Emilia Javorsky
speaker
1,653 appearances
2 recordings
1 series
first heard Mar 2026
last heard 20 Mar
Emilia Javorsky’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 2 in all, peaking in Mar 2026 with 2.
Appearances
Finally, given the time for an idea in the lab to become an approved therapeutic, the science in the clinic today actually reflects discoveries from over a decade ago.
AI tools are already accelerating the era of precision oncology, a fact that gets overshadowed by big tech's promise of an absolute cure, as the work is often decentralized across academia, small startups, and internal development programs at pharmaceutical companies.
AI is helping to discover new drug targets, biomarkers, predict toxicity, and has helped expand the design of potential new biologics beyond their traditional set of druggable targets.
AI is identifying which treatments a patient is likely to respond to, predict resistance in advance, and minimize toxicity.
AI is even helping surgeons in the operating room better identify tumor margins to make sure they get all of the cancer and don't need an additional operation.
But all of this progress raises the question, why aren't cancer survival rates dramatically increasing?
One of the strongest prognostic factors is early detection.
And outside of mammograms and colonoscopies,
there has been little material progress that has gone mainstream.
This is partly because new diagnostics struggle to find viable business models, but also because early detection itself is medically complex.
Not all early-detected cancers are destined to become life-threatening, creating risks of overdiagnosis and overtreatment.
The South Korean screening program for thyroid cancer resulted in a 15-fold increase in thyroid cancer over two decades,
But mortality remained stable, as most of the cancers detected were ultimately small tumors unlikely to cause problems, putting patients through the risks of treatment without benefits.
Conversely, broad screening can save lives, with mammography being a prime example, and one recently improved by AI tools.
In fact, leading physician Eric Topol has argued that the largest clinical trial ever conducted on AI in medicine
now supports mandatory AI-assisted mammograms to improve early detection of breast cancer.
FDA requirements mandate cancer therapies are studied in populations, not individuals, imposing limits on leaning into the complexity of disease.
The narrower you define a patient population, the longer the study takes, burning precious patent time.
Worse are the economic incentives driving pharmaceutical firms towards late-stage trials.
Bluntly, advanced-stage cancer patients die faster, reaching survival endpoints sooner, meaning faster, cheaper clinical trials.
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