. Target One Painful Bottleneck and Solve It Completely
Finally, instead of trying to sprinkle AI everywhere, you need to focus its power on a single, high-value bottleneck. The biggest wins come from solving one problem completely, not ten problems partially.
The Cleveland Clinic's approach to trial recruitment is a perfect blueprint. They didn't try to build a general-purpose diagnostic AI. They targeted the single process that universally slows down drug development and applied a powerful tool—a medically-trained LLM—to solve it with surgical precision. By processing unstructured EHR data, their AI unlocked a pool of eligible patients that was previously invisible, turning a 400-minute manual task into a 2.5-minute automated one. This kind of focused, high-impact application delivers a clear and massive ROI, builds incredible internal momentum, and creates a tangible competitive advantage you can measure in months saved and dollars earned.
Leading transformation with AI at work first starts with you. Want to know if you are truly taking advantage of GenAI? Sign up to beta test my GenAI Personal Compass Assessment and get a roadmap to upskill yourself.
PS...If you're enjoying Healthtech for Lifescience Leaders, please consider referring this edition to a friend.
And whenever you are ready, schedule time to get a free advisory consultation.
Welcome to the debate! This episode confronts the escalating pressures in pharmaceutical R&D—soaring costs, complex trials, and a deluge of biomedical data. We explore a pivotal question: Is Artificial Intelligence the transformative change the industry needs, or does its adoption introduce more complexity and risk?
Our discussion navigates the undeniable potential of AI against the significant practical hurdles of its implementation. We debate whether AI is the crucial differentiator for competitive advantage or if true leadership requires a more meticulous, ethical, and patient-centric approach that goes beyond mere technological adoption.
Key Takeaways:
Embrace AI for a Decisive Competitive Edge in Clinical Trials.
Prioritize Explainable AI (XAI) and Rigorous Validation to Build Trust.
Look Beyond Operational Efficiency to AI-Driven Market Differentiation.
Invest in a Holistic, Integrated AI Strategy—Not Isolated Solutions.
Highlights:
[0:45] Introduction to the central debate
[1:45] Accelerating Clinical Trials
[2:45] Operational Efficiency and Patient-Centricity
[3:45] The Friction of Real-World Deployment
[4:45] Ensuring Data Quality with Explainable AI (XAI)
[5:45] Limitations and Inconsistencies in XAI
[6:45] AI for Market Differentiation
[7:45] The Emergence of Agentic AI
[8:45] A Measured View on Market Dominance
[9:45] Final Summary: A Holistic Strategy is Key
Podcast created with NotebookLM
Source Articles Used for the podcast:
Driving Transformation With a Connected Clinical Platform: 2025 Veeva R&D and Quality Summit Clinical Opening Keynote (Published on Applied Clinical Trials Online)
From Hype to Impact: Unlocking AI's Full Potential in Asia-Pacific Pharma (Published by IQVIA)
How Cleveland Clinic Is Speeding Up Clinical Trial Recruitment (Published by Healthcare Innovation)
Reimagining Clinical Trials for Remote Populations (Published on Applied Clinical Trials Online)
Moving Beyond the Model: Our Perspective on Meaningful AI Research in Cardiovascular Care (Published in JACC)
"Shortage of professionals with expertise in both AI and life sciences complicates adoption" (Published by BioSpectrum)
HT4LL-20250902
Hey there,
Waiting for regulators to hand us a perfect AI roadmap is the single biggest mistake we can make right now.
The promise of AI to revolutionize clinical trials is undeniable—faster recruitment, better risk assessment, non-invasive monitoring. Yet, every step forward feels like a step into a regulatory fog. We're grappling with "black box" models, the very real risk of AI hallucinations compromising patient safety, and the fundamental challenge of proving AI's value in a way that will satisfy the FDA or EMA. This uncertainty doesn't just slow down innovation; it creates a tangible risk for our entire R&D pipeline.
So, how do we move forward with confidence when the path isn't fully paved? Today, we’re going to talk about:
How to build an internal framework that proves AI's value and safety.
Why human oversight is non-negotiable for mitigating risks like hallucination.
What it takes to move from a reactive to a proactive regulatory strategy.
If you’re an R&D leader trying to balance groundbreaking innovation with the pragmatic need for regulatory approval, then here are the resources you need to dig into to build a future-proof AI strategy:
Weekly Resource List:
Artificial intelligence and clinical trials: a framework for effective adoption (8 min read)
Summary: This article argues that the biggest barrier to AI adoption in clinical trials isn't the technology itself, but the lack of a standardized way to measure its value. It calls for a value-based framework, developed with patient input, to quantify AI's impact beyond simple cost savings, ensuring we can prove its worth to stakeholders and regulators.
Key Takeaways: You must lead the charge in defining how to measure AI’s value, focusing on trial informativeness and patient-centric outcomes, not just operational efficiency. Proactively creating these metrics internally will give you a massive head start in regulatory conversations.
A scoping review of artificial intelligence applications in clinical trial risk assessment (30 min read)
Summary: A deep dive into 142 studies reveals that while AI is increasingly used for risk assessment, many models are built on biased, retrospective data and evaluated with potentially misleading metrics. The authors stress the need for higher quality data, more robust evaluation, and integrated models that assess risk holistically.
Key Takeaways: Your AI is only as good as your data. It's critical to invest in curating diverse, high-quality datasets and mandate the use of stronger evaluation metrics (like F1-score or MCC) to truly understand your model's performance and present a credible case to regulators.
Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning (33 min read)
Summary: This study showcases the power of using wearable sensor data and ML to predict glucose levels non-invasively, a huge step forward for digital biomarkers. However, its success in a small, healthy cohort highlights the major hurdle ahead: validating these tools across diverse, real-world patient populations.
Key Takeaways: The future involves non-invasive monitoring, but regulatory approval will hinge entirely on generalizability. Prioritize funding extensive validation studies in varied populations and incorporate explainable AI (XAI) to build the clinical trust necessary for adoption.
The algorithmic consultant: a new era of clinical AI calls for a new workforce of physician-algorithm specialists (14 min read)
Summary: This piece proposes a new specialist role—the "algorithmic consultant"—to bridge the gap between complex AI tools and clinicians. Arguing that direct physician-AI interaction is often flawed, this expert would oversee AI selection, interpretation, and governance, ensuring safe and ethical deployment.
Key Takeaways: Stop designing AI tools just for physicians. Anticipate the need for expert intermediaries. Your AI solutions should have interfaces and auditable features built for these specialists, which will de-risk adoption for health systems and simplify liability concerns.
Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support (20 min read)
Summary: This sobering study reveals that even sophisticated LLMs have an alarmingly high hallucination rate (up to 82%) when fed fabricated clinical details. Simple fixes like prompt engineering help but don't solve the problem, posing a significant risk to patient safety.
Key Takeaways: The risk of AI hallucination is not a theoretical problem; it's a clear and present danger. You must build rigorous, multi-layered validation pipelines with human-in-the-loop oversight to catch these errors. Documenting this process is non-negotiable for proving safety to regulators.
3 Pillars for Building a Regulatory-Ready AI Strategy (Even When the Rules Aren't Clear)
In order to confidently deploy AI in our clinical trials and get ahead of regulatory scrutiny, we need to shift our thinking from compliance to strategic preparation.
Here’s how to build a robust foundation for your AI initiatives that will stand up to the inevitable questions from regulators.
1. Build an Ironclad Evidence Framework
The first thing you need is a rigorous internal process for proving that your AI tools are not just efficient, but safe and effective. This goes far beyond a simple accuracy score.
You need to establish a value-based framework that quantifies AI's impact on trial informativeness and patient outcomes, not just cost-minimization. This means adopting more robust evaluation metrics (like F1-score or Matthew’s Correlation Coefficient) that are suited for complex, imbalanced clinical data. Most importantly, this framework must include adversarial testing to actively find and measure your model’s breaking points, especially for risks like LLM hallucinations. Think of it as building a comprehensive regulatory dossier before you're ever asked for one.
2. Design for Expert Human Oversight
The future of clinical AI isn't a world without doctors; it's a world with new kinds of experts. The idea that we can create a "black box" so perfect that any clinician can use it flawlessly is a fantasy.
Instead, we must design our AI systems for a new specialist: the "algorithmic consultant." This is a physician-data science hybrid who can interpret, validate, and manage AI tools at the point of care. This "human-in-the-loop" model is our most reliable safety net. It acknowledges AI's inherent limitations and creates a system of checks and balances that regulators will find far more compelling than any claim of algorithmic perfection.
3. Shift from Passive Compliance to Proactive Engagement
Finally, you cannot afford to sit back and wait for the regulatory landscape to solidify. By the time the final rules are written, you'll already be behind.
Leaders in this space have an opportunity to help write the rules. This means actively engaging with regulatory bodies like the FDA and EMA. Share your internal validation methodologies and data on AI performance and safety. Participate in industry consortiums to help establish standards for value assessment and risk mitigation. By demonstrating a deep commitment to transparency and evidence-based innovation, you can help shape a regulatory future that is both rigorous and supportive, rather than one that is reactive and stifling.
Leading transformation with AI at work first starts with you. Want to truly take advantage of the GenAI tools and increase your productivity? Sign up to beta test my GenAI Personal Compass Assessment and get a roadmap to upskill yourself.
PS...If you're enjoying Healthtech for Lifescience Leaders, please consider referring this edition to a friend.
And whenever you are ready, schedule time to get a free advisory consultation.
Welcome to the Health Tech Dose! In this episode, we dive into the transformative yet complex world of AI in pharmaceutical R&D, focusing on how to harness its power while navigating the evolving regulatory landscape. We explore actionable strategies for pharma executives to leverage AI's potential, making ethical, evidence-informed decisions that drive innovation and improve patient outcomes.
As AI reshapes pharmaceutical R&D, navigating the evolving regulatory landscape is a critical challenge. For AI-driven insights to gain regulatory approval, companies must build a strong foundation of trust through transparency and rigorous validation. The core issue lies in proving the safety, efficacy, and fairness of these complex systems. This demands a strategic focus on robust data governance to ensure that the vast datasets used are high-quality, diverse, and truly representative of patient populations. A major hurdle is overcoming model limitations like hidden biases and ensuring that performance on paper translates to the real world. Success in this new era hinges on creating clear frameworks and audit processes that can satisfy regulatory scrutiny, guaranteeing that AI tools are not only innovative but also verifiably safe and effective for all.
Key Takeaways:
◦ Proactive Regulatory Engagement: Engage early and often with regulators, providing clear validation evidence.
◦ Invest in Data Integrity: Prioritize diverse, representative data and robust evaluation metrics for generalizability.
◦ Develop a Specialized Workforce: Integrate roles like algorithmic consultants for responsible deployment.
◦ Patient-Centered and Ethical Design: Treat AI solutions as products with a servant leadership mindset, focusing on user needs, transparency, and bias mitigation to improve outcomes and avoid digital disparities.
Highlights:
[00:20] The Importance of Trust, Validation, and Responsible Innovation
[01:05] AI Accelerating Clinical Trials: Predictive Modeling for Risk Assessment
[03:20] Drug Repurposing: Shortening Development Timelines and Reducing Costs
[05:15] Treating AI Solutions as Products: Rigorous Development and Ethical Application
[06:00] Ensuring High-Quality Data for Regulatory Approvals
[07:10] The Risk of "Elusory Generalizability"
[09:00] Data Quality and Validation as Ethical Responsibilities
[09:20] Data Governance for Trust and Equitable AI Application
[10:40] Identifying Unmet Needs and Novel Therapeutic Modalities
[11:20] The Need for a Specialized Workforce: Algorithmic Consultants
[12:50] The Broader Strategic Insight: Addressing Ethical Concerns and Building a Skilled Workforce
[13:50] Key Takeaways: Proactive Engagement, Data Integrity, Specialized Workforce, Patient-Centered Design
[15:00] Concluding Thought: Empowering Teams for Scientific Integrity and Equitable Access
Podcast created with NotebookLM
Source Articles Used for the podcast:
Remote monitoring in older adults with cancer, opportunities and challenges: a narrative review
Artificial intelligence and clinical trials: a framework for effective adoption
Breaking Barriers: Drug Repurposing Advances in Oncology - BIOENGINEER.ORG
A scoping review of artificial intelligence applications in clinical trial risk assessment
Systematic review and meta-analysis of artificial intelligence for image-based lung cancer ...
Using generative AI to create synthetic data - Stanford Medicine
Integrating artificial intelligence into medical education: a narrative systematic review of ...
Developing Requirements for a Digital Self-Care Intervention for Adults With Heart Failure
AI Enhances Personalized Cancer Treatment Recommendations - BIOENGINEER.ORG
How CHART (Chatbot Assessment Reporting Tool) can help to advance clinical ... - The Lancet
A deepening digital divide in cardiovascular disease management | Nature Reviews Cardiology
Cleveland Clinic and Dyania Health Partner to Accelerate Clinical Trial Recruitment with AI
a new era of clinical AI calls for a new workforce of physician-algorithm specialists - Nature
When Neutrality Conceals Bias: Perceived Discrimination in Algorithmic Decisions
Opportunities for Pragmatic Design Elements in Surgical Trials | Surgery - JAMA Network
HT4LL-20250826
Hey there,
Our current approach to AI is costing us millions, and it’s not because the technology is too expensive. We’re over-investing in static, generic AI systems that fail to deliver a tangible return and under-investing in the foundational data infrastructure that would truly accelerate our R&D. Without a strategic shift, we risk falling into the "GenAI Divide" where we realize zero ROI from our AI investments.
So today, we're talking about how to reduce R&D costs with AI.
Why we need to move from generic AI to context-aware systems.
How better data can prevent costly trial failures.
The surprising place AI can deliver the biggest savings.
The fastest path to reduced R&D costs isn't by cutting headcount, it's by reducing inefficiency and failed experiments.
If you're a pharma exec focused on accelerating clinical trials, ensuring high-quality data for regulatory approvals, and differentiating your products in a competitive market, then here are the resources you need to dig into to reduce your R&D costs with AI:
Weekly Resource List:
JAMA: The PRO-DUCE Randomized Clinical Trial (12 min read)
Summary: The PRO-DUCE trial demonstrated that using electronic patient-reported outcomes (ePROs) and vital sign monitoring improved the quality of life (QOL) for patients in a clinical trial. This proactive monitoring helped in the early detection and management of adverse events, which kept more patients on study.
Key Takeaways: Integrating ePROs and remote vital sign monitoring can significantly reduce trial costs by improving patient retention and minimizing treatment discontinuations due to unmanaged side effects. Leveraging this real-time data also helps us optimize therapeutic regimens and enhance the depth of our real-world evidence.
npj Digital Medicine: Synthetic clinical data generation (15 min read)
Summary: This study introduces an end-to-end pipeline for generating high-fidelity, privacy-preserving "digital twin" datasets from complex electronic health records (EHR) and wearable data. The method, called DataSifter, showed superior privacy protection while preserving key statistical fidelity, making it a robust solution for secure biomedical data sharing.
Key Takeaways: Generating high-fidelity, privacy-preserving synthetic data can enable faster and more secure data sharing across our R&D teams and with external collaborators. This drastically reduces the time and cost associated with stringent data access permissions, accelerating early-stage research and model development without compromising patient privacy.
arXiv: Generative Medical Event Models Improve with Scale (5 min read)
Summary: The CoMET models are a new family of large-scale generative medical event models trained on 300 million patient records. These models can simulate patient health timelines and predict disease progression and treatment response without extensive fine-tuning. Their predictive power consistently improves with scale, outperforming or matching supervised models across 78 diverse real-world tasks.
Key Takeaways: We should explore adopting large-scale generative models like CoMET to simulate patient journeys and optimize clinical trial design. Their generalizability can drastically minimize the time and cost associated with building and maintaining numerous bespoke AI models for various research questions, helping us streamline the AI adoption process.
The GenAI Divide: STATE OF AI IN BUSINESS 2025 (20 min read)
Summary: This report reveals a "GenAI Divide" where 95% of organizations realize zero ROI on their GenAI investments due to a "learning gap." The report highlights that most AI systems fail to retain feedback or adapt to context. Successful organizations, in contrast, prioritize deeply customized, learning-capable systems that deliver measurable ROI, particularly in back-office automation.
Key Takeaways: To avoid the "GenAI Divide," we need to shift our investment from static, generic AI tools to learning-capable, context-aware systems that adapt to our specific R&D workflows. We should also target back-office and operational functions for AI automation, as they often yield higher, more measurable ROI.
Efficiency of AI in the diagnosis of cognitive disorders (6 min read)
Summary: This article highlights how AI can revolutionize the early diagnosis of cognitive diseases like Alzheimer's. A machine learning system, integrating brain MRI and genetic data, achieved high accuracy (87.5% balanced accuracy). The article emphasizes that AI should augment human expertise, not replace it, by optimizing workflows and reducing diagnostic errors.
Key Takeaways: Integrating AI/ML for early and precise disease diagnosis can minimize R&D expenditure on failed clinical trials by enabling earlier intervention and better patient stratification. We must also prioritize interpretable AI systems to ensure transparency and trust for clinical and regulatory acceptance.
3 Strategic Moves to Dramatically Cut R&D Costs, Even With Budget Constraints
We all feel the pressure to reduce R&D costs. But a penny-pinching mindset won’t get us there. The real cost savings will come from a strategic shift in how we use AI to eliminate the most expensive and inefficient parts of our process: failed experiments, redundant workflows, and unmanaged patient drop-off.
Here are three key areas where you can leverage AI to dramatically cut R&D costs.
1. Stop Building Bespoke Tools and Start Investing in AI with Scale
For years, we've built a vast number of bespoke, task-specific tools for every new research question that comes up. This "one-off" approach is incredibly time-consuming and expensive to build and maintain. When you factor in the inevitable failures and re-designs, the costs skyrocket, trapping us in a cycle of expensive inefficiency.
Instead, we need to leverage large-scale generative models like CoMET, which are being trained on vast datasets of patient records. These foundation models can predict disease progression and treatment response across a wide range of tasks without requiring extensive fine-tuning for each new project. By adopting and investing in these scalable models, we can fundamentally reduce the development costs and time associated with building and maintaining dozens of individual AI tools. This shift not only streamlines our operations but also ensures our insights become more accurate and robust over time, consistently minimizing costly errors in drug discovery and development.
2. Leverage Patient Data to Protect Your Most Valuable Asset: The Clinical Trial
One of the biggest hidden costs in a clinical trial is patient attrition. When a patient drops out due to an unmanaged adverse event or poor quality of life, we lose valuable data, time, and resources. Our current episodic monitoring methods are simply not enough to prevent these costly trial failures.
To address this, we need to integrate electronic patient-reported outcomes (ePROs) and remote vital sign monitoring into our trial designs. As shown in the PRO-DUCE trial, this approach allows us to proactively manage adverse events and enhance patient QOL. By catching issues early and intervening with precision, we improve patient retention and minimize treatment discontinuations. This shift to continuous, patient-centric monitoring directly reduces trial costs and preserves the integrity of our most valuable asset: the clinical trial data itself.
3. Automate the Back Office, Not Just the Science
Many of us are looking for AI to solve our biggest scientific challenges, but the reality is that the most immediate and measurable ROI from AI is often found in the back office. Our research and development processes are bogged down by administrative, manual, and repetitive tasks—from data curation and literature review to process compliance. These workflows are high-friction, expensive, and a perfect target for AI automation.
We must prioritize AI solutions that target operational and "back-office" R&D functions. For example, use synthetic data generation tools like DataSifter to automate the creation of privacy-preserving datasets for early-stage research. This eliminates the time and cost associated with manual data de-identification and access permissions. By tackling these high-friction processes with AI, we free up our expert scientists to focus on true innovation, directly contributing to cost savings and accelerating our breakthroughs.
Do you feel the GenAI divide personally? Want to truly take advantage of the GenAI tools and increase your productivity? Sign up to beta test my GenAI Personal Compass Assessment and get a roadmap to upskill yourself.
PS...If you're enjoying Healthtech for Lifescience Leaders, please consider referring this edition to a friend.
And whenever you are ready, schedule time to get a free advisory consultation.
In this insightful episode, we explore the evolving landscape of pharmaceutical R&D and how modern technologies are reshaping drug development processes. We begin by examining how AI is revolutionizing clinical trial design through improved patient stratification and early intervention strategies, illustrated through groundbreaking studies in multiple sclerosis and insulin resistance prediction. We also delve into critical challenges around data quality and regulatory compliance, exploring innovative solutions like digital twin datasets that enable secure data sharing while maintaining privacy. Finally, we discuss how R&D innovation translates into market differentiation, including fascinating developments in AI-driven diagnostics and the crucial elements for successful technology implementation. This episode offers valuable insights for R&D executives, healthcare technology leaders, and anyone interested in the future of pharmaceutical development and healthcare innovation.
Highlights:
[00:59] - Exploring the transformation of clinical trials through AI-driven patient stratification
[01:41] - Understanding the breakthrough MS study challenging traditional disease categorization
[03:32] - Diving into wearable technology's role in predicting insulin resistance
[05:18] - Examining data quality challenges and privacy solutions in healthcare
[07:20] - Understanding the potential of digital twin datasets for secure data sharing
[09:12] - Exploring Google DeepMind's GMIE and its impact on medical consultations
[11:10] - Understanding the "Gen AI divide" and implementation challenges
[12:08] - Discussing strategies for successful AI integration in healthcare workflows
[13:01] - Examining how digital transformation is reshaping the entire R&D process
[13:38] - Exploring the vision for more accessible and personalized healthcare through technology
Podcast created with NotebookLM
Source Articles Used for the podcast:
Efficiency of artificial intelligence in the diagnosis of cognitive disorders
https://doi.org/10.1016/j.procs.2025.07.229
The GIST AI model redefines multiple sclerosis as a continuum with dynamic stages instead of subtypes
https://medicalxpress.com/news/2025-08-ai-redefines-multiple-sclerosis-continuum.html
Challenges and standardisation strategies for sensor-based data collection for digital phenotyping
https://doi.org/10.1038/s43856-025-01013
Dosing Algorithms for Insulin Pumps
https://doi.org/10.2337/dsi25-0004
AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations
https://www.jmir.org/2025/27/e66100
Medical data sharing and synthetic clinical data generation – maximizing biomedical resource utilization and minimizing participant re-identification risks
https://doi.org/10.1038/s41746-025-01935-1
Generative Medical Event Models Improve with Scale
◦ https://doi.org/10.48550/arXiv.2508.12104
Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers
https://arxiv.org/pdf/2505.03784
Towards physician-centered oversight of conversational diagnostic AI
https://arxiv.org/pdf/2507.15743
Application of Digital Tools in the Care of Patients With Diabetes: Scoping Review
https://www.jmir.org/2025/1/e72167
HT4LL-20250819
Hey there,
The promise of AI to transform R&D is only as strong as the data it’s trained on. While we're all excited about the latest AI models, we're not talking enough about the fragmented, messy data that’s preventing us from making any real progress. Without a solid, ethical, and representative data foundation, even the most advanced AI is unreliable and useless.
So today, we're talking about how to fix our data strategy to unlock the true potential of AI.
The shift from passive data storage to active data utilization.
How to build trust-based governance frameworks to access the data you need.
Why a pragmatic approach to AI is the only way forward.
AI is only as good as the data it’s trained on, and right now, our data is the biggest bottleneck to our AI ambitions.
If you’re a pharma exec looking to leverage AI for better decision-making and faster trials, then here are the resources you need to dig into to build a data strategy that actually supports your AI goals:
Weekly Resource List:
Nature Medicine: A personal health large language model for sleep and fitness coaching (30 min read)
Summary: This article introduces a Personal Health Large Language Model (PH-LLM) finetuned to interpret aggregated sensor data from wearables. The model exceeded human expert performance on multiple-choice examinations and showed significant improvement over a base model for personalized sleep insights, demonstrating the potential of LLMs to revolutionize personal health monitoring through tailored insights.
Key Takeaways: We should explore finetuning LLMs for every patient in our clinical trials along with diverse data sources for enhanced patient phenotyping and real-world evidence generation. It's important to invest in robust data quality control and contextualization frameworks to ensure the reliability of these insights.
npj Digital Medicine: Scoping review of remote cognitive assessments (30 min read)
Summary: This review evaluates remote and unsupervised digital cognitive assessment tools for preclinical Alzheimer’s disease. It highlights the significant advantages of these tools in scalability and reliability compared to traditional methods, enabling the capture of subtle cognitive changes. The review found high feasibility (86-87% consent) and acceptable reliability for these assessments.
Key Takeaways: Remote digital assessments can overcome traditional data collection challenges, offering a scalable and reliable way to monitor cognitive changes. These tools provide a consistent data stream that can be used to inform design of clinical trials and accelerate patient recruitment Alzheimer clinical trials. Start investing in building capabilities to manage deployment of such solutions.
BMC Medical Ethics: A national genomic data governance framework (25 min read)
Summary: This systematic review explores opportunities for a national genomic data governance framework in Australia, identifying a critical literature gap: an over-reliance on individual consent. It argues for a shift toward "trusted governance" models, like citizen-led Community Advisory Boards, to foster public trust and account for communal interests, particularly for Indigenous and minority ethnic communities, whose inherent communal data properties are often overlooked by individualized consent models.
Key Takeaways: We should advocate for standardized, interoperable genomic data governance frameworks and move beyond individual consent to build trust-based models that account for communal interests. This is crucial for ethically sourcing representative data and building defensible data moats that will enable equitable precision medicine.
Bessemer Venture Partners: The State of AI 2025 (15 min read)
Summary: Bessemer Venture Partners’ report asserts that AI is driving a major technological shift. It highlights the move from "systems of record" to "systems of action" and emphasizes the importance of robust AI evaluation frameworks and data lineage tracking. The report also predicts a surge in AI acquisitions and the need to address data fragmentation and privacy for a competitive advantage.
Key Takeaways: We need to prioritize AI solutions that actively process and leverage data to automate workflows, rather than just storing it. Build AI evaluation frameworks and track data lineage to validate performance, interpret outcomes, and gain regulatory confidence for the workflows you automate.
The New Yorker: What If A.I. Doesn’t Get Much Better Than This? (10 min read)
Summary: This article challenges the belief in the exponential scaling of AI capabilities, arguing that current advancements are focused on "post-training improvements" for narrow tasks rather than broad, transformative leaps. It advocates for a more realistic outlook on AI's near-future impact and emphasizes the critical need for effective AI regulations and digital ethics.
Key Takeaways: We should adopt a pragmatic, focused approach to AI integration, concentrating investments on well-defined problems where current models can deliver demonstrable, incremental value. We must also develop rigorous internal validation for our AI models, as public benchmarks may not reflect real-world utility.
3 Keys to Unlocking Your Data Silos and Fueling AI, Even with Privacy Concerns
In order to truly leverage AI's power, you can't just buy a model and hope for the best. You need to address the foundational issue of data silos and quality. Overcoming this challenge requires a strategic shift in how we collect, govern, and utilize our data.
Here are three critical areas to focus on right now to get your data in order.
1. Move From "Systems of Record" to "Systems of Action"
For years, we've focused on creating "systems of record"—databases that meticulously store and organize clinical trial data. But in the AI era, this isn't enough. Our data is just sitting there, waiting for a human to manually pull it, clean it, and make sense of it. This creates a massive, time-consuming bottleneck that prevents us from moving with the speed and efficiency AI promises.
Instead, we need to build "systems of action." This means prioritizing AI solutions that don't just store data but actively process and leverage it to automate complex workflows. Think about using AI to automatically analyze clinical operations workflows and flag a potential deviation, or to cross-reference a patient's genomic data with a new trial's eligibility criteria. By investing in tools that put data to work, you transform passive information into active intelligence, enabling better decision-making and dramatically accelerating your R&D processes.
2. Build Trust-Based Governance Frameworks, Not Just Consent Forms
Our current data governance model, which relies on individual consent, is broken. It's fragmented, siloed, and often fails to account for the communal nature of data, especially for diverse and Indigenous populations. This makes it incredibly difficult to access the representative, high-quality data that’s essential for training unbiased and effective AI models. The lack of public trust is a major barrier to collecting the data we need to innovate.
To overcome this, we must advocate for standardized, interoperable data frameworks and move toward what the research calls "trusted governance." This means going beyond a simple consent form and engaging with communities to create models that foster transparency and represent collective interests. By doing this, we can ethically source the diverse data needed to build robust AI models. This isn’t just about compliance; it’s about building a defensible competitive advantage by being able to innovate responsibly and equitably.
3. Prioritize Internal Validation over AI Hype
We're constantly bombarded with news of the next big AI breakthrough. But the reality is that many public benchmarks don't reflect real-world utility. Chasing the latest "supernova" AI model without a plan to validate its performance within your specific context is a risky and costly mistake.
Instead, we need to adopt a pragmatic, use-case-driven approach. Before you invest, ask a simple question: "What is a well-defined, high-friction problem for us? Is AI the right solution for it?" For example, are screen failure rates impacting your recruitment timelines? Can AI accurately validate your recruitment criteria by integrating and interpreting unstructured notes from EMRs with structured data from disconnected lab and genomic databases? If the answer is yes, then develop a rigorous internal validation framework where the AI runs in a "shadow mode," during trial design and compare its recommendations against your internal “gold standard”. By focusing on a solution that delivers tangible, incremental value—such as reducing screen failure rates for a single, high-priority trial—and proving its worth internally, you manage strategic investments more effectively and build crucial confidence in the technology with your clinical teams, sponsors, and regulators.
PS...If you're enjoying HealthTech for Lifescience Leaders, please consider referring this edition to a friend.
And whenever you are ready, schedule time to get a free advisory consultation.