>> ALL RIGHT.
GOOD AFTERNOON AND WELCOME TO
TODAY'S CLINICAL CENTER GREAT
TEACHERS GRAND ROUNDS.
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CODE FOR TODAY'S GRAND ROUNDS IS
57870.
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YOU TO SUBMIT SUGGESTIONS FOR
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THE CONTEMPORARY CLINICAL
MEDICINE GREAT TEACHERS GRAND
ROUNDS IS A SPECIAL SERIES
WITHIN THE CLINICAL CENTER GRAND
ROUNDS PROGRAM.
DR. PAUL DISTINGUISHED SCIENTIST
EMERITUS OF NIAMS WHO RECENTLY
PASS ASH WAY, THE PURPOSE WAS TO
INVITE THOUGHT LEADERS IN
MEDICINE AND SCIENCE TO PRESENT
ON TOPICS AND IDEAS ABOUT WHICH
THEY ARE PASSIONATE TO SHARE THE
REFLECTIONS WITH US AND TO
INSPIRE YOUNG INVESTIGATORS IN
THE PURSUIT OF CAREERS IN
BIOMEDICAL RESEARCH.
SO TODAY WE'RE HONORED TO HOST
OUR GREAT TEACHER DR. EERIC
TOPOL, EXECUTIVE VICE PRESIDENT
OF SCRIPTS RESEARCH, WHEN HE WAS
THE DIRECTOR AND FOUNDER AND
DIRECTS THE SCRIPTS
TRANSLATIONAL INSTITUTE AND
PROFESSOR OF MOLECULAR MEDICINE.
DR. TOPOL IS A RENOWNED
SCIENTIST AND THOUGHT LEADER
THIS GENOMIC DIGITAL AND
ARTIFICIAL INTELLIGENCE TOOLS TO
PROMOTE HUMAN HEALTH.
A PIONEERING CARDIOLOGIST
DR. ERIC TOPOL IS 1 OF THE MOST
CITED RESEARCHERS IN MEDICINE
TODAY.
DR. TOTOL EARNED HIS DEGREE WITH
HIGHEST DISTINCTION IN MEDICINE
FROM UNIVERSITY OF VIRGINIA AND
HIS MEDICAL DOCTORATE WITH
HONORS FROM THE ROUGH ATOM
CHESTER SCHOOL OF MEDICINE IN
DENTISTRY.
HE COMPLETED HADIS INTERNSHIP
AND RESIDENCY AT THE UNIVERSITY
OF CALIFORNIA SAN FRANCISCO, AND
HIS FELLOWSHIP IN CARDIOVASCULAR
MEDICINE AT THE YAWNS HOPKINS
HOSPITAL AND THEN A SENIOR
FOLLOW OSHIP IN CORONARY ANGIO
PLAOF THEY AT THE SAN FRANCISCO
HEART INSTITUTE.
IN THE YEARS THAT FOLLOWED,
DR. TOPOL CONTINUED AN
EXTRAORDINARY CAREER AS A
PRACTICING CARDIOLOGIST,
EDUCATOR, MENTOR, RESEARCHER AND
A PROFESSOR IN MEDICINE AT THE
UNIVERSITY OF MICHIGAN, CASE
WESTERN RESERVE AND SCRIPTS
RESEARCH INSTITUTE.
MANY OF HIS OVER 200 FORMER
TRAINEES SERVE IN ACADEMIC
POSITIONS AS LEADERS IN
CARDIOVASCULAR MEDICINE AND
GENOMIC RESEARCH WORLD WIDE.
HE HAS LED CLINICAL TRIALS IN
OVER 40 COUNTRIES AND PIONEERED
THE DEVELOPMENT OF MANY
MEDICATIONS USE INDEED MEDICINE
AND CARDIOLOGY TODAY SUCH AS
TPA, PLAVIX, AND
[INDISCERNIBLE].
HIS RESEARCH NOW FOCUSES ON THE
BROADER QUESTIONS OF PRECISION
MEDICINE AND ARTIFICIAL
INTELLIGENCE.
DR. TOPOL HAS A LONG ARK
FILIATION WITH THE NIH AND HE
CURRENTLY SERVES AS PRINCIPAL
INVESTIGATOR TO 2 LARGE NIH
GRANT.
ALSO THE ALL OF US RESEARCH
PROGRAM THAT SUPPORTS PRECISION
MEDICINE AND A CLINICAL AND
TRANSLATIONAL SCIENCE AWARD THAT
PROMOTES INNOVATION IN MEDICINE
AND FUTURE MEDICAL RESEARCHERS
EDUCATION AND CAREER TRAINING.
DR. TOPOL IS ALSO A MEMBER OF
THE NATIONAL ACADEMY OF MEDICINE
AND RECIPIENT OF NUMEROUS AWARDS
INCLUDING THE ASSOCIATION FOR
BLACK CARDIOLOGIST AWARD FOR
PROMOTING DIVERSITY IN
CARDIOVASCULAR MEDICINE, ARN
OLDER PEOPLE P. GOLD'S
FOUNDATION IN HUMANISM MEDAL AND
THE NATIONAL LIBRARY OF
MEDICINE'S DONALD A. B.
LINDBERGH DISTINGUISHED HEALTH
COMMUNICATIONS AWARD.
DR. TOPOL IS ALSO THE AUTHOR OF
3 BEST SELLERS ON THURE OF
MEDICINE, TO INCLUDE DEEP
MEDICINE HOWARD FICIAL
INTELIENCE CAN MAKE HUMAN
HEALTHCARE AGAIN.
TODAY'S TITLE IS OPPORTUNITIES
AND PERILS OF ARTIFICIAL
INTELLIGENCE IN HEALTH AND
MEDICINE.
PLEASE JOIN ME IN WELCOMING
TODAY'S GRAND ROUND SPEAKER
DR. ERIC TOPOL.
[ APPLAUSE ]
>> THANKS.
>> THANKS, SO MUCH CHRIS FOR
YOUR KIND INTRODUCTION AND IT'S
GOOD TO BE WITH YOU HERE AT THE
CLINICAL CENTER HAVING NOT BEEN
HERE BEFORE OVER ALL THESE YEARS
WITH MANY TRIPS TO NIH CAMPUS
AND I'M GOING TO GET INTO AI AS
IT STANDS TODAY AND AS YOU KNOW
IT'S A VERY DYNAMIC HOT FIELD
AND IN SOME WAYS TROUBLING IN
OUR DAILY LIFE FIELD.
AND I'LL BE TALKING ABOUT
VARIOUS ASPECTS OF HOW WE EXPECT
IT TO TRANSFORM MEDICINE AND
HUMAN HEALTH.
I WOULD JUST MENTION I HAVE NO
CONFLICTS OF INTEREST TO
DISCLOSE AND I WILL NOT BE
DISCUSSING ANY DRUGS.
SO JEFFREY HINTON REGARDED AS
THE FATHER OF DEEP LEARNING AI,
HE RECENTLY LEFT GOOGLE BECAUSE
HE FELT CONFLICTED ABOUT THE
WORRIES OF AI.
BUT WHEN I SPOKE TO HIM WHAT
HEED WAS THIS, AND THIS
IS--ACTUALLY JUST POSTED THIS
MORNING, A PODCAST WITH FRANCIS
COLLINS ON HIS NEW BOOK, THE
ROAD TO KNOWLEDGE, BUT AT THE
TIME WHEN I SWOAK TO
GEOFFREY HINTON, HE SAID I
ALWAYS PIVOT TO MEDICINE AS AN
EXAMPLE OF AI, GOOD IT CAN DO
BECAUSE ALMOST EVERYTHING IT'S
GOING DO THERE IS GOING TO BE
GOOD.
SO THIS IS COMING FROM A PERSON
WHO HAS THE HIGHEST REGARD OF
THE AI COMMUNITY AND SPEAKING
ABOUT THE EXPECTATIONS AND
MEDICINE AND HEALTHCARE.
SO NOW LET ME GET INTO THOSE
BRIGHT PROSPECTS AND FIRSTLY THE
ISSUE OF MEDICAL ERRORS, AND
THAT IN ITSELF IS NOT A BRIGHT
PROSPECT BUT THE FACT THAT WE
CAN TURN THESE AROUND TO SOME
DEGREE.
SO THIS IS A BOOK BY DANIEL
OFFREY, AND MEDICAL ERRORS ARE
NOT DISCUSSED MUCH, THEY'RE KIND
OF KEPT QUIET WHICH HE KIND OF
SOUNDED THE ALARM WITH THIS BOOK
BECAUSE THEY ARE VERY HARMFUL
AND MORE OVER JOHNS HOPKINS
STUDY THAT WAS PUBLISHED LAST
YEAR, POINTED ON UTR THAT THERE
ARE 800,000 AMERICANS
CONSERVATIVE ESTIMATE, FROM
SERIOUS DIAGNOSTIC ERRORS WHO
ARE SIGNIFICANTLY DISABLED OR
DIE.
SO THIS IS A BIG PROBLEM THAT
ISN'T ADEQUATELY ACKNOWLEDGED BY
THE MEDICAL COMMUNITY.
THEN WE HAVE THIS TERM PRECISION
MEDICINE WHICH HAS BEEN OF
COURSE USED QUITE A BIT OVER THE
YEARS.
WE HAD A LITTLE PROBLEM WITH
THIS TERM BECAUSE IF YOU KEEP
MAKING THE SAME MISTAKES OVER
AND OVER AGAIN, IT'S VERY
PREICIZE.
BUT WHAT WE REALLY NEED IS
ACCURACY IN MEDICINE AND THAT'S
HOW WE GET THESE DIAGNOSTIC
ERRORS WHICH ARE VERY TROUBLING
DOWN.
SO I WROTE A PIECE IN SCIENCE
EARLIER THIS YEAR TOWARDS THE
ERADICATION OF MEDICAL ERRORS
BRINGING IN ERA OF LARGE MODEL
AI AND LET ME TAKE YOU THROUGH
WHY THERE'S SO MUCH OPTIMISM
THAT WE CAN BRING DOWN THE ERROR
RATE IN DIAGNOSIS.
SO FIRSTLY BEFORE WE GOT TO THE
CURRENT CHAT GPT ERA IF YOU WILL
FROM LATE 2022, THERE WASUNE
MODAL SUPERVISED AI THAT
REQUIRES GROUND TRUTHS AND
REQUIRES THE EXPERTS TO REVIEW
IMAGES OR WHATEVER THE INPUTS
WERE, TYPICALLY THEY WERE
IMAGES.
AND WE LEARN FROM THAT TIME THAT
EVERY TYPE OF MEDICAL SCAN COULD
GET AN AI INTERPRETATION THAT
WOULD BE AS GOOD AS CLINICIANS,
FOR EXAMPLE, RADIOLOGIST OR
PATHOLOGIST AND THIS IS JUST 1
EXAMPLE FROM NYU AND MILLIONS OF
CHEST X-RAYS WHEREBY THE
RADIOLOGIST MISSED A NODULE AND
IT WAS PICKED UP BY THE AI AND
IT TURNED OUT TO BE A CANCEROUS
NODULE.
SO THIS IS PRETTY PROTOTYPIC AND
THE LARGEST STUDY THAT'S BEEN
DONE TO DATE WAS IN SWEDEN WHERE
THEY RANDOMLY ASSIGN 80,000
WOMEN UNDER GOING MAMMOGRAPHY TO
EITHER HAVE TRADITIONAL
INTERPRETATION BY A TRAINED
RADIOLOGIST OR AI ASSISTED AND
THIS PROVED WITHOUT QUESTION
THAT THE PICK UP RATE FOR CANCER
WAS SUPERIOR WHEN THE 2 WERE
COMBINED RATHER THAN THE
RADIOLOGIST ALONE.
AND ALSO IN DOING THAT THE
STREAMLINING OF THE PROCESS WAS
IMPROVED.
NOW THIS IS THE AREA THAT'S HAD
THE MOST STUDY, SO FAR,
INTERESTINGLY AND THAT'S IN
COLONOSCOPY.
AND THERE'S NOW BEEN 40
RANDOMIZED TRIALS OF AI PLUS THE
MACHINE VISION WITH REALTIME,
WITH THE GASTRONEUROLOGYIST OR
JUST A
GASTROENTHUSIASMERROLOGYIST AND
WHAT TURNS OUT TO BE THE CASE IS
THAT THE PICK UP RATE FOR POLYPS
IS SUBSTANTIALLY HIGHER WHEN THE
AI IS USED.
AND SO THIS OF COURSE IS JUST 1
EXAMPLE, AND NOT ONLY IS IT PICK
UP THE POLYPS BUT ALSO IN
REALTIME GIVES A GRADATION OF
HOW LIKELY IT IS TO BE CANCEROUS
AND REQUIRING A BIOPSY.
SO THIS IS JUST A SUMMARY, WHEN
THERE WERE 33 RANDOMIZED TRIALS,
ALMOST EVERY WEEK OR 2 THERE'S
ANOTHER 1 ADDED AND IT SHOWS HOW
THERE'S MARKED IMPROVEMENT OF
PICKING UP POLYPS AND ADENOMAS,
AND INTERESTINGLY STUDY VS SHOWN
THAT IT ESPECIALLY WORKS WELL
TOWARDS THE END OF THE DAY, WHEN
THE DOCTOR OR GASTRONEUROLOGYIST
HAS TIRED EYES BUT THE AI NEVER
GETS TIRED.
NOW WE RECENTLY REVIEWED ALL THE
RANDOMIZED TRIALS, NOW THERE'S
JUST OVER A HUNDRED, BUT AT THE
TIME THERE WERE ABOUT 90 AND
THEIR LARGEST PROPORTION OR
INTERESTINGLY IN GI, LIMITED
NUMBER IN OTHER AREAS SUCH AS
CARDIOLOGY, RADIOLOGY.
SO THIS IS AN EXAMPLE OF AI NOW
THAT HAS BEEN IMPLEMENTED AND
THIS IS A BIG DEAL BECAUSE NOT
MUCH OF IT HAS BEEN IMPLEMENTED.
LOTS OF PUBLICATIONS, THOUSANDS
OF PUBLICATIONS BUT WHEN YOU
LOOK AT WHAT IS DONE TO CHANGE
IMMEDIATE, THERE'S NOT THAT
MUCH.
THERE'S ALMOST A THOUSAND AI
ALGORITHMS THAT ARE AUTHORIZED
BY THE FOOTWORK DECKERA BUT MOST
OF THEM ARE NOT IN COMMON USE
AND MOST OF THEM DON'T HAVE
GREAT DATA.
SO THIS IS THE MAYO CLINIC, THEY
DID A RANDOMIZED TRIAL WITH AI
ASSISTED ECG READING AND SHOWED
IT WAS SUPERIOR FOR THE ACCURATE
DIAGNOSIS AS COMPARED TO JUST
THE PHYSICIAN AND NOW THEY'VE
IMPLEMENTED THIS AND THEY ARE
PICKING UP PEOPLE WHO HAVE POOR
EJECTION FRACTION AND VARIOUS
OTHER DIAGNOSIS THAT WOULD
OTHERWISE NOT GET PICKED UP AS
FREQUENTLY BY THE PHYSICIAN.
SO THAT GETS US TO THIS TOPIC OF
MEDICINE EYES OR DIGITAL EYES,
AND WHAT I MEAN BY THAT IS, THE
MACHINE WILL SEE THINGS THAT
HUMANS WILL NEVER SEE.
IT'S ACTUALLY QUITE
EXTRAORDINARY AND THIS IS WHY
THE HOPE FOR IMPROVING ACCURACY
IS SO RICH.
SO THE RETINAL LOCATIONINNA IS A
PROTOTYPIC EXAMPLE.
WHO WOULD HAVE EVER THOUGHT THAT
THROUGH THE RETINA GATEWAY TO
THE HUMAN BODY WE WOULD BE ABLE
TO DETECT ALL THESE THINGS SUCH
AS THE BLOOD PRESSURE AND
DIABETES CONTROL OR AS YOU SEE
HERE, THE KIDNEY DISEASE BEFORE
THERE'S EVER BEEN ANY KIDNEY
DISEASE, THE LIKELIHOOD, LIVER
AND GALLBLADDER DISEASE FROM THE
RETINA, THE HEART CALCIUM SCORE
EQUIVALENT TO A CT OF THE HEART,
OF THE CHEST, BY THE VESSELS IN
THE RETINA, PREDICTING
ALZHEIMER'S DISEASE, 7 YEARS
BEFORE THERE ARE ANY SYMPTOMS,
THE LIKELIHOOD AND THE SAME FOR
PREDICTING STROKE AND HEART
ATTACK RISK, LIPIDEMIA, HYPER
LIPIDEMIA AND PARKINSON'S
DISEASE MORE THAN 5 YEARS BEFORE
ANY SYMPTOMS HAVE BEEN MANIFEST,
THIS IS QUITE EXTRAORDINARY AND
IT'S NOT THE ONLY EXAMPLE.
THE ELECTROCARDIOGRAM WITH
MACHINE EYES PROVIDES
EXTRAORDINARY DATA AS SHEEN HERE
SUMMARIZED, YOU WILL SEE THAT IT
CAN NOT JUST GET THE AGE AND SEX
OF THE PATIENT HAVING READ
CARDIA GRAMS I WOULD NEVER GUESS
WHAT THAT IS AND HERE IT IS
QUITE ACCURATE BUT YAWBD THAT
THE EJECTION FRACTION OF THE
PERSON'S HEART, MANY DIFFICULT
DIAGNOSIS THAT MIGHT BE MISSED,
THE HEMODPLOABIN TO THE DECIMAL
POINT FROM THE CARDIO GRAM.
AND THEN ADD TO THAT IN SOMEONE
WITH NORMAL 69US RHYTHM
PREDICTING WHETHER THEY WILL
EVER HAVE ATRI OLDER PEOPLE
FIBRILLATION OR STROKE FROM A 12
WEEK CARDIOGRAM.
DIABETES NEVER WOULD HAVE
GUESSED THAT 1 OR PREDIABETES
DIAGNOSED FROM THE
ELECTROCARDIOGRAM, THE FILLS
PRESSURE OF THE HEART,
HYPOTHYROIDISM, KIDNEY DISEASE
LIKE THE RETINA, AND
PERIPARADIGM UMKC CARDIOMYOPATHY
LIKELIHOOD EMPLOY SO IT'S JUST
EXTRAORDINARY WHAT CAN BE
GLEANED THROUGH DIGITAL EYES
THAT WE WOULD NOT HAVE
ANTICIPATED AND FOR
PATHOLOGISTS, THIS IS REALLY
ALSO QUITE NOTE WORTHY BECAUSE
IT CAN PICK UP THE MUTATIONS
THAT ARE DRIVING THE PATIENT'S
CANCER FROM THE PATTERN
RECOGNITION HAVING THEM TRAIN
WIDE HUNDREDS OF THOUSANDS OF
PATH SLIDES, WHOLE SLIDE IMAGES,
MALIGNANT VERSUS BENIGN, THE
ORIGIN OF THE TUMOR, THE
STRUCTURAL CNV GENOMIC
UNDERPINNINGS, AND RESPONSE TO
THERAPY ALONG WITH PROGNOSIS,
ALL FROM THE SLIDE, WHO WOULD
HAVE GUESSED.
OKAY, SO THIS IS A RECENT STUDY
THAT LOOKED AT THE CHEST X-RAY,
AND THE CHEST X-RAY ALSO IS
QUITE EXTRAORDINARY BECAUSE WHAT
YOU CAN SEE IS IT PROVIED BETTER
RISK SCORE FOR A BURN DEVELOPING
CARDIOVASCULAR DISEASE THAN RISK
SCORES THAT WE USE TODAY FROM
THE CHEST X-RAY.
SO THIS IS REALLY QUITE
EXTRAORDINARY AND I THINK IT
REALLY CHANGES THE WHOLE WAY WE
MIGHT BE ADDING WAYS TO ASSESSA
PERSON'S CARDIOVASCULAR RISK
EMPLOY THIS FIELD IS CALLED
OPPORTUNISTIC AI AND BASICALLY
WHAT WE'RE TALKING ABOUT HERE IS
THAT YOU GET A SCAN FOR SOME
REASON AND YOU GET ALL THESE
OTHER EXTRA ADDED OUTPUTS THAT
MAYBE USEFUL WHICH IS WHAT WE'RE
LEARNING FROM DIGITAL EYES.
IT'S PRETTY EXCITING.
OKAY, SO NOW LET ME MOVE TO THE
CURRENT ERA, BECAUSE THAT'S AN
OUTGROWTH OF DEEP LEARNING, THAT
THE MULTIMODAL AI IS WHERE WE
ARE NOW WITH SELF-SUPER VISED
THE DATA IS BASICALLY LEARNING
FROM ITSELF RATHER THAN
REQUIRING ANNOTATIONS BY EXPERTS
WHICH IS WELL ALINED IN MEDICINE
SINCE WE DON'T HAVE EXPERTS WITH
TIME TO ANNOTATE HUNDREDS OF
THOUSANDS OF IMAGES.
SO YOU MAY KNOW THAT THIS WHOLE
ERA OF DEEP LEARNING AND NOW,
TRANSFORMER MODELS ONLY BECAME
POSSIBLE BECAUSE OF VIDEO GAMES.
I MEAN IS THAT AMAZING.
IT WAS THE NVIDIA CHIPS THAT
BACK AT THE TURN OF THE CENTURY
THAT MADE THE COMPUTING POSSIBLE
TO CHANGE IMMEDIATE AND
EVERYTHING IN OUR DAILY LIVES
AND NOW WE'RE AT A POINT WITH
THE LATEST NVIDIA CHIP THAT IT
HAS OVER 2 BILLION TRANSISTORS
IN IT, AND THAT'S PRETTY
STRIKING, SO WHAT WE'RE TALKING
ABOUT HERE IS GOOD OLD DEEP
NEURAL NETWORKS THAT WERE NOW,
7, 8, 9, YEARS REALLY CHANGING
THE LOOKS OF A LOT OF STUDIES I
WAS MENTIONING TO NOW, SWITCHING
TO TRANSFORMER MODELS THAT ARE
BASICALLY MULTIATTENTION
MODELINGS THAT CAN BRING IN THE
CONTEXT IN THE DATA IN THE FORM
OF TOKENS AND THIS IS REALLY
CHANGING, THAT'S WHAT REALLY SET
THE WHOLE WORLD INTO MOTION WITH
EXCITEMENT OF CHAT BOTS AND LIKE
GPT AND SUBSEQUENT 1S.
DPT 4 UP UNTIL SEVERAL MONTHS
AGO WAS REGARDED AS THE MOST
ADVANCED LARGE LANGUAGE MODEL.
AND WHAT'S INTERESTING ABOUT
THAT IS THAT IT HAS OVER
1 TRILLION PARAMETERS OR
CONNECTIONS AND WHAT'S ALSO
INTERESTING THAT IT REQUIRED
24,000 GRAPHIC PROCESSING UNITS.
NOW DO HAVE YOU ANY IDEA HOW
MANY GRAPHIC PROCESSING UNITS
ARE ON THE NIH CAMPUS?
ANYBODY?
MIGHT BE A COUPLE HUNDRED,
MAYBE.
THERE'S NOT THAT MANY AT
STANFORD OR M. I.T. AND THIS IS
WHAT IS NOW THE ARMS RACE OF
GPUs TO DO THESE BASE LARGE
LANGUAGE MODELS.
AND THE AMOUNT OF TRAINING COSTS
INVOLVED IN SETTING UP THESE
MODELS BECAUSE OF THE MASSIVE
DATA THAT NEEDS TO BE PROCESSED
IS REALLY AMAZING, IT'S WORTH
TALKING ABOUT MILLIONS TO A
COUPLE HUNDRED MILLION FOR SOME
OF THE RECENT TRANSFORMER
MODELS.
IN ANY CASE, DESPITE THESE
LIMITATIONS, THIS IS SETTING UP
A LOT OF NEW APPLICATIONS IN
HEALTHCARE, IN MEDICINE, AND
THAT WAS A SECOND PIECE I WROTE
FOR SCIENCE LAST YEAR.
SO ALL THESE LAYERS OF THE HUMAN
THAT MAKE US UNIQUE, EVERYTHING
FROM THE ANATOMY, THE
PHYSIOLOGY, ALL THE DIFFERENT
BIOLOGY OMICs, DNA, RNA,
EPIGENOMICS, MICROBIOME, METRICS
TAB O LOAM, AND THE EXPOETICSOME
ARE EXPOSURES, COLLECTIVELY AND
THE IMMUNE OME MAKE US UNIQUE
AND INTERESTINGLY IT ISN'T JUST
A 1-OFF, MAYBE A DNA SEQUENCE IS
A 1-OFF BUT MANY OTHERS ARE
DYNAMIC AND THIS IS OF COURSE
THE IDEA THAT WE HAVE TO
SERIALLY ASSESS THESE DATA OVER
A PERSON'S LIFE.
SO RECENTLY AS OF LAST YEAR
PIERCE KEEN AT MOOREFIELD'S--THE
MOST PREOF THE IEJOUS EYE
INSTITUTE IN THE WORLD--WE HAD A
CHANCE TO COLLABORATE WITH HIS
GROUP BECAUSE THEY HAD
1.6 MILLION RETINAL IMAGES AND
THEY DID A SO CALLED FOUNDATION
MODEL.
SO REMEMBER THAT RETINA THAT I
SHOWED 8 DIFFERENT THINGS IT
COULD DO, THEY DID IT ALL IN 1
MODEL WITH THE MASSIVE RETINAL
IMAGES WITH THE CLINICAL
FEATURES PREDICTING PARKINSON'S
DISEASE, HEART DISEASE, KIDNEY
DISEASE AND EVERYTHING ELSE IT
WAS PREVIOUSLY MENTIONED.
SO THAT'S WHAT'S REALLY SEEN AS
THE FOUNDATION MODEL, THIS
MULTIMODAL, MULTITASK CAPABILITY
WHICH HAS NOW BEEN BROUGHT IN BY
THESE NEW MODELS, THESE LARGE
LANGUAGE MODELS.
SO, THIS WEEK, THERE WAS A PAPER
IN NATURE.
THIS IS ON CANCER PATHOLOGY AND
THIS IS THE MOST ADVANCED CANCER
PATHOLOGY FOUNDATION MODEL YET
AND IT BASICALLY IS PICKING UP
NOT JUST A DIAGNOSIS, BUT AS I
SHOWED YOU THE DIFFERENT DRIVING
MUTATIONS, THE PROGNOSIS OF THE
PATIENT AND THESE CIRCLES HERE,
ARE BASICALLY SHOWING ITS
PERFORMANCE AGAINST PRIOR
PATHOLOGY AI MODELS, IT JUST
KEEPING GET BEING BETTER AND
BETTER FOR DOING ALL THESE
THINGS.
NOW THE NEXT THING IT'S CAPABLE
OF DOING WHICH IS EXCITING IS A
DIFFICULT DIAGNOSIS.
WRASSE PATIENTS COME TO THE NIH
CLINICAL CENTER WITH IDIO PATHIC
CONDITIONS NOW WE HAVE THE
ADVANTAGE OF SAYING TO GPT4 OR
GEMINA ULTRA OR WHATEVER, WHAT
IS THE DIAGNOSIS OF THE PATIENT?
HERE'S AN EXAMPLE, INTERESTINGLY
THIS BOY'S CASE WAS FIRST
PRESENTED ON THE TODAY SHOW OF
ALL PLACES, BUT IT'S A TAC
FASCINATING CASE BECAUSE HE HAD
3 YEARS OF RELENTLESS WORSENING
PAIN, DIFFICULTY WALKING,
HEADACHES, AND FAILURE TO GROW.
AND HIS MOTHER AND FATHER IN
MICHIGAN TOOK HIM TO 17 DOCTORS,
NOT EVEN JUST IN MICHIGAN TO GET
HIM EVALUATED AND THEY COULDN'T
COME UP WITH A DIAGNOSIS, SO
ULTIMATELY OUT OF FRUSTRATION
SHE ENTERED ALL HIS DATA INTO
CHAT GPT AND WITHIN SECONDS IT
CAME OUT WITH THE DIAGNOSIS OF A
SPINA BIFIDA AND SHE WENT TO A
NEUROSURGEON AND INDEED THAT'S
WHAT HE HAD AND HE HAD SURGERY
TO CORRECT THIS AND HE'S DONE
EXTREMELY WELL.
THIS IS 1 OF MANY ANECDOTES OF
DIFFICULT CASES THAT WERE
STUMPING VARIOUS PHYSICIANS, 1 I
WAS INVOLVED IN WAS THIS PATIENT
WHOSE SISTER WAS TOLD SHE HAD
LONG COVID WHICH HAS YOU KNOW
HAS NO VALIDATED TREATMENT.
SO SHE PUT IN ALL OR DATA TO
CHAT GPT AND IT SAID SHE DOESN'T
HAVE LONG COVID, SHE HAS EMILY
CIBOL ENCEPHALITIS WHICH IS
IMMINENTLY TREATABLE, SHE WAS
TREATED AND SHE FULLY RECOVERED.
SO THERE'S THE MASS GENERAL
HOSPITAL CASE CPC'S CLINICAL
PATHOLOGIC CONFERENCE OF THE
STUMP THE EXPERT, JUST A MASTER
CLINICIAN OF MAN'S GREATEST
HOSPITAL AND THIS HAS BEEN GOING
ON FOR A HUNDRED YEARS, RIGHT?
WELL, TURNS OUT GUESS WHAT?
WHEN YOU PUT A LARGE LANGUAGE
MODEL UP AGAINST THE MASTER
CLINICIANS, IT DOES EXTREMELY
WELL, IT DOES AS WELL AS THE
MASTER CLINICIANS, THESE ARE
CASES, ACTUAL CASES.
300 NEW ENGLAND JOURNAL CPCs,
THEY DID A RANDOMIZED TRIAL AND
YOU SEE THE LARGE LANGUAGE MODEL
DID BETTER THAN THE MASTER
CLINICIANS AND HERE IS ANOTHER
1.
THIS IS A GOOGLE DEEP MIND LARGE
LANGUAGE MODEL, IT LOOKED AT 26
DIFFERENT OUTCOMES, NOT JUST
DIAGNOSTIC ACCURACY BUT WE'RE
TALKING ABOUT EXAM SKILLS,
PATIENT-CENTERED COMMUNICATION,
EMPATHY, I WILL COME BACK TO
THAT, WOULD HAVE THOUGHT?
DIAGNOSIS AND MANAGE AM.
OUT OF 26 DIFFERENT TASKS THE
LARGE LANGUAGE MODEL DID BETTER
THAN THE PHYSICIAN.
SO, THIS WAS THE NATURE COVERAGE
OF THAT PAPER, GOOGLE AI HAS
BETTER BEDSIDE MANNER THAT IS
HUMAN DOCTORS, THAT DOESN'T
SOUND VERY UNCOURAGING DOES IT?
OKAY, THIS WAS ANOTHER STUDY
PUBLISHED MORE RECENTLY IN
NATURE MEDICINE SHOWING HOW
COMPARING THE GPT 4 TO PHYSICIAN
FOR THE NOTES, COMPLETENESS,
CORRECTNESS AND CONCISENESS AND
YOU CAN SEE GPT4 WAS SUPERIOR
AND THEY LOOKED AT VARIOUS OTHER
LARGE LANGUAGE MODELS AS WELL
AND THIS IS JUST AN EXAMPLE OF
HOW THIS WAS PROCESSED BY THE
LARGE LANGUAGE MODEL, ALL THIS
DIFFERENT DATA PROCESSED
CORRECTLY BY THE LARGE LANGUAGE
MODEL AND INCORRECTLY, THE
EXPERT, THE MEDICAL EXPERT
MISSED THE THINGS IN GREEN, SO
TOGETHER, THE ACCURACY IS
GREATLY FOSTERED AND THE
CONCLUSION OF THAT STUDY WAS
INCORPORATING LARGE LANGUAGE
MODELS WILL MAKE THESE SUMMARIES
OF DOCUMENTATION SYNTHESIS OF A
PATIENT'S RECORD MUCH BETTER,
LESS STRAIN, TIME FOR THE
CLINICIAN AND IMPROVING PATIENT
CARE.
SO NOW WE HAVE THESE MULTIMODAL
MODELS THAT ARE FLOURISHING AND
BEING PUBLISHED ON A FREQUENT
BASIS.
THIS WAS FROM LAST MONTH, BIOMED
GPT TAKING IN ALL THESE
DIFFERENT MODES OF DAILY BASIS
THEA, LAYERS OF DATA FROM THE
MEDICAL LITERATURE, FROM THE
ELECTRONIC HELT RECORD, EVERY
TYPE OF SCAN, REALLY QUITE
IMPRESSIVE.
THIS IS ANOTHER NOTE WORTHY 1
BECAUSE AS YOU KNOW DIABETIC
RETINAL LOCATION NUMBER OF
PATIENTSATHY IS FREQUENTLY
MISSED BECAUSE THERE'S NO SCAN
OF THE RETINA IN HALF OF
DIABETICS AND IT'S AN IMMINENTLY
PREVENTIBLE FORM OF PLIENDNESS
AMONG DIABETICS.
WELL, THIS WAS A DEEP DR,
DIABETIC RETINAL LOCATION NUMBER
OF PATIENTSATHY NLM, AND IT
SHOWED 800S PATIENTS WERE
ASSESSED AND WHEN THE PRIMARY
CARE PHYSICIAN WAS GIVEN THIS
TOOL THE ACCURACY WAS IMPROVED
SUBSTANTIALLY AND THEN IT WAS
RAIDED BY ENDOCRINOLOGIST AS
WELL AS BY PATIENTS AND YOU CAN
SEE BY PATIENTS THEY WERE RATING
THE AI TO MORE EMPATHETIC THAN
THE PHYSICIAN.
AND THEN THE ENDOCRINOLOGIST,
THAT'S AGAIN RATING THIS, THEY
ALSO RATED THE COMBINATION OF
THE PC P, PRIMARY CARE PHYSICIAN
AND THE AI TO BE SUPERIOR TO
PHYSICIANS.
THIS WASN'T ANTICIPATED BUT IT'S
CONSISTENTLY SHOWN NOW IN 10
DIFFERENT STUDIES THAT YOU CAN
BOLSTER EMPATHY BY HAVING A
LARGE LANGUAGE MODEL ASSIST A
PHYSICIAN, PASQUANTINOINATEING.
SO WE WROTE A REVIEW NOW A
COUPLE YEARS AGO, IT'S ALREADY
WELL SURPASSED BUT MANY
DIFFERENT WAYS WE CAN USE
MULTIMODAL AI IN THE FUTURE.
ONE OF THOSE IS HOSPITAL AT
HOME, REVIEW ARTICLE WE
PUBLISHED BECAUSE WHEN HAVE YOU
A PERSON WHO CAN HAVE SENSORS
ON, CAN HAVE EXQUISITE
MONITORING, WHY WOULD THEY BE
ADMITTED TO THE HOSPITAL WHEN
THEY CAN BE IN THE CONVENIENCE
OF THEIR OWN HOME WHICH IS A LOW
EXPENSE AND HIGH COMFORT AND BE
WITH THEIR FAMILIES AND THIS IS
THE FUTURE, NOT FOR PEOPLE IN AN
INTENSIVE CARE UNIT BUT PER
THOSE WHO WOULD OTHERWISE BE IN
A REGULAR HOSPITAL ROOM.
NOW TO GET TO THIS NEXT SECTIO,
ESPECIALLY TAKING A STEP FORWARD
BUT 1 I THINK WILL HAVE QUITE A
LARGE IMPACT IN THE TIMES AHEAD
AND THAT'S FORECASTING.
SO YOU PROBABLY KNOW ALTHOUGH IT
HASPT BEEN UNLEASHED ON THE
WORLD, BUT WEATHER FORECASTING
WITH LARGE LANGUAGE MODELS THE 1
PROTOTYPE IS CALLED GRAPH CAST,
IT HAS WEATHER FORECASTING
ACCURATE TO 99.7% WHICH IS A BIG
JUMP FROM WHAT WE HAVE TODAY
MUCH WE OFTEN MAKE JOKES ABOUT
THE WEATHER PREDICTIONS, THAT'S
PROBABLY GOING TO BECOME A THING
OF THE PAST.
WHAT IF WE HAVE MEDICAL
FORECASTING THAT'S AS GOOD AS
THE WEATHER FORECASTING WE'RE
SEEING WITH LARGE LANGUAGE
MODELS AND HERE IS THE
OPPORTUNITY OUTLINED BECAUSE
AGAIN, ANOTHER SCIENCE INVITED
PIECE ABOUT THIS POTENTIAL.
WE HAVE THESE 5 DOMAINS THAT ARE
COME TOGETHER NOW THAT ARE
GIVING US ABILITY TO PREDICT AND
FORECAST THINGS IN MEDICINE AT
THE INDIVIDUAL LEVEL THAT WE
NEVER HAD BEFORE.
WHETHER IT'S THE VARIOUS
OMICs, THE CELLS THAT WE HAVE
SO MUCH THE NEWER DRUGS AND
VACCINES THAT ARE AVAILABLE AND
THE LIFESTYLE + AS A CALL IT
BECAUSE WE'RE TALKING ABOUT
EXPOSURES, WE'RE TALKING ABOUT A
LOT MORE THAN JUST SLEEP,
EXERCISE AND DIET.
SO WITH THAT, THE ABILITY TO
PREDICT A PERSON'S POLYGENIC
RISK SCORE THAT,'S ONLY 1 LAYER
OF DATA AND IT'S STILL BEING
DEBATED HOW MUCH EVALUATED AS
WELL AS 1 LAYER WITH ORTHOGONAL
LAYERS TO COMPLEMENT IT, IT'S
EXTREMELY VALUABLE.
WE SHOULD BE USING POLYGENIC
RISK SCORES IN ALL OUR PATIENT
BUT WE'RE NOT AND IT'S
UNFORTUNATE BECAUSE IT'S AN
INEXPENSIVE WAY TO GET AN
ASSESSMENT OF COMMON DISEASE
ANDS TO COMPLEMENT OTHER TOOLS
WE HAVE.
THIS IS A REVIEW WE RECENTLY
WROTE ON CARDIO METABOLIC RISK
THAT YOU COULD PREDICT AT THE
INDIVIDUAL LEVEL USING MULTIPLE
LAYERS AND REBOOTING SCREENING
FOR CANCER WHICH IS OF
CONSIDERABLE WASTE, USES THE
DUMB-DOWN CRITERIA OF AGE, SOLE
CRITERIA FOR CANCER SCREENING
FOR MOST OF THE COMMON CANCERS,
WE MUST DO BETTER THAN THAT
BECAUSE MOST OF THE PEOPLE FOR
EXAMPLE, WOMEN WITH BREAST
CANCER, 88% OF THE WOMEN WILL
NEVER DEVELOP BREAST CANCER IN
THEIR LIFE, BUT ALL ARE SUPPOSED
TO BE HAVING FREQUENT
MAMMOGRAMSA THE A CERTAIN AGE
AND EVEN THAT'S DEBATED.
SO WE CAN DO BETTER THAN THE WAY
WE CANCER SCREEN AND PREDICT THE
RISK OF A PERSON SPECIFIC CANCER
IN THE YEARS AHEAD.
SO LET ME ALSO MENTION PAN
KRISTATTIC CANCER, 1 OF THE MOST
DIFFICULT TO DIAGNOSE, ALMOST
ALSO STAGE 3 OR 4 WHEN IT IS
FINALLY DIAGNOSED.
WE'VE SEEN HOW AI COULD PICK UP
THINGS IN THE ELECTRONIC HEALTH
RECORD THAT WOULD OTHERWISE BE
MISS INDEED A LARGE NATIONWIDE
DENMARK STUDY AS WELL AS THE VA
SYSTEM TO PICK UP PANCREATIC
CANCER YEARS BEFORE IT WAS
MANIFEST.
SO HOPEFULLY WE WILL USE THESE
EARLY DETECTIONS OF CANCER,
PETITIONING OF RISK IN THE TIMES
AHEAD EMPLOY NOW THIS WILL
BECOMING OUT LATER THIS MONTH,
ALSO IN SCIENCE, IT'S ABOUT THIS
REVOLUTION GOING ON IN
PROTEOMICS, HIGH THROUGH PUT
PROTEOMICS AND AI AND I JUST
SUMMARIZED 10 RECENT STUDIES,
MOST OF THEM ARE ERROR RECENT,
AND WE'RE SEEING ORGAN CLOCKS,
BODY WIDE PROTEOMIC CLOCKS THAT
GIVE US HIGH ACCURACY AND
SPECIFICITY ABOUT A PERSON'S
RISK AND DOWN TO THE ORGAN LEVEL
FOR BRAIN, FOR HEART, FOR
KIDNEY, WHATEVER THE ORGAN,
INCLUDING THE IMMUNE SYSTEM, SO
I WON'T GO THROUGH THAT BUT I
JUST WANT TO TAKE YOU THROUGH
THE 3 MOST IMPORTANT AGE RELATED
DISEASES AND HOW WE CAN REVAMP
OUR APPROACH WITH AI, TAKING ALL
THESE DIFFERENT LAYERS OF DATA,
SOME OF THEM I'VE BEEN
MENTIONING PREDICTING HEART
DISEASE, PUTTING PEOPLE UNDER
ACTIVE SURVEILLANCE NOT JUST
THAT THEY WOULD DEVELOP HEART
DISEASE BUT IF THEY'RE
DEVELOPING HEART DISEASE AT AGE
100 NOT NEARLY AS MUCH OF A
CONCERN AS DEVELOPING IT AT AGE
60 EMPLOY AND WE CAN USE THIS TO
MANIFEST THIS AND ALL OF THESE
OTHERS TAKE 2 DECADES TO
DEVELOP, AND THEN GO INTO HIGH
GEAR PREVENTION SO THAL RICK OF
A PERSON'S DEC NEVER IMETS
MANIFEST.
THE SAME IS TRUE FOR CANCER,
SOMEWHAT DIFFERENT LAYERINGS OF
DATA AND SOMEWHAT DIFFERENT
SURVEILLANCE BUT THE SAME THEME
OF PREVENTINGA THAT CANCER NOT
JUST BY EARLY DIAGNOSIS BUT BY
KNOWING THE PERSON'S RISK IN THE
FIRST PLACE AND THEN USING THE
THINGS LIKE MULTICANCKER EARLY
DETECTION TEST WHICH IS NOT THE
WAY THEY'RE BEING USED TODAY.
THEY'RE BEING USED AGE 50 OR
OLDER, THE SAME VERY DUMB-DOWN
APPROACH AND ESPECIALLY NOW WE
KNOW THAT MANY CANCERS ARE
DEVELOPING IN YOUNG PEOPLE'S AGE
AND IN FACT IN 20 ANDS 30INGS
FOR COLON CANCER AND BREAST
CANCER.
AND THEN FINALLY FOR ALSZ
JEIMERS FOR EXAMPLE OF THE 3
ANAL RELATED DECS, THE SAME
THEME HERE USING DIFFERENT
LAYERS OF DATA, DIFFERENT TOOLS,
AI CAN TELL US WHEN THIS IS
LIKELY TO SHOW UP, AND ALSO SET
UP THE INTERVENTION TO PREVENT
THIS FROM EVER HAPPENING.
REALLY EXCITING TIME IN
MEDICINE, WE'VE NEVER HAD THIS
OPPORTUNITY BEFORE.
NOW THE LAST THING I WANT TO
TOUCH ON BEFORE OPENING UP TO
QUESTION SYSTEM ABOUT THE
PATIENT DOCTOR RELATIONSHIP.
YOU KNOW CHRIS MENTIONED THAT I
HAD WRITTEN A BOOK 5 YEARS AGO
ABOUT DEEP MEDICINE AND HOW THIS
IS THE OVERARCHING GOAL.
IF WE CAN IMPROVE THAT
RELATIONSHIP, WHICH IS ERODED
TERRIBLY OVER THE DECADES SINCE
THE 70S ESPECIALLY, THAT WOULD
BE A BIG CRITICAL OBJECTIVE.
SO TODAY, THIS IS ALL TYPES OF
CLINICIANS SEEING A DOCTOR, THEY
DON'T REALLY SEE THE DOCTOR
BECAUSE THEY'RE KEYBOARD, DATA
CLERK KEYBOARD SLAVES AND SO
THIS IS NOT THE RIGHT TYPE OF
MEDICINE THAT WE WANT TO
PRACTICE.
WE WANT TO HAVE PRESENCE, AND
TRUST, AND A BOND THAT'S
AMELIORATED AND BUILT UPON
DURING A VOICE NOLESS BEYOND THE
VISIT AND DANNY [INDISCERNIBLE]
WHO RECENTLY PASSED HE WROTE A
PHENOMENAL BOOK THAT ACTUALLY
ALSO APPLIES TO MEDICINE.
BECAUSE THE WAY MEDICINE IS
TODAY IS FAST AND SHALLOW.
THE AFRAMMING APPOINTMENT IS AT
7 MINUTES.
HOW DO YOU HAVE A RELATIONSHIP
WITH THE PATIENT WHEN THE
PATIENT IS INTERRUPTED AFTER THE
FIRST 82ndS OF TALKING, NEVER
ABLE TO TELL THEIR STORY, NOLESS
NOT HAVING AN ADEQUATE PHYSICAL
EXAM.
SO, WE NEED TIME AND WHAT'S
HAPPENING IS REALLY EXCITING
RIGHT NOW AND THAT'S WITH THIS
ERA OF AI AUTOMATED NOTES FROM
THAT CONVERSATION, A SYNTHETIC
NOTE IS GENERATED WHICH IS
BETTER THAN THE NOTES THAT EXIST
TODAY IN EPIC CENTER AND ALL OF
OUR DIFFERENT HEALTH IT SYSTEMS.
THAT NOTE IS NOT JUST ABOUT THE
NOTE THAT'S GIVEN TO THE PATIENT
WITH AUDIO LINKS AND CAN
TRANSLATED INTO ANY LANGUAGE OR
ANY LEVEL OF EDUCATION BUT ALSO
IS USED TO GET PREAUTHORIZATION,
TO SET UP PRESCRIPTIONS, RETURN
VISITS, FOLLOW-UP LABS,
PROCEDURES, BILLING CODES
EVERYTHING, INCLUDING NUDGING
THE PATIENT ABOUT WHAT WAS
DISCUSSED AT THE VISIT, WHAT
LIKE WHAT WERE THE BLOOD
PRESSURES WE ASKED TO CHECK, ALL
THROUGH AN AUTOMATED NOTE AND
THIS IS NOW IN MANY HEALTH
SYSTEMS AROUND THE COUNTRY, WE
ADOPTED VERY QUICKLY AND THE
AVERAGE PHYSICIAN REPORTS 2-3
HOURS LESS ELECTRONIC HEALTH
RECORD DATA CLERK FUNCTION AND
IT JUST KEEPS GROWING IN TERMS
OF THE BENEFIT AND TIME, THE
GIFT OF TIME FROM AI AND THIS IS
ALSO THE PREAUTHORIZATION WHICH
IS 1 OF THE WORST THINGS TO GO
THROUGH THAT IT'S HELPING IN A
VERY BIG WAY.
SO THIS GIFT OF TIME IS WHAT I
THOUGHT WE WOULD BE ABLE TO TURN
AROUND THIS PATIENT-DOCTOR
RELATIONSHIP BUT I NEVER
ENVISIONED GIVING PATIENTS MORE
CHARGE WITH THEIR DR. TOPOLA
WITH AI ALLEGOR RYTH EPIGENETICS
TO SUPPORT THAT IT WOULD ALSO
LEAD TO THIS FACILITATION OF
EMPATHY.
MACHINES HAVE NO IDEA WHAT
EMPATHY IS, BUT INTERESTINGLY
BECAUSE THEY ARE TRAINED FROM
OUR CULTURE, FROM OUR LANGUAGE,
THEY ARE ABLE TO PROMOTE EMPATHY
AS YOU'VE SEEN IN MANY STUDIES.
THIS IS 1 OF THE FIRST STUDIES
FROM UC SAN DIEGO THAT'S
CONSISTENT WITH EVERY STUDY
SINCE AND I'VE WRITTEN ABOUT
THIS, IT'S REALLY EXCITING,
WOULD NOT HAVE BEEN PREDICTED, I
NEVER THOUGHT IT WOULD BE
POSSIBLE BUT IT JUST SHOWS YOU
HO AI CAN DO THINGS THAT WERE
NOT NECESSARILY ANTICIPATED.
SO, HUMAN TOUCH, THAT IS
BRINGING BACK A MUCH MORE OF A
PHYSICAL EXAM, THAT'S WHAT
PATIENTS WANT WHEN THEY GO TO
SEE A LOSS, AND THEY RECOGNIZE
IT'S ALL IMPORTANT AND NOW I
WANT TO TOUCH ON THE DOWN SIDES
OF AI BECAUSE MOST EVERYTHING I
TALKED ABOUT GIVING YOU THE
IMPRESSION AI IS GLOWING AND
DOING ALL THESE WONDERFUL
THINGS, WELL, NOT NECESSARILY,
RIGHT?
YOU ALL KNOW IT HAS ANOTHER DARK
SIDE, IT COULD MAKE FOR A LOT
MORE BLURRING OF WHAT IS TRUTH
AND FACT.
IT ALSO HAS THE ABILITY TO
PROMOTE [INDISCERNIBLE]
HALLUCINATION AND ERRORS ITSELF,
IT HAS THE PROBLEM AS THESE 2
BOOKS THE MIRROR OF OUR CULTURE
AND SO ALL THE DIFFERENT BIASES
THAT ARE EMBEDDED IN THE DATA
INPUTS NOT NECESSARILY THE
ALGORITHM, AND THIS RECENT BOOK
WHICH WILL BE INTERVIEWED 1 OF
ITS AUTHORS AND WILL BE A
SUBSEQUENT GROUND TRUTHS IN THE
DAYS AHEAD, AI NCI--AND OIL, A
VERY TOUGH TITLE OF A BOOK AND I
GAVE THE AUTHOR SOME TOUGH TIMES
BECAUSE DURING THE INTERVIEW, HE
SAID OH AI IS GREAT, WE JUST
DIDN'T WRITE ABOUT IT IN THE
BOOK.
I SAID OH, THAT'S REALLY NICE.
[LAUGHTER]
,
SO THE BIASES AND POTENTIAL OF
WORSENING INEBS QUITYS, WE DON'T
WANT THAT.
WE'VE ALREADY SEEN EXAMPLES OF
HOW IT CAN IMPROVE INEQUITIES
AND OF COURSE THE FEAR OF AI
THAT IS PARTICULARLY CLINICIANS
THINK IT WILL REPLACE THEM
SOMEHOW WHICH IT'S NOT GOING TO
REPLACE THEM BUT THESE ARE
EXAMPLES OF IMPROVING EQUITY
WHETHER IT'S RHEUMATIC HEART
DISEASE IN AFRICA OR GETTING
PEOPLE WITH MENTAL HEALTH IN THE
UK TOA ACCESSIBILITY OF CHAT
BOTS WHERE THEY HAD THEM BEFORE,
THIS IS WHERE AI IS HELPING TO
BRIDGE AND REDUCE THE PROBLEMS
OF HEALTH INIQUITIES.
HOPEFUL THERE THERE WILL BE MANY
MORE EXAMPLES OF THAT IN THE
FUTURE.
JUST TO SUMMARIZE, WE HAVE LOTS
OF UPHILL BALGTS WITH,A I.
I MENTIONED IT'S NOT BEEN
IMPLEMENTED VERY MUCH, I
MENTIONED THERE'S A PROBLEM WITH
TRUST BECAUSE THE DATA FROM
THESE FDA AUTHORIZED ALGORITHMS
AND FREQUENTLY, MOST FREQUENTLY
NOT PUBLISHED OR IT IS PUBLISHED
IT'S RETROSPECTIVE MODELS AND A
LOT OF IT IS LEFT IN PROPRIETARY
SPHERE, SO LACK OF TRANSPARENCY,
LACK OF COMPELLING TRIALS,
REGULATORY LOW THRESHOLD FOR
GETTING THESE AUTHORIZED, LACK
OF REIMBURSEMENT AND ALSO THE
GENERAL RESISTANCE OF CHANGE IN
THE MEDICAL COMMUNITY.
SO I JUST WANT TO LEAVE YOU WITH
MY FAVORITE QUOTE IN MEDICINE
AND THIS IS FROM FRANCIS
PEABODY, ALMOST A HUNDRED YEARS
AGO, A SINGLE AUTHOR, PAPER,
1927 JAMA, IF YOU HAVEN'T READ
IT, YOU WILL GET INSPIRED ABOUT
MEDICINE AND WHAT HE SAYS AT THE
END IS 1 OF THE ESSENTIAL
QUALITIES OF THE CLINICIAN IS
INTEREST IN HUMANITY FOR THE
SECRET OF THE CARE OF THE
PATIENT IS IN CARING FOR THE
PATIENT AND HOPEFULLY AI WILL
HELP US GET THERE.
SO THANK YOU VERY MUCH.
THIS IS AN ACKNOWLEDGMENT OF THE
PEOPLE I GET TO WORK WITH AT
SCRIPTS RESEARCH AND OUR FUNDING
SOURCES AND I LOOK FORWARD TO
YOUR QUESTIONS AND CRITIQUE,
THANK YOU.
[ APPLAUSE ]
>> THANK YOU DR. TOPOL FOR SUCH
A WONDERFUL TALK.
SO THE PODIUMS ARE OPEN IF YOU
HAVE A QUESTION PLEASE STEP UP
TO THE MICROPHONE SO THAT WE CAN
IDENTIFY YOU AND YOU CAN ASK
YOUR QUESTION, AND SO DR. TOPOL,
JUST TO LET YOU KNOW WE HAD
ABOUT 300 PEOPLE ONLINE WATCHING
SO CLEARLY AN EXCELLENT TOPIC.
>> HI, I HAVE A QUESTION ABOUT
THE AMBIENT LISTENING NOTES, I
DON'T KNOW A SINGLE DOCTOR THAT
DOESN'T WANT THAT.
OKAY?
AND YOU GAVE A LOT OF GREAT
EXAMPLES OF WHAT IT CAN DO, WHAT
ARE THE PRIVACY CONCERNS,
REGULATORY PIECES, GOVERNMENT
AGENCY, CAN YOU SPEAK A LITTLE
BIT TO WHY THAT MIGHT NOT BE
WANTED?
>> I'M SO DPLAD YOU ASKED ABOUT
IT EMPLOY SO THERE ARE HEALTH
SYSTEMS, 1 THAT'S NOTABLE IS
EMORY IN ATLANTA WHERE ALL THE
DOCTORS HAVE ACCESS TO 1 OF
THESE AND WHAT'S INTERESTING IS
THAT THE HEALTH IT PHYSICIAN
OVERSEEING, HE SELLS, HE SAYS HE
GETS LOVE NOTES FROM ALL HIS
DOCTORS, I NEVER HEARD OF THAT,
LOVE NOTE TO THE IT PHYSICIAN.
THERE HASN'T BEEN A CONCERN
WHERE IT DOESN'T HAVE TO GO
THROUGH FDA, THE POINT ABOUT
PRIVACY AND WITH THE HEALTH AND
DATA BUT WHAT'S NICE IS EVERY
PATIENT GETS THEIR AUDIO AND
THEIR WRITTEN TRANSCRIPT WHEN
THEY LEAVE THE OFFICE VISIT.
I THINK IT WILL BECOME THE NORM
VERY QUICKLY FOR EPIC CONCERN
AND THE OTHER HEALTH SYSTEMS ARE
BASICALLY FIGURING OUT WHICH 1S
TO WORK WITH AND HOW TO
INTEGRATE IT AND MAKE IT
EMBEDDED IN THEIR CAPABILITY,
IT'S EXTRAORDINARY AND IT'S
WOORKING MUCH BETTER THAN I
EXPECTED.
NOW THERE'S ABOUT 50 DIFFERENT
COMPANIES IN THIS SPACE, THE
LARGEST 1 IS MICROSOFT WITH
NUANCE AND THAT 1 IS NOT
ACTUALLY NEARLY AS GOOD AS SOME
OF THE OTHERS THAT ARE
START-UPS.
WHAT'S INTERESTING IS SOME OF
THEM JUST DO THE NOTE, THEY
DON'T DO ANYTHING ELSE, AND SOME
OF THEM DO EVERYTHING.
AND OF COURSE IT DOES REQUIRE
THE NURSE OR PHYSICIAN OVERSIGHT
TO MAKE SURE THAT THE
PRESCRIPTIONS AND--YOU KNOW BUT
IT'S MUCH QUICKER BECAUSE IT
DOES ALL THE CLICKS AND ALL THE
EXTRA WORK AND THE NOTABLE TIME
IT SAVES IN THE EARLY GOING OF
SAVE 2-3 HOURS ON AVERAGE AND
THAT KEEPS GROWING SO RIGHT NOW
IT'S THE NEAR TERM EXCITEMENT OF
AI.
AND WITH THE OVERSIGHT THE ERROR
CONCERN IS NOT THERE AND IT
QUICKLY LEARNED THE PHYSICIANS
WAY OF COMMUNICATING AND HERE'S
1 OTHER THING I DIDN'T MENTION
AND THIS IS STRIKING.
IN THE NEXT COUPLE YEARS EVERY
PHYSICIAN WILL HAVE TO HAVE
COACHING BY THE AI.
NOW THINK ABOUT THIS, WHEN THE
AI NOTE, AND NOTE IT SAYS,
DR. JONES, WHY DID YOU INTERRUPT
THE PATIENT SO QUICKLY?
WHY DIDN'T YOU LET HER TELL YOU
ABOUT THIS OR THAT?
AND SO WHEN YOU SEE THE CRITIQUE
TAKEN--THEY IT PROVIDES TO THE
PHYSICIAN, YOU SAY OH MY GOSH
THIS WILL MAKE DOCTORS BETTER.
IT DOESN'T HAVE THE SAME AFFECT
AS MUCH ON NURSES BUT DOCTORS
REALLY NEED EMPATHY CHECK UPS
AND COMMUNICATION AND
SENSITIVITY CHECK UPS SO I SEE
THAT AS 1 OF THE NEXT FEATURES
THAT WILL BECOME NEVER AGAIN,
WHO WOULD HAVE GUESSED AND SO I
WOULDN'T BE SURPRISED IF IT'S A
REQUIREMENT OVER THE NEXT COUPLE
OF YEARS.
NETWORK HI, JEFF, NCI LABORATORY
PATHOLOGY, I KNOW YOU TOUCHED A
LITTLE BIT ABOUT FDA AI AND
MEDICAL TALK AND I'VE DONE A LOT
OF RESEARCH AROUND A DEVICE
THAT'S A LIMITED OUTPUT OUTCOME,
IT'S ASKING A YES OR NO QUESTION
IN THE AI MULTIMODAL INPUT AND
OUTPUT, IT SEEMS LIKE THEY'RE
REALLY LOST ON HOW TO REGULATE
THOSE AND I WAS WONDERING IF YOU
COULD EXPAND ON HOW THEY SHOULD
APPROACH ACCURACY WHEN THE INPUT
AND OUTPUT ARE VARIED.
>> WELL THE SIMPLE ANSWER IS THE
FDA HAS NOT, PROVED ANY MOWLTY
MODAL MODELS BECAUSE THEY
HAVEN'T FIGURED OUT HOW THEY'RE
GOING TO DO IT, AS YOU SAY.
BUT THE CRITERIA FOR THE
ORIGINAL MOSTLY RADIOLOGY BUT A
LOT OF THE OTHERS AS I
MENTIONED, THE CRITERIA FOR
APPROVAL WAS SO WEAK.
I'VE ACTUALLY GIVEN MY FRIEND
ROB [INDISCERNIBLE] A HARD TIME
ABOUT THIS BECAUSE THEY'RE NOT
SHOWING TEETH ABOUT DEMANDING
THAT THE DATA IS MORE ROBUST AND
PUBLISHED AND TRANSPARENT AND
HOW WILL WE GET THAL TO BE
ACTUALIZED IN DAILY PRACTICE, SO
RIGHT NOW THEY'RE CAUGHT IN A
VERY TOUGH SITUATION BECAUSE
THEY WERE TOO LOW A THRESHOLD
AND NOW AI HAS GOTTEN MUCH MORE
COMPLICATED IN THESE NEW MODELS
SO WE'RE SITTING IN SUSPENSION
RIGHT NOW EMPLOY THEY HAVEN'T
OFFERED GUIDANCE AND IT'S GOING
TO BE INTERESTING TO SEE WHAT IS
THE NEXT STEP BUT I DON'T KNOW
HOW THIS IS GOING TO PLAY OUT
EMPLOY I'M WORRIED ABOUT IT
BECAUSE THE FACT THAT WE HAVE
NERL A THOUSAND AI--THEY CALL
DEVICES, THEY'RE REALLY SOFTWARE
THAT ARE AUTHORIZED AND MOST OF
THEM ARE NOT IN RUES, THAT
DOESN'T SAY--AND MOST OF THEM
ARE OF COURSE NEVER BEEN
PUBLISHED IN A MEDICAL PEER
REVIEW IT'S REALLY WORRISOME
THAT WE CAN'T KEEP DOING THAT
SORT OF THING.
WE NEED TO HAVE MUCH BETTER DATA
SETS AND TRANSPARENCY AND WE'RE
GOING TO--IT'S HITTING THE ACID
TEST RIGHT NOW BECAUSE WITHIN
THE NEXT FEW MONTHS SOME OF
THESE MULTIMODAL MODELS WILL BE
TRYING TO GET FDA APPROVAL.
>> HI, THANK YOU VERY MUCH.
I'M JASMINE [INDISCERNIBLE] FROM
BIOETHICS AND MY BACKGROUND IS
IN PHILOSOPHY SO I HOPE YOU WILL
EXCUSE PERHAPS THE SIMPLISTIC
DESCRIPTION I'M ABOUT TO GIVE OF
THE DIAGNOSTIC PROCESS BUT I
WANTED TO ASK YOU ABOUT THE
TIMING OF WHEN WE SHOULD USE
THESE AI TOOLS IN DIAGNOSTICS.
SO IT SEEMS TO ME THAT THERE'S
SOME REASON TO HAVE A PHYSICIAN
FIRST TAKE A LOOK AT YOU KNOW
THE SCAN THEMSELVES AND SEE WHAT
THEY THINK, AND THEN TURN TO THE
AI AND SEE IF IT'S FLAGGING
SOMETHING THEY MISSED ARK
CORRELATED POSE DOING IT THE
OTHER WAY AROUND WHICH WILL BE
TIME SAVING BUT THEN YOU RISK ON
LEANING ON IT TOO HEAVILY, AND I
WONDERED WHAT YOU THINK ABOUT
THESE RISKS, THAT BALANCE ON
RELYING ON IT TOO MUCH VERSUS
TIME SAVING COMPONENT?
>> I LIKE YOUR POINT.
THE HUMAN FIRST, U MAN
INTELLIGENCE FIRST AND THEN AI
TO BE KIND OF CHECKING MAYBE
SOME OTHER DIAGNOSIS TO CONSIDER
OR ONLY TURNING TURNING TO IT N
THERE'S LACK OF FULL CONFIDENCE
FOR THE RIGHT DIAGNOSIS.
AS FAR AS I KNOW THAT SEQUENCE
IS VERY IMPORTANT BUT IT HASN'T
REALLY BEEN STUDIED.
I THINK IT'S A REALLY
INTERESTING QUESTION YOU BRING
UP.
IT WOULD BE BETTER, I WOULD JUST
THINK TO HAVE THE AI DONE MORE
IN THE INTEREST OF CONSULT AND
THOUGHT PROCESS AS STARTED.
THE PROBLEM IS WE GOTTA GET
CLINICIANS MORE TIME SO THEY CAN
ACTUALLY GO THROUGH ALL THIS AND
OFTEN THEY'RE THINKING ABOUT
THESE DAYS, HOW CAN I SAVE TIME,
I WILL GO TO AI FIRST AND NOT
EACH HAVE TO THINK, THAT'S
PROBABLY NOT A GOOD IDEA BUT
GREAT POINT YOU'RE MAKING.
>> THANK YOU.
BEFORE WE JUMP TO THE NEXT
QUESTION, THERE ARE MANY
QUESTIONS THAT HAVE COME IN
THROUGH THE ONLINE CHAT SO I'M
JUST GOING TO PULL 1 AND ASK
YOU.
>> SURE.
>> SO, SOMEBODY ONLINE IS
DESCRIBING THAT SOMETIMES
PHYSICIANS BECAUSE OF TIME
CRUNCHS OR OTHER THINGS HAVE A
RUSH TO JUDGMENT, THEY MIGHT
DIAGNOSE HYPERTENSION AFTER A
SINGLE CERTAINLY--CERTAINLY
VALENTINEDUATEED ELEVATED BLOOD
PRESSURE, AND THE QUESTION BEING
ASKED IS HOW WELL DO AI MODELS
ACCOUNT FOR GASH CANCER CENTER
IN AS A WIMBERLY TO ALSO SUPPORT
DOUBLE CHECKING BASED ON CERTAIN
DIAGNOSIS SO THAT IT CAN BE MORE
OBLIGATIONS YECTIVE IN THE
FUTURE SO THE GARBAGE IN-GARBAGE
OUT MODEL.
>> YEAH THIS, IS WHAT I WAS
TRYING TO GET AT WITH THE
FORECASTING PART OF THE TALK
WHICH IS IF WE HAVE IN RICH
DATA, MULTIPLE LAYERS WE WILL BE
SO MUCH BETTER, SO MUCH MORE
ACCURATE BUT WE'RE ONLY DEALING
WITH 1 THING LIKE A PERSON'S
BLOOD PRESSURE WHEN THEY SHOWED
UP TO A CLINIC WHICH IS
NOTORIOUSLY HARD TO ASSESS, THAT
WILL LEAD TO PROBLEMS AND SOME
OF OUR DIAGNOSTIC INACCURACY IS
THE RUSH, THE LACK OF TIME, RUSH
TO JUDGMENT, RUSH TO ERONIOUS
DIAGNOSIS.
IT'S BEEN SHOWN THAT IF A
CLINICIAN THINKS OF THE FIRST
DIAGNOSIS WITHIN THE FIRST 5
MINUTES THE ACCURACY IS HIGH
ABOUT 95%, BUT AFTER A WHILE, IT
DROPS OFF, DOWN INTO THE 20S.
WE NEED TO DO BETTER ON THIS FOR
SURE AND THE BIGGEST THING WE
CAN DO IS HAVE THESE LAYERS OF
DATA AVAILABLE, SO THE AI, WHICH
IS WHAT THE U MAN CAPABILITIES
FOR GOING THROUGH LARGE DATA
SETS, WE'RE TALKING ABOUT BIG
DATA THEA THE INDIVIDUAL LEVEL,
WE'RE NOT GOOD AT THAT.
SCANNING ALL THE LABS EVEN
WITHIN THE NORMAL RANGE TO LACK
AT TRENDS, PULLING IN DATA FROM
DIFFERENT PROVIDERS AND HEALTH
SYSTEMS AND EVERYTHING ELSE AND
ALSO THE UNSTRUCTURED TEXT.
WE DON'T TEND TO READ THAT IN
THE NOS.
SO THAT'S WHERE AI CAN HOPEFULLY
MAKE A DIFFERENCE.
>> HI, JONATHAN [INDISCERNIBLE]
I ALSO IN THE BIOETHICS
DEPARTMENT HERE.
SO I WAS REALLY--CAN YOU HEAR
ME?
>> YEAH.
>> SO I WAS REAL E EXCITED ABOUT
THE TALK, IT WAS INTERESTING.
I AM WONDERING IF YOU CAN SAY
MORE, THIS IS LESS OF A
SHORT-TERM CONCERN, MAYBE A
LONG-TERM THING, COULD YOU SAY A
BIT MORE ABOUT WHY YOUR--YOU
SEEM FAIRLY CONFIDENT THAT THIS
SORT OF THING WON'T BE A THREAT
TO YOB CORPSS FOR INSTANCE AND
IT SEEMS LIKE, I COULD SEE WHY
YOU WANT SOME KIND OF HUMAN
OVERSIGHT BUT HISTORICALLY WHEN
THERE'S SOME AUTOMATION THAT'S
INTRODUCED, IT'S USUALLY THIS
WON'T REPLACE YOUR JOB, BUT YOU
KNOW IF WE'RE TALKING ABOUT AN
AI CORRECTING A DOCTOR, COACHING
A DOCTOR AT A CERTAIN POINT, IT
MAKING ME WONDER, YOU KNOW WHY
INEVITABLY THIS SORT OF THING
WON'T LEAD TO JOB LOSS, SOIME
WONDERING IF YOU CAN EXPLAIN
THAT.
>> SURE, SURE.
I WANT TO BE CLEAR, I'M NOT
TALKING ABOUT THE ADMINISTRATIVE
JOBS LIKE CODING AND BILLING AND
BACK OFFICE, THOSE JOBS WILL BE
THREATENED BECAUSE ARK I CAN DO
A LOT OF THAT AND THEY CAN
REDUCE THE NUMBER OF PEOPLE IN
THOSE OPERATIONS CONSIDERABLY.
BUT WE HAVE A SHORTAGE OF
PHYSICIANS AND NURSES AND
PHARMACISTS AND MOST
PROFESSIONALS IN HEALTHCARE, SO
THE POINT IS IF WE CAN IMPROVE
THEIR PRODUCTIVITY AND REBOOT TO
GET BACK THE HUMAN-HUMAN BOND
THAT IS THE ESSENTIALITY OF
MEDICINE, THAT SHOULD BE OUR
OBJECTIVE.
THE QUESTION THOUGH, GOING BACK
TO JEFFREY HINTON HE PREDICTED 9
AGOS THAT WITHIN 5 YEARS WE
WOULDN'T HAVE ANY RADIOLOGISTS,
WELL, GUESS WHAT IN IT DOESN'T
HAVE AN EFFECT AND IT LIKELY
WILL HAVE AN EECT BUT THE POINT
IS THAT WE DON'T HAVE ENOUGH
PHYSICIANS, OUR MAN POWER, IF
YOU LOOK AT THE YB GROWTH IN THE
UNITED STATES, WHAT YOU SEE IS
HEALTHCARE KEEPS EVERY QUARTER,
EVERY YEAR, LEADS ALL OTHER JOBS
AND IT'S BASICALLY ACCOUNTING
FOR INCREASING SHARE OF OUR
GDPRKS P, SO WE HAVE--GDP SO WE
HAVE TO FIGURE OUT WAYS SOMEHOW
TO FIGURE OUT THE WORKFORCE
ISSUES AND HOW AI CAN HELP WITH
PRODUCTIVITY.
SO FOR EXAMPLE, IF A PHYSICIAN
HAS PAJAMA TIME WORKING ON THIS
AFTER HOURS, THEY'RE NOT DOING
THAT, BUT IF THEY HAVE MORE
TRUST AND WORKING WITH PATIENTS
BUT THAT'S WHEREY WOO WANT TO
GO.
THE HOPE IS TO IMPROVE MEDICINE
WITHOUT THREATENING THE YOB
CORPSS, THERE IS NO EVIDENT YET
OF CLINICIANS HAVE ANY REDUCTION
IN WORKFORCE, EVEN IN PLACES
OUTSIDE OF THE U.S., IN CHINA,
AND UK AND EUROPE EMPLOY SO I
DON'T THINK IT'S LIKELY.
>> [INDISCERNIBLE] FROM NIAMS,
YOU MENTIONED LLMs A LOT
NOVEMBER TALK AND I HAVE A
FEELING SOMEONE LISTENING TO
YOUR TALK MAY HAVE COME OUT WITH
THE IMPRESSION THAT THESE THINGS
BY DESIGN ARE NONFACTUAL AND
NONDETERMINISTIC RIGHT, SO YOU
DID MENTION THE CONTABUALATIONS
AT THE END WHICH IS A CLEAR
EXAMPLE OF THAT AND THE
CONQUENCES OF THAT ARE OBVIOUSLY
MAJOR WHEN WE APPLY THESE THINGS
TO MEDICINE SO SO WHAT WE'VE
BEGUN TO SEE, IS THE NLM, OR THE
NLM IS THE TALKING HEAD OF THE
MULTIMODAL SYSTEM, WHAT'S YOUR
SENSE OF WHERE NLMs WILL END
UP IN THE MULTIMODAL SPACE GIIVE
WE KNOW THAT THE NLM IS NOT THE
PART THAT WILL BE SORT OF
SOLVING THE PROBLEM.
DOES THE QUESTION MAKE SENSE?
>> YEAH, WE'RE SEEING A LOT OF
STRATEGIES TO REDUCE ERRORS, SO
FOR EXAMPLE, OPEN AI IS
INTRODUCING STRAW BERRY AS AN
NLM, AND IT THINKS BEFORE IT
GIVES AN ANSWER, THERE'S A DELAY
AND THAT THE QUESTION IS WILL
THAT LOWER--BUT WE HAVE SEEN
STRONG EVIDENT THAT OVER THE
LAST 2 YEARS, THE HALLUCINATION
CONFABULATION RATE KEEPS
DECREASING.
IT HAS TO BE KOWPED IN HERE WITH
THE HUMAN IN THE LOOP STORY
WHICH IS UNIQUE TO MEDICINE.
WHO'S GOING TO WANT TO MAKE A
DIAGNOSIS WITHOUT THAT TYPE
OF--SO I AM NOT AS CONCERNED
ABOUT IT BECAUSE I SEE IT AS
IMPROVING, I SEE VARIOUS
STRATEGIES, AND THE
CONVERSATIONAL CAPABILITY, AND
SO, AT THE PATIENT LEVEL, WE
DON'T TALK ENOUGH ABOUT THAT,
THE FACT THAT ANYBODY COULD PUT
IN DATA AND GET SOME IDEA WHAT
COULD BE GOING ON, COMTHAT TO A
GOOGLE SEARCH WHERE A PERSON
LOOKS UP SOMETHING ASK THEY
ALREADY HAVE THAT DISEASE AND
NONE OF THE DATA SPECIFIC TO
THEM, BECAUSE THEY DON'T HAVE
THAT CAPABILITY, SO I SEE
THERE'S LOTS OF POTENTIAL FOR
IMPROVEMENT BUT CAN WE REST ON
THAT?
NO, WE HAVE TO KEEP DEMANDING,
TO STRIVE, IT'LL NEVER BE
PERFECT BUT I'M NOT--I'M NOT AS
CONCERNED ABOUT THAT AS SOME OF
THE OTHER ISSUES LIKE THE
BIASES, THE INIQUITIES, THOSE
SORTS OF THINGS.
NTHANK YOU.
>> THANK YOU.
>> SO I'M MAT MAT
[INDISCERNIBLE] I'M NICHD, BUT I
GREW UP IN A RURAL AREA AND IT
SEEMS LIKE AI HAS A THE POWER TO
EXTRAPOLATE CLINICAL DATA AND GO
THERE WOULD YOU BE WILLING TO
TALK MORE ABOUT OTHER WAYS THAT
YOU COULD SEE AI HELPING
HEALTHCARE IN RURAL AREAS WHERE
DATA COLLECTION FOR PHYSICIANS
HAS HISTORICALLY BEEN LIMITED?
>> YEAH, I MEAN I THINK WHAT
WE'RE SEEING EVEN NOT JUST IN
RURAL U.S., BUT WORLD WIDE.
I MEAN WE HAVE PEOPLE IN AFRICA
WHO NEVER ACQUIRED AN ULTRA
SOUND IMAGE USING AI TO SAY PUT
IT ON THE CHEST, GO CLOCKWISE,
GO COUNTER CLOCKWISE, LIKE AN
AUTOMATED CAPTURE WHEN THE IMAGE
IS APPROPRIATE AND THEN FEETING
AN INTERPRETATION, SO TAKING
SOMETHING LIKE THAT THAT CAN BE
DONE ANYWHERE IN THE WORLD BOTH
IN THE ACQUIRING OF THE CLINICAL
GENETICSAGE AND THE ALGORITHM
FOR INTERPRETATION.
THIS IS JUST 1 EXAMPLE WHERE I
THINK THIS CAN GO BUT I DO THINK
THE REACH, WHEREVER THERE'S
ADEQUATE BAND WIDTH, WE CAN SEE
BIG CHANGES SO LEVELING THE TYPE
OF CAPABILITIES AND REDUCING THE
WORRY ABOUT INIQUITIES PEOPLE
DON'T SPEND ENOUGH TIME THINKING
ABOUT THE RURAL SIDE, USUALLY
ARE THINKING ABOUT
UNDERREPRESENTED MINORITIES BUT
I DO THINK IT'S A BIG ISSUE AND
I'M SAYING THAT IT WILL BE
IMPROVED.
>> ALL RIGHT, SO TO KEEP
EVERYBODY ON TIME WE WILL TAKE 1
LAST QUESTION AND FOR THOSE THAT
DON'T GET YOUR QUESTIONS ASKED
YOU CAN PERHAPS APPROACH AFTER
THE SESSION IS COMPLETED AND FOR
THOSE THAT ARE ONLINE, WE WILL
PASS YOUR QUESTIONS TO DR. TOPOL
AS WELL.
SO PLEASE, FINAL QUESTION.
>> HI, I'M JACOB [INDISCERNIBLE]
FROM THE CLINICAL NEUROIMACKING
RESEARCH CORE AT THE NIAA, THANK
YOU FOR THE INSPIRING YET
HUMBLING TALK.
IN THE CONTEXT OF ALL OF THESE
DRAMATIC CHANGES THAT WE'RE
FACING IN THE MEDICAL FIELD, DO
YOU HAVE ANY PIECE OF ADVICE FOR
ALL THE TRAINEES HERE THAT ARE
AT THE BEGINNING OF THEIR
CAREERS IN SCIENCE AND MEDICINE?
>> WELL MY ADVICE WOULD BE I
WISH I WAS YOU BECAUSE YOU'RE
GOING TO BE IN THE MOST EXCITING
TIME OF MEDICINE.
AND YOU DON'T NECESSARILY HAVE
TO BE AN AI EXPERT BUT YOU
SHOULD UNDERSTAND THE NUANCES
AND GET ENOUGH GROUNDING AND
HAVE THE TYPE OF COLLABORATIONS
YOU NEED TO TEST THESE NEW
WATERS AND TOOLS AND HOW THEY
IMPLEMENT THEM, WHEN TO
IMPLEMENT THEM BUT IT IS REALLY
EXCITING BECAUSE WE'VE BEEN IN
NEED OF A RESCUE IN MEDICINE FOR
A NUMBER OF YEARS.
WE HAVE ALL THIS DATA ALL
DRESSED UP AND NOWHERE TO GO,
HOARDING DATA, STORING DATA AND
TO YOU WE HAVE A WAY FORWARD
THAT IS QUITE EXTRAORDINARY.
SO IT'S GOING TO TAKE TIME IN
MEDICINE, HEALTHCARE ALWAYS
TAKES LONGER THAN IT SHOULD.
WE HAD TO HAVE A PANDEMIC BEFORE
TELEMEDICINE EVER CAME IN USE,
SO HOPEFULLY THIS WON'T REQUIRE
SOMETHING LIKE THAT BUT IT TAKES
LONGER BUT YOU'RE BEING YOUNG
AND ALL THE OTHER TRAINEES HERE
ARE IN A VERY PREPICIOUS
SITUATION AND POSITION BECAUSE
YOU'RE GOING TO SEE IT.
IT'S GOING TO HAPPEN IN YOUR
CARE OF PATIENTS AND YOUR CARER
AND THAT'S SO EXCITING.
>> THANK YOU DR. TOPOL FOR A
WONDERFUL TALK .
[ APPLAUSE ]