Primetime Living 11.26.25 - Flipbook - Page 22
22 A Special Advertising Section of Baltimore Sun Media Group | Wednesday, November 26, 2025
AI Cancer Detection
Continued from page 8
methods in both sensitivity and consistency. It was applied to the blood of
1,000 individuals – 352 patients with
advanced cancers and 648 individuals
without cancer.
“MIGHT gives us a powerful way
to measure uncertainty and increase
reliability, especially in situations where
sample sizes are limited but data complexity is high,” says Vogelstein. “All
this bodes well for our ability to achieve
our goal of creating highly accurate
cancer tests with very low false positives.”
In an article titled “Current AI technologies in cancer diagnostics and
treatment,” from Molecular Cancer,
published in June 2025, it says,
“Cancer continues to be a significant international health issue, which
demands the invention of new methods for early detection, precise diagnoses, and personalized treatments.
Artificial intelligence (AI) has rapidly
become a groundbreaking component
in the modern era of oncology, offering
sophisticated tools across the range of
cancer care.”
A quick search will find numerous
articles detailing how much AI is helping researchers improve detection of
cancers in addition to breast cancer,
such as with melanoma, pancreatic
cancer and prostate cancer.
In an article from the National Cancer
Institute (NCI), part of the National
Institutes of Health, titled “Artificial
Intelligence and Cancer,” it says:
“NCI research is advancing the use
of AI across the spectrum of cancer
research and care, including mechanisms of cancer, cancer screening
and diagnosis, drug discovery, cancer
surveillance, and health care delivery.
“AI is helping to improve the speed,
accuracy, and reliability of some cancer
screening and detection methods. For
example: NCI scientists are using AI to
improve cervical and prostate cancer
screening. One group of NCI research-
ers and their collaborators developed
a deep learning approach for the automated detection of precancerous cervical lesions from digital images.”
AI was described and predicted in
the late 1930s and 1940s by Alan
Mathison Turing (1912-1954), the person who decoded the German cypher
machine, Enigma, that “saved a million lives” and helped the Allies win
WWII. He created what was dubbed
the Turing machine, a theoretical model
describing how computers could work,
up to and including programs that
resided in the machine.
Once we had the term “artificial intelligence,” we had to know that intelligence wasn’t out-thinking humans. In
1950, Turing proposed a test to determine whether a computer can “think,”
– what a lot of us imagine as being
similar to Skynet in the Terminator
movies – by introducing a practical test
for computer intelligence that is now
known simply as the Turing test. The
Turing test, in artificial intelligence, is
to determine whether a computer can
“think.” So far, depending on who you
ask, no computer has passed the test.
There’s always tomorrow.
“MIGHT could be applied to any
field where measuring uncertainty and
having confidence in the reliability and
reproducibility of findings is key. This
powerful new method significantly
improves upon the reliability and accuracy of artificial intelligence for many
applications. Research across all fields
of science requires confidence that
what the algorithm is spitting out is
real, reproducible, and accurate,” says
Vogelstein.
We are at the cusp of an explosion
of better diagnostics and treatments
as researchers like Vogelstein continue
their deep dives into extending their
grasp of how much they can accomplish by adding AI to their tool kits. For
them, it’s an exciting period to be alive.
