In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Nijat Ahmadov, CEO of Nucs AI, about molecular imaging as one of pharma’s most underused data assets.

Nijat explains why PET, CT and other molecular imaging data remain largely “untouched”: clinically valuable and created at scale, but still too often trapped in qualitative reads rather than structured, standardised data that can support decision making. As radioligand therapies expand in oncology, that gap becomes harder to ignore.

The conversation explores how AI can help turn molecular imaging into computable, decision-grade data for patient selection, response monitoring and companion diagnostic strategy. Nijat argues that AI is no longer a nice-to-have in this space. Without it, pharma risks losing confidence in the outcomes that affect adoption, reimbursement and commercial success.

They also discuss what it will take for AI-derived imaging biomarkers to become regulatory grade: analytical validation, reproducibility, diverse data sets, clinical validation and evidence that endpoints are meaningful, not just technically impressive.

The key message is that imaging is not only diagnostic. Once structured properly, it can reveal predictive signals about disease behaviour and treatment response, making it a powerful asset for pharma teams building the next generation of oncology trials.

Topics Covered

Why molecular imaging is still underused
Turning PET and CT scans into structured data
Radioligand therapy and patient selection
Moving beyond eligible vs not eligible
AI-derived imaging biomarkers
Clinical validation and regulatory trust
Imaging data as a competitive moat
Why prediction matters more than diagnosis

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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.

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