
Sanjay Juneja: Is the Future of Oncology AI Limited to Smarter Prompts, and thus Faster Execution and Delivery?
Sanjay Juneja, Vice President of Tempus AI Clinical Operations, shared a post on LinkedIn:
“All this stuff about AI models against doctors/MDAPPs miss an important concept we don’t talk about enough: Context engineering.
We all know by now that ‘prompt engineering’–which if in an academic system, would be that oncology fellow with perfect scores that can recall every study by name. But what makes your attending still necessary is their knowing of what? Your institution’s treatment pathways, your tumor board’s decision-making nuances, and the approach to all those EDGE cases.
That’s context engineering.
In oncology, where 10 oncologists could make 7 different—but still evidence-based—choices for the same patient, the magic isn’t in asking the right question; it’s in preserving those subtle but validated differences.
Why does this matter? Because oncology isn’t monolithic:
- Academic centers lean on early-phase trial data.
- Community clinics may prioritize patient logistics and toxicity management.
- Individual oncologists bring experience and institutional philosophy that guidelines can’t fully capture.
If AI simply spits out NCCN summaries, it’s not replacing friction—it’s replacing personalization with standardization. And if that were the world, then how could doctors not be replaced?
Context engineering lets us embed the “why” behind a center’s or a physician’s approach, so decision support becomes adaptive, not prescriptive. Imagine LLMs that reflect your clinic’s actual practice patterns—without losing fidelity to national guidelines. That’s how you move from “copilot” to “true partner.”
Is the future of oncology AI limited to smarter prompts, and thus faster execution and delivery? Or does it go as far as being, too, smarter in context? Or is that the line…?! Interested in thoughts…”
More posts featuring Sanjay Juneja.
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OncoThon 2024, Online
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Global Summit on War & Cancer 2023, Online
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