AI For Pharma Growth

Dr. Eugene Chan, a Toronto Metropolitan University marketing professor and consumer-psychology researcher, explores why pharma AI falters when human behavior gets in the way. They discuss trust, autonomy, professional identity, cognitive load, automation bias, and the lasting damage of one bad output. The conversation also examines explainability, accountability, and making AI adoption stick through workflow design, governance, and culture.

Health technology assessment is entering an AI accountability era. Explore what NICE, CDA-AMC and the EU Joint Clinical Assessment may expect when AI supports evidence generation, from named human oversight and automation-bias safeguards to full records of tools, versions, prompts and outputs. The discussion also covers vendor responsibility, privacy, copyright, security risks and building an audit trail assessors can reconstruct.

Mattia Marco Caruson, co-founder and CEO of mama health, brings pharma commercial expertise to the patient intelligence revolution. He explores why surveys and focus groups miss crucial lived experiences, how generative AI turns open-ended patient stories into structured insight, and what this means for launches, market access, medical affairs and support. They also tackle consent, privacy, hallucinations and trusted medical sources.

Dr. Kurt Rohloff, engineer, privacy researcher, and Duality Technologies CEO, explores how encrypted analytics and federated learning can transform pharma research. They discuss cross-border collaboration, rare diseases, underserved populations, data sovereignty, regulatory acceptance, secure AI and LLM workloads, and how reusing clinical data could enable virtual trials and faster development.

Clara Bernardes, co-founder and Chief Scientific Officer of Biorce, combines life sciences, law, and clinical operations expertise. She explores how AI can accelerate protocol design, reduce avoidable trial amendments, and learn from nearly one million annotated protocols. The conversation also examines explainability, ethics, regulatory compliance, human oversight, and what sponsors should ask AI vendors.

Polished AI can hide serious risks in pharma: fabricated citations, distorted claims, dropped qualifiers, stale guidance, and planted instructions. Explore how automation bias and anchoring weaken review, why hallucination is only part of the problem, and practical ways to trace claims, test stability, challenge assumptions, and strengthen human oversight.

Rebecca Maff, cancer biologist, health educator, and TEDx speaker known as the Healthy Biologist, explores how AI could move healthcare upstream. They discuss cancer risk prediction, early biomarkers, personalized screening, metabolic health, and behavior change. The conversation also examines pharma’s prevention opportunity, patient-data trust, responsible AI, and what GLP-1s reveal about a more proactive model of care.

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Raviv Pryluk, co-founder and CEO of PhaseV, about why so many clinical trials still fail and where AI can genuinely improve the odds.Raviv argues that failure is often not because the drug itself is wrong. Trials can fail because of the wrong patient population, indication, dose, design, sites, monitoring or interpretation of the data. PhaseV uses causal machine learning, adaptive trial design and large-scale simulation to help sponsors make better decisions across those areas.The conversation explores why explainability and validation matter in clinical development. Raviv explains that a 95% prediction is not enough on its own. Sponsors and regulators need to understand why a recommendation is being made, which evidence supports it, and whether the result is statistically and clinically defensible.They also discuss using existing trial data to identify responder subgroups, stress-testing designs before patients are enrolled, adapting trials mid-flight and connecting protocol design more closely with clinical operations.Topics CoveredWhy clinical trials still failCausal ML versus predictive modellingPatient selection and responder subgroupsAdaptive trial designSimulating trials before enrolmentSite selection and recruitmentValidation, explainability and statistical guaranteesGo/no-go portfolio decisionsAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates⁠⁠ LinkedIn⁠⁠ |⁠⁠ Facebook⁠⁠ |⁠⁠ X

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dan Pratl, founder and CEO of Quadron Inc, about a trust problem emerging as AI moves deeper into pharma: how do organisations preserve confidence when more work is generated, shaped and accelerated by machines?Dan argues that verified output alone is not enough. Pharma already has human review, MLR, regulatory sign-off and quality controls, but trust also depends on understanding where data came from, which models were used, where human judgement entered the process and whether that chain can be audited.The conversation explores why human expertise may become more valuable, not less, as AI spreads. Dan discusses shadow AI, the limits of forcing employees onto a single approved model, and why organisations need to reward people for curating, verifying and applying judgement across tools rather than treating AI usage itself as productivity.They also examine the risk of losing the junior work that traditionally builds expertise, the need for stronger audit trails, and why redesigning systems around AI may matter more than simply adding another governance dashboard.Topics CoveredAI and pharma's emerging trust problemWhy verified output is not the whole answerHuman judgement as a scarce resourceShadow AI and unsanctioned modelsAudit trails across humans, models and dataThe danger of equating token use with productivityBuilding future expertise in an AI-enabled workforceWhy trust requires incentives as well as governanceAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comAbout the PodcastAI For Pharma Growth is the podcast from Dr Andree Bates, helping pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands and improve company results. The show demystifies AI for biopharma leaders, from start-up biotech through to Big Pharma.Dr. Andree Bates LinkedIn | Facebook | X

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dr. Jacek Marczyk, co-founder and CEO of BioDynLab, about a contrarian view of computational drug discovery: that the next leap may come not from more data and bigger models, but from physics.Dr. Marczyk brings a background in aerospace engineering, automotive, Silicon Graphics and complexity science. His work led to quantitative complexity theory, which he now applies to molecules through BioDynLab’s deterministic, training-free approach.The conversation explores why high precision and high complexity cannot coexist, and why throwing more compute at biological problems does not automatically produce useful knowledge. Dr. Marczyk argues that machine learning can produce impressive outputs, but without explainability, teams may get a result without understanding the physics behind it.He explains how BioDynLab uses molecular dynamics and complexity theory to study how atoms and amino acids move, how information flows through molecules, and which residues act as key “hotspots” in that dynamic system. Instead of treating molecules as static structures, this approach looks at the motion and information patterns that help determine biological function.The key message is that AI and physics should not be seen as enemies. In data-sparse areas such as rare diseases, novel targets and first-in-class chemistry, physics-led methods may offer a complementary route to insight, especially where machine learning has little or no training data to rely on.Topics CoveredWhy pharma’s AI gold rush may miss key biologyThe principle of incompatibilityPhysics-first drug discoveryQuantitative complexity theoryWhy explainability mattersMolecular dynamics and information flowAtomic and amino acid participation factorsComplexity hotspots in moleculesStatic structures versus molecular motionRare disease and data-sparse discoveryAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comDr. Andree Bates⁠ LinkedIn⁠ |⁠ Facebook⁠ |⁠ X