AI For Pharma Growth

Many pharma and life science organisations have been investing in AI for years: pilots across commercial, medical, regulatory, and R&D, innovation labs, steering committees, vendor spend, and genuine effort from smart teams. And yet the same story keeps showing up in boardrooms: ROI is unclear, adoption is patchy, and leaders struggle to explain how all the AI activity connects to strategic goals.

In this solo episode, Dr Andree Bates steps into “The Diagnostic Room” to explain why this happens, and why it’s usually not a technology, talent, or speed issue. It’s a diagnosis issue: organisations often haven’t identified what is actually constraining value, so they end up executing hard on the wrong problem.

Dr Andree shares a real example from a mid-sized pharma company that believed its AI programme was failing due to lack of velocity. On the surface, it was a reasonable hypothesis. But a focused diagnostic revealed three hidden structural blockers: unclear decision rights for scaling pilots into production, fragmented data ownership preventing access to the best datasets, and incentive misalignment where the people expected to adopt AI tools were not rewarded for the behaviours those tools required.
She then clarifies what a diagnostic is and is not. A diagnostic is not a strategy, roadmap, vendor shortlist, financial model, or implementation plan. Instead, it provides evidence-based clarity: what’s broken, how you compare to peers, what’s at stake, and what questions have been opened that cannot responsibly be answered in ten days. That clarity creates a shared language for leadership, replacing vague frustration with a precise problem statement.
Finally, Dr Andree explains why the next step after diagnosis is not “faster action”, but smarter action: a full strategic AI blueprint with proper financial modelling, governance design, sequencing, and adoption architecture. The organisations pulling ahead are not simply those with the biggest budgets, but those willing to find what’s actually broken before trying to fix it.

Topics Covered
Why AI initiatives can grow without creating measurable ROI

The gap between pilots and a true AI strategy

Misdiagnosis: executing brilliantly on the wrong problem

What a diagnostic sprint is (and what it is not)

Three hidden blockers: decision rights, data ownership, incentive misalignment

Why working groups can’t fix structural AI constraints

What a full strategic AI blueprint includes

Why many AI business cases are untested projections

How to improve board confidence with evidence, governance, and measurement

Why diagnostics create speed by creating shared clarity

Eularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.
If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It’s a focused diagnostic that surfaces what’s actually broken and what’s blocking results, before you invest in a larger strategy effort.
The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.
Details at eularis.com.
About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.
This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

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AI ethics has moved from theory to urgent necessity, especially as AI systems become embedded in healthcare, business decisions, and society at large. In this episode, Dr Andree Bates is joined by Dr Nadia Morozova, founder of Enriched Insights, to unpack what ethical AI really means in practice, and how organisations can innovate quickly without creating risk, bias, or governance failures.

Nadia shares insights from the global conversation on AI ethics, including discussions at Davos, and explains why trust is becoming the true competitive advantage. She argues that organisations should use AI to build stronger, more open relationships with customers and stakeholders, where technology acts as an enabler rather than the centrepiece.

The conversation then gets practical. Nadia outlines a human-centric framework for high-quality AI outcomes, covering accurate sampling, futureproofing (because models are trained on the past), data connectivity across sources, and responsible blending of human and synthetic data. She warns that leadership teams often treat AI as “magic”, assuming tools will solve complex problems like data harmonisation without the hard work of ontology, governance, and expert oversight.

A real-world example brings this to life: the Zillow case, where initial success collapsed as market dynamics shifted and the model failed to adapt in time, leading to huge losses. For Nadia, the lesson is clear: ethical responsibility is not a checkbox at launch, it requires ongoing monitoring, review, and culture change.

Nadia closes with a strategic message for leaders: start with business goals and targeted use cases, involve data experts early, build governance upfront, and keep humans in the loop throughout the AI lifecycle. Done properly, ethical AI is not a constraint on innovation, it is how you protect long-term value and trust.

Topics Covered
Why AI ethics is now an urgent business and societal issue

Trust, transparency, and accountability in AI deployment

Human centricity as the foundation of high data quality

Accurate sampling and avoiding “biased reality” in models

Why futureproofing matters when algorithms learn from the past

Data connectivity, governance, and the ontology problem

Responsible blending of human and synthetic data

Dangerous leadership assumptions about AI “magic”

The Zillow case and what happens without ongoing oversight

Strategy first: KPIs, targeted use cases, and right-sized models

Skills gaps: technical roles, business acumen, and cross-functional teams

Culture change and post-deployment monitoring

About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

Digital biomarkers are turning everyday movement into clinically useful data, giving doctors a clearer picture of what’s happening between appointments, and giving pharma new ways to measure drug impact earlier and more precisely. In this episode, Dr Andree Bates interviews Dr Quique Llaudet, CEO and co-founder of Ephion Health, about precision monitoring and how AI-driven mobility analysis is changing both clinical care and drug development.

Quique shares his journey from academic research into entrepreneurship, driven by a desire to turn science into real products that help patients. Ephion Health grew out of early work with paediatric hospitals in Barcelona, where sensor technology used in rehabilitation and exoskeleton projects revealed a bigger opportunity: objective, high-sensitivity gait and movement analysis that can detect disease signatures and track progression over time.

The conversation breaks down what a digital biomarker actually is: a measurable signal of health captured via connected devices and analysed with digital methods. Ephion’s platform integrates multiple validated, off-the-shelf sensors to capture rich movement data in a short test, replacing blunt measures like the six-minute walk test with something both more sensitive and less stressful for patients. The system then combines key parameters into a single composite score to track progression and treatment response.

Quique also tackles the “black box” concern head on. He explains how their models are developed alongside clinicians, with clinical relevance checked throughout, and how doctors can inspect the underlying parameters behind the biomarker score in a dashboard. For rare diseases with limited data, he highlights deep collaboration with clinicians and patient associations, and the use of synthetic data to support modelling and testing.

Finally, Quique outlines the economics: reducing specialist assessment time, enabling more frequent remote monitoring, supporting earlier treatment adjustments, and helping pharma generate evidence in real-world settings. The long-term vision is continuous monitoring that helps clinicians act earlier, plus AI-assisted diagnosis and eventually prevention.

Topics Covered
What digital biomarkers are and how they differ from traditional biomarkers

Turning mobility data into clinically meaningful signals

Multi-sensor monitoring: IMUs, pressure insoles, and EMG

Why short tests can beat the six-minute walk test

Composite biomarker scoring and tracking treatment response

AI patterns clinicians may sense but cannot quantify

Explainability and building models “hand in hand” with doctors

Data challenges in rare disease and the role of patient associations

Synthetic data for modelling and validation

Economic impact: time savings, remote monitoring, and better treatment adjustment

Pharma use cases: real-world evidence and earlier efficacy signals in trials

About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

Digital twins have become one of the most promising tools in Alzheimer’s research, but the bigger story is what happens when they scale across disease areas. In this episode, Dr Andree Bates interviews Aaron Smith, Founder and Head of Machine Learning at Unlearn AI, about how “digital twin generators” can transform trial design by modelling realistic patient progression and improving statistical power without compromising the fundamentals of randomised controlled trials.
Aaron shares his journey from academic mathematics into computer vision and machine learning, then into biopharma, where Unlearn began by building generative models that learn the joint distribution of clinical variables. In practice, that means the model can take baseline patient measurements and generate likely future progressions that are as indistinguishable from real clinical records as possible.
The conversation dives into a key misconception: digital twins are not only about replacing control arms. Aaron explains a regulatory friendly approach where you keep standard trial structure, but add counterfactual information for every patient into the analysis. Unlearn’s best known method, ProCOVA (prognostic covariate adjustment), summarises a predicted control outcome per patient and uses it for covariate adjustment, creating more efficient treatment effect estimates. The headline result is simple: you can increase power, or reduce recruitment burden while maintaining power, potentially speeding time to results.
Finally, Aaron explains why scaling across diseases is genuinely hard. Data structures differ wildly by indication, missingness can block transfer learning, and areas like oncology require modelling complex treatment histories. He also highlights that combining sources is not just “more data”, it demands careful harmonisation and context modelling to avoid biased predictions, especially when bringing in real world evidence.

Topics Covered
What “digital twin generators” are in clinical trials

Generative modelling of clinical records and disease progression

Counterfactual prediction under standard of care

Why replacing control arms is not the only use case

ProCOVA and prognostic covariate adjustment

Getting more statistical power and reducing trial size

FDA openness to digital twins in trials and what it enables

Why scaling across disease areas is not just parameter tuning

Missing data, confounding context, and data harmonisation

CNS versus oncology modelling challenges

Real world evidence and how to validate digital twin models

About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.
This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Dr. Andree Bates LinkedIn | Facebook | X

AI in drug development is moving beyond “failure prevention” into something much bigger: redesigning how we discover, develop, and deliver medicines. In this episode, Dr Andree Bates speaks with Vitalay Fomin of Numenos about biomarker discovery, patient stratification, and why the next breakthroughs come from breaking down data silos across diseases, modalities, and even species.
Vitalay shares his background across biotech and pharma, including work in biomarker discovery, translational medicine, and data science, and how frustration with existing approaches led her to build a new architecture for clinical genomic insights. A core theme is that traditional methods often oversimplify biology by forcing outcomes into binary labels and treating each disease area as an isolated box, even when the available data is too limited to answer meaningful questions well.
The conversation explores how foundation model approaches can unify clinical, genomic, transcriptomic, proteomic and imaging signals to create a fuller “biological fingerprint” of each patient. Vitalay explains how this can enable earlier insight from single-arm trials by effectively benchmarking against standard-of-care cohorts, helping teams enrich later-stage trials with the right subpopulations sooner, and reducing time and cost.
They also discuss the real blockers to adoption: not only scientific conservatism, but commercial uncertainty around how Big Pharma structures deals with tech-bio companies that bring platforms rather than single assets. Vitalay argues that explainability is non-negotiable in this space, because clinicians, scientists, patients, and regulators will not trust black-box predictions.
Topics Covered
Why AI is shifting from failure prevention to pipeline redesign

Biomarker discovery beyond binary responder vs non-responder labels

Breaking disease silos to learn across indications

Multimodal integration: DNA, RNA, protein, imaging, and clinical data

Using foundation models to bridge trial data and real-world data

Patient stratification and trial enrichment from early studies

Reverse translation and identifying unmet need before target hunting

Explainability, trust, and regulatory readiness

Adoption barriers: culture, champions, and deal structures for tech-bio

Misconceptions about AI in drug development and why “press a button” is a myth

About the Podcast
AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.
This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.
Dr. Andree Bates LinkedIn | Facebook | X