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Medical Science OPEN ACCESS

Generative AI In Life Sciences: Unlocking Operational Value Across the Product Lifecycle

Efim Iuresku
Engagement Manager, Strategic Consulting Firm New York City, USA
DOI Crossref DOI 10.37547/tajmspr/Volume07Issue07-03
tajmspr 2025
VOL. 7 / NO. 07 JULY
FULL TEXT PP. 15-21
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VOLUME 7
ISSUE 07
YEAR 2025
PAGES 15-21
RESEARCH OVERVIEW

Abstract

The development and commercialization of pharmaceutical and MedTech products represent one of the most complex and resource-intensive endeavors in the modern economy. The process involves long timelines, high attrition rates, and the integration of vast volumes of structured and unstructured data. Generative artificial intelligence (GenAI) has emerged as a transformative tool capable of enhancing efficiency, accelerating scientific discovery, and streamlining operations across the life sciences value chain - from early research to clinical development, manufacturing, medical affairs, and commercialization.

A synthesis of current literature, real-world implementations, and industry benchmarks was conducted to evaluate high-impact application areas of GenAI in life sciences. Documented use cases from biopharmaceutical and MedTech organizations illustrate the deployment of GenAI in target identification, de novo molecule generation, trial protocol design, medical writing automation, manufacturing deviation analysis, medical engagement support, and omnichannel content generation.

Estimates indicate that GenAI could unlock between $60 billion and $110 billion in annual value across the pharmaceutical industry. The greatest economic potential lies in commercial functions, followed by research and clinical development. Early adopters - including Pfizer, Novartis, AstraZeneca, and Novo Nordisk - have reported productivity improvements ranging from 20% to 60% in pilot programs focused on regulatory documentation, manufacturing quality, and HCP engagement.

Despite these benefits, large-scale adoption remains constrained by several challenges. Key barriers include hallucination risks in language models, regulatory ambiguity, limitations in technical infrastructure and data quality, and resistance to organizational change. Addressing these constraints will be critical to ensuring the safe, compliant, and impactful integration of GenAI technologies into life sciences workflows.

RESEARCH TOPICS

Keywords

Generative AI Life sciences Pharmaceutical operations Pharma digital transformation

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References

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