AI For Pharma Growth

In this solo episode of AI For Pharma Growth, Dr Andree Bates explores one of the biggest blind spots in pharma AI: the failures that can survive a normal review process even when the output sounds polished, plausible and well grounded.
Dr Andree explains why better models and better retrieval do not remove the need for human judgement. In fact, as AI systems improve, people often scrutinise them less. Fluent prose can create a false sense of reliability, while automation bias and anchoring make reviewers less likely to challenge the underlying frame.
The episode introduces three broad families of failure: invented, distorted and misdirected. These include fabricated or misattributed citations, silent interpolation, dropped qualifiers, hedge-to-claim escalation, manufactured consensus, stale guidance and planted instructions. Many of these are harder to catch because the output reads better than the truth.
Dr Andree also shares practical checks teams can use, including tracing claims back to source, checking what is unsupported, looking for missing qualifiers, regenerating outputs for stability and deliberately testing the opposite case.
The key message is that prompting is only half the skill. In a regulated industry, teams also need to be trained to judge what comes back.
Topics Covered
Why better AI can lead to less scrutiny

Automation bias and anchoring

Invented, distorted and misdirected failures

Fabricated and misattributed citations

Dropped qualifiers and strengthened claims

Manufactured consensus

Prompt injection and planted instructions

Claim-by-claim verification

Why review methods must change for AI

The growing importance of the judgement layer

About Eularis
Eularis 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.com
About the Podcast
AI 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 cancer biologist and health educator Rebecca Maff about how AI could help shift healthcare from treating disease to preventing it.
Rebecca’s interest in prevention began after seeing women in her own family diagnosed with late-stage cancers and serious autoimmune conditions. Her subsequent work has included machine learning models designed to identify patients at higher risk of cancers and encourage overdue screening before disease progresses.
The conversation explores what prevention really means in practice, from early biomarkers and personalised risk modelling to screening, lifestyle, metabolic health and behavioural change. Rebecca explains how AI can help analyse electronic health records and large patient populations to identify signals that would be difficult for humans to find manually.
They also discuss the commercial opportunity for pharma. If late-stage disease becomes more preventable, the industry may need to think beyond treating established illness and consider new models built around earlier detection, intervention and maintaining health.
Trust remains a major challenge. Rebecca argues that patients and clinicians need to see credible, successful AI implementations before confidence grows, particularly when systems are using highly personal health, genomic and behavioural data.
Topics Covered
Moving healthcare upstream towards prevention

AI-powered cancer risk stratification

Early screening and disease detection

Biomarkers and personalised risk

Using electronic health records to identify signals

Prevention as a future pharma opportunity

Lifestyle, metabolic health and chronic disease

Patient data, privacy and trust in AI

Why proof of concept matters before scaling

About Eularis
Eularis 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.com
About the Podcast
AI 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 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 Covered
Why clinical trials still fail

Causal ML versus predictive modelling

Patient selection and responder subgroups

Adaptive trial design

Simulating trials before enrolment

Site selection and recruitment

Validation, explainability and statistical guarantees

Go/no-go portfolio decisions

About Eularis
Eularis 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.com
About the Podcast
AI 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 Covered
AI and pharma's emerging trust problem

Why verified output is not the whole answer

Human judgement as a scarce resource

Shadow AI and unsanctioned models

Audit trails across humans, models and data

The danger of equating token use with productivity

Building future expertise in an AI-enabled workforce

Why trust requires incentives as well as governance

About Eularis
Eularis 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.com
About the Podcast
AI 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 Covered
Why pharma’s AI gold rush may miss key biology

The principle of incompatibility

Physics-first drug discovery

Quantitative complexity theory

Why explainability matters

Molecular dynamics and information flow

Atomic and amino acid participation factors

Complexity hotspots in molecules

Static structures versus molecular motion

Rare disease and data-sparse discovery

About Eularis
Eularis 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.com

Dr. Andree Bates⁠ LinkedIn⁠ |⁠ Facebook⁠ |⁠ X