AI in Pharma: Hype vs. What Actually Works

Pharma leaders are investing a lot in large language models (LLMs) for issues that a simpler, cheaper machine learning model could handle more quickly and validate with less effort. This is the warning from Lara Masad, an AI and Data Innovation Independent Consultant, who spoke on the Pharmatica podcast hosted by Shubhangi Dua.

The main issue, according to Masad, is that the industry confuses two fundamentally different technologies. "Traditional machine learning models are trained to do one specific task very well," she explained, alluding to examples like deviation rates, batch records, and sensor readings. These models are deterministic, explainable, and highly auditable, which makes them well-suited to a GXP environment.

In the recent episode of the Digital Pulse podcast, host Shubhangi Dua, Podcast Producer and B2B Journalist, sat down with Masad to clarify the key distinctions in AI and machine learning models. Furthermore, Masad explains how pharmaceutical companies benefit from using AI models. 

LLMs function differently. They are trained on vast amounts of text and generate probabilistic outputs rather than fixed ones. They are designed to be generalists. "The same model that can draft a deviation investigation, for example, can also rewrite a poem," she said. This flexibility is also a challenge: "You can't create a fixed qualification protocol for a model that can respond in a thousand different ways."

Masad points out that this difference has real financial implications. "I have seen organisations default to large language models for everything because that’s what’s in the news, when in reality, a well-trained machine learning model would have been faster to validate, cheaper to maintain, and more defensible in front of an inspector."

Also Read: Where Healthcare AI Investment Is Going — And Where It Isn't

Where AI Is Actually Delivering Value Right Now

When asked where AI is truly making an impact in pharma, Masad provided a practical answer: "more areas than people think, but fewer than what vendors would like to suggest."

In quality assurance, she highlighted inspection readiness and deviation management as key applications. Identifying risk signals from CAPA records and trending data "before it becomes a 483 observation by the US FDA," she stated, is genuinely valuable.

Masad described document processing tasks like gap analysis against changing guidelines, regulatory intelligence monitoring, and drafting response narratives as the current focus. However, she is realistic about the limits: 

"Full-scale regulatory submission support is still a few years away from being valid in most markets and for most regulatory bodies."

The use case she finds most promising is inspection risk prediction. She believes it offers "a clear return on investment, a clear validation pathway, and a clear regulatory rationale" because it relies on machine learning instead of generative AI.

Also Read: Reimagine AI-driven Drug Discovery with Pharmaceutical Superintelligence

Where Leaders Should Focus for the Next Decade

Looking ahead, Masad identified three priorities for pharma organisations serious about long-term AI capability, ranked in order: people, regulatory understanding, and localisation.

Regarding people, she argued that the main barrier is not access to technology but having scientists, QA professionals, and leaders who can use it responsibly. On regulatory understanding, she predicted that future leaders "won't just be the ones with the best models" but those who can advance models through validation and approval the quickest. 

She also shared insights from her health-tech startup GeneAId Ltd, which applies machine learning to genetic variant classification for underrepresented Gulf and Arab populations. She emphasised that AI designed for US and/or European markets does not automatically apply elsewhere, making localisation a critical blind spot for global pharma companies.

Her final message tied the three priorities together: "We build a foundation, not a series of one-off projects. AI should compound over time, but only if you have created something worth compounding on."

Also Read: What Pharma R&D Tech Investment Committees Actually Fund

Key Takeaways:

1. ML is deterministic and auditable; LLMs are probabilistic and harder to validate.
2. The real AI value today is in deviation management and document processing, not full regulatory submissions.
3. Validation needs scoped use cases and human review gates instead of fixed test sets.
4. "Human in the loop" isn't enough without clear rubrics and continuous monitoring.
5. People, regulatory fluency, and localisation, not models, will determine who wins.

Chapters

• 00:00 Introduction to AI in Pharma
• 03:02 Understanding Machine Learning vs. Large Language Models
• 06:00 AI's Role in Pharma Business Processes
• 09:09 Challenges of AI in Regulated Environments
• 12:11 Practical Applications of AI in Pharma
• 15:12 Balancing Accuracy and Explainability
• 17:55 Responsible Adoption of AI in Pharma
• 20:57 Regulatory Oversight and Human Review
• 23:58 Continuous Monitoring and Validation
• 26:55 Building Long-term AI Capability in Pharma

For more industry-leading insights on AI in life sciences, visit pharmatica.io.

Reach out to Lara Masad here: https://www.linkedin.com/in/laramasad/

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