>> WELCOME TO THE
ANNUAL KUAN-TEH JEANG
LECTURE.
MY I AM THE ACTING DIRECTOR OF E
NIH OFFICE OF EQUITY, DIVERSITY
AND INCLUSION.
EDI IS PLEASED TO PARTNER WITH
THE OFFICE OF INTRAMURAL
RESEARCH TO HOST THIS VERY
IMPORTANT LECTURE AS WE
CELEBRATE ASIAN AMERICAN, NATIVE
HAWAIIAN AND PACIFIC ISLANDER
HERITAGE MONTH.
THANK YOU FOR TAKING TIME TO
PAUSE AND HONOR THE LIFE AND
LEGACY OF AN INDIVIDUAL WHO
MEANT SO MUCH TO SO MANY.
I'M HONORED TO HAVE PERSONALLY
KNOWN AND WORKED WITH K.T.
THOSE WHO KNEW K.T. KNEW HE WAS
A BRILLIANT SCIENTIST WHO WAS
PASSIONATE ABOUT DIVERSITY,
EQUITY, INCLUSION AND
ACCESSIBILITY.
I HAD THE PLEASURE OF WORKING
WITH K.T. IN THE DEIA SPACE AND
TO HAVE EXPERIENCED HIS STAUNCH
ADVOCACY FOR THE CAREER
DEVELOPMENT OF ASIAN AMERICAN
SCIENTISTS.
I KNOW K.T. WOULD BE PROUD OF
THE FEDERAL GOVERNMENT'S CURRENT
FOCUS ON DEIA, AND HE WOULD
SURELY BE ACTIVELY ENGAGED IN
NIH'S EFFORT TO DEVELOP ITS
FIRST DEIA STRATEGIC PLAN.
I ENCOURAGE YOU TO VISIT EDI'S
WEBSITE TO TAKE PART IN THE
CURRENT ASIAN AMERICAN NATIVE
HAWAIIAN AND PACIFIC ISLANDER
HERITAGE MONTH CAMPAIGN.
THIS YEAR'S THEME IS "WE'RE NOT
A MONO LITH," AND IT FOCUSES ON
THE VASTLY DIVERSE LANGUAGES,
CULTURES, IDENTITIES, RELIGIONS,
AND CUSTOMS FROM THE MAIN
COUNTRIES UNDER THE AANHPI
UMBRELLA.
LET HIS LIFE'S WORK INSPIRE US
TO STRIVE FOR PRO TETIONAL
EXCELLENCE, RAISE OUR VOICES TO
DRAW ATTENTION TO INEQUITIES,
AND COMMIT TO FINDING SOLUTIONS.
NOW WE WILL HEAR FROM DR. ROLAND
OWENS, DIRECTOR OF RESEARCH
WORKFORCE DIVERSITY IN THE
OFFICE OF INTRAMURAL RESEARCH,
AND HE WILL PAY TRIBUTE TO
DR. JEANG AND ACKNOWLEDGE HIS
FAMILY.
>> THANK YOU.
SO I KNEW TEH IN TWO CAPACITIES.
BEFORE I TOOK OVER MY CURRENT
POSITION, I WAS A VIROLOGIST IN
THE DIABETES INSTITUTE AND SO I
KNEW HIM AS PART OF THE VIROLOGY
COMMUNITY.
AND THEN WHEN I TOOK OVER AS
DIRECTOR OF RESEARCH WORKFORCE
DEVELOPMENT, WE ENGAGED
FREQUENTLY IN ISSUES OF
DIVERSITY AND INCLUSION.
SO I'D LIKE TO SHARE SOME OF MY
REMEMBRANCES OF DR. JEANG.
HE WAS BORN IN 1958 IN TAIWAN,
AND THEN MOVED TO LIBYA TO SPEND
MOST OF HIS CHILDHOOD.
THEN HE CAME TO THE UNITED
STATES IN 1970.
THIS IS ACTUALLY MORE COMMON A
STORY AT NIH THAN YOU WOULD
THINK OF PEOPLE BEING WORLD
TRAVELERS TO FINALLY GET TO NIH.
HE ATTENDED THE MASSACHUSETTS
INSTITUTE OF TECHNOLOGY, AND
THEN EARNED HIS MD PH.D. FROM
JOHNS HOPKINS AT AGE 25.
AND STUDIED REGULATION OF GENE
EXPRESSION IN CYTOMEGLOVIRUS
WITH GARY HAYWARD.
THEN HE CAME TO THE NIH IN 1985,
ACTUALLY THE SAME YEAR I DID.
AND DID A POSTDOCTORAL
FELLOWSHIP WITH THE LATE
DR. GEORGE KORY IN THE NATIONAL
CANCER INSTITUTE.
HE THEN MOVED TO THE NATIONAL
INSTITUTE OF ALLERGY AND
INFECTIOUS DISEASES AND BECAME
CHIEF OF THE MOLECULAR VIROLOGY
SECTION IN THE LABORATORY OF
MOLECULAR MICROBIOLOGY, WE HE WE
STUDIED HUMAN IMMUNODEFICIENCY
VIRUS HIV-1 AND A RELATED VIRUS,
HTLV-1.
HE WENT ON TO BECOME
EDITOR-IN-CHIEF OF RETRO
VIROLOGY, AND HE WAS A LEADER
WITHIN THE VIROLOGY INTEREST
GROUP.
HE WAS ALSO A LEADER WITHIN THE
SEW
SOCIETY OF CHAI KNEE
BIOSCIENTISTS IN AMERICA AND AN
ADVOCATE FOR RACIAL MINORITY
GROUPS AND PERSONS WITH
DISABILITIES.
JUST AS A PERSONAL ASIDE, WE
USED TO PARK IN THE SAME PARKING
LOT FREQUENTLY AND, YOU KNOW,
ABOUT ONCE A MONTH, HE WOULD
COME UP TO ME AND SAY, HEY,
ROLAND, OKAY, SO HAVE YOU MADE
ANY PROGRESS THIS MONTH?
AND IT'S REALLY IMPORTANT TO
HAVE PEOPLE LIKE THAT WHO ARE
WILLING TO KEEP TALKING ABOUT
THINGS AND KEEP HOLDING OUR FEET
TO THE FIRE.
AND WE CELEBRATE HIS LIFE AND
LEGACY IN MANY WAYS.
ONE, THIS LECTURESHIP.
WE ALSO HAVE A SPECIAL
RECOGNITION FOR FAIR AWARD
WINNERS WHO SPECIALIZE IN
VIROLOGY, AND IF YOU'VE EVER
BEEN BETWEEN BUILDING 31 AND --
OUTSIDE BUILDING 31, THERE'S A
LITTLE MEMORIAL TO DR. JEANG.
WHICH IS SHOWN HERE.
AND THIS ALSO REMINDS ME TO
RECOGNIZE THAT WE DO HAVE
MEMBERS OF THE FAMILY PRESENT ON
THIS -- AT THIS EVENT.
I WOULD LIKE TO THANK HIS WIFE,
HIS BROTHERS, HIS CHILDREN, AND
ALL THE FAMILY MEMBERS FOR
SHARING HIS LIFE WITH US.
NOW I'LL HAND IT OVER TO
DR. MICHAEL GOTTESMAN, THE
DEPUTY DIRECTOR FOR INTRAMURAL
RESEARCH, WHO WILL INTRODUCE
TODAY'S SPEAKER.
>> THANK YOU, ROLAND.
GOOD AFTERNOON, EVERYONE.
LET ME ADD MY WELCOME TO THIS,
THE 2022 KUAN-TEH JEANG MEMORIAL
LECTURE.
I'M MICHAEL GOTTESMAN, AS YOU'VE
HEARD, DEPUTY DIRECTOR FOR
INTRAMURAL RESEARCH AT THE NIH.
AS YOU ALSO JUST HEARD, TEH
JEANG, ASIDE FROM BEING A
REMARKABLE SCIENTIST, WAS A
STAUNCH ADVOCATE FOR DIVERSITY
AND INCLUSION OF MINORITIES.
HE SUBSCRIBED TO THE MOTTO THAT
TALENT KNOWS NO BORDERS.
FOR TEH, THIS APPLIED NOT ONLY
TO THE BIOMEDICAL RESEARCH
WORKFORCE AS A WHOLE, BUT TO
LEADERSHIP POSITIONS TOO.
AND WE CERTAINLY HAVE FOUND
REMARKABLE TALENT IN TODAY'S
SPEAKER, DR. MICHAEL CHIANG, WHO
IS ONLY THE SECOND CHINESE
AMERICAN TO DIRECT AN NIH
INSTITUTE.
DR. CHIANG IS THE DIRECTOR OF
THE NATIONAL EYE INSTITUTE,
RECRUITED JUST TWO YEARS AGO,
JUST BEFORE THE PANDEMIC IN
2020.
DR. CHIANG IS AN OPHTHALMOLOGIST
WITH EXTENSIVE EXPERIENCE AS A
CLINICIAN, RESEARCHER, AND
EDUCATOR.
HIS RESEARCH HAS INVOLVED
TELEMEDICINE AND ARTIFICIAL
INTELLIGENCE FOR DIAGNOSIS OF
RETINOPATHY OF PREMATURITY AND
OTHER OPHTHALMIC DISEASES.
IMPLEMENTATION AND EVALUATION OF
ELECTRONIC HEALTH RECORD
SYSTEMS, MODELING OF CLINICAL
WORKFLOW, AND DATA ANALYTICS.
THESE TALENTS WHICH ARE CUTTING
EDGE HAVE REALLY IMPROVED THE
ENVIRONMENT AT THE NIH
SCIENTIFICALLY.
DR. CHIANG EARNED HIS
UNDERGRADUATE DEGREE IN
ELECTRICAL ENGINEERING AND
BIOLOGY FROM STANFORD
UNIVERSITY.
HIS MASTER'S DEGREE IN
BIOMEDICAL INFORMATICS FROM
COLUMBIA UNIVERSITY COLLEGE OF
PHYSICIANS AND SURGEONS, AND HIS
M.D. AND ADDITIONAL MASTERS IN
MEDICAL SCIENCE FROM THE HARVARD
MEDICAL SCHOOL AND THE HARVARD
M.I.T. DIVISION OF HEALTH
SCIENCES AND TECHNOLOGY.
PRIOR TO BEING NAMED DIRECTOR OF
THE NATIONAL EYE INSTITUTE,
DR. CHIANG WAS THE KNOWLES
PROFESSOR OF OPHTHALMOLOGY AND
MEDICAL INFORMATICS AND CLINICAL
EPIDEMIOLOGY AT OREGON HEALTH
AND SCIENCE UNIVERSITY, ALSO
KNOWN AS OHSU, IN PORTLAND,
OREGON, AND ASSOCIATE DIRECTOR
OF THE OHSU CASEY EYE INSTITUTE.
HE WAS A PRINCIPAL INVESTIGATOR
ON SEVERAL NIH GRANTS SINCE
2003, AND HE AND HIS RESEARCH
GROUP PUBLISHED MORE THAN 200
PEER REVIEWED JOURNAL PAPERS.
HIS CLINICAL PRACTICE IS FOCUSED
ON PEDIATRIC OPHTHALMOLOGY AND
ADULT STRABISMUS.
AS NEI DIRECTOR, DR. CHIANG
OVERSEES AN ANNUAL BUDGET OF
APPROXIMATELY $825 MILLION, THE
MAJORITY OF WHICH SUPPORTS
VISION RESEARCH THROUGH NEARLY
1600 RESEARCH GRANTS AND
TRAINING AWARDS MADE TO
SCIENTISTS AT MORE THAN
250 MEDICAL CENTERS,
UNIVERSITIES AND OTHER
INSTITUTIONS.
NEI, OF COURSE, ALSO HAS A
SIGNIFICANT AND SUCCESSFUL
INTRAMURAL RESEARCH PROGRAM THAT
INCLUDES CLINICAL RESEARCH.
THE TITLE OF DR. CHIANG'S TALK
TODAY IS ARTIFICIAL
INTELLIGENCE, OPHTHALMOLOGY AND
BEING AN ASIAN AMERICAN CLINICAL
SCIENTIST.
MICHAEL, I'M THRILLED YOU
ACCEPTED THE OFFER TO DELIVER
THIS YEAR'S KUAN-TEH JEANG
MEMORIAL LECTURE.
TEH WAS A GOOD FRIEND AND
COLLEAGUE VERY SPECIAL TO ME,
AND HE, TOO, WOULD BE THRILLED
TO KNOW SOMEONE OF YOUR CALIBER
HAS BEEN RECRUITED TO LEAD THE
NEI.
MICHAEL, YOU'RE ON.
>> MICHAEL, THANK YOU VERY MUCH
FOR THE INTRODUCTION.
AND TO ROLAND AND SHELMA, THANK
YOU FOR YOUR WORK IN PUTTING
THIS EVENT TOGETHER.
YOU KNOW, I AM ENORMOUSLY
PRIVILEGED TO BE ABLE TO GIVE
THIS KUAN-TEH JEANG MEMORIAL
LECTURE BASICALLY IN RECOGNITION
OF ASIAN AMERICAN NATIVE
HAWAIIAN PACIFIC ISLANDER
HERITAGE MONTH IN MAY.
I REALLY HOPE TO BE ABLE TO DO
JUSTICE TO DR. JEANG'S LEGACY
HERE.
I NEVER HAD AN OPPORTUNITY TO
MEET HIM BECAUSE I CAME ONLY IN
NOVEMBER 2020.
AND -- BUT WHAT I HAD A CHANCE
TO DO IS TO BE IN TOUCH WITH HIS
WIFE DIANE AND I SPOKE WITH HIS
BROTHER MING JUST TO LEARN A
LITTLE ABOUT HIM AND ABOUT THE
FAMILY AND I THOUGHT I WOULD
SHARE SOME OF WHAT I LEARNED.
JUST TO BUILD ON WHAT SOME OF
ROLAND AND MICHAEL MENTIONED, HE
WAS BORN IN TAIWAN AND WAS THE
YOUNGEST OUT OF THREE BROTHERS.
BECAUSE HIS FATHER WAS A CIVIL
ENGINEER, HE WAS SENT BY THE
GOVERNMENT AND THAT'S HOW THE
FAMILY ENDED UP IN AFRICA.
AND HE WAS KNOWN BY K.T., OR
SIMPLY T., AND K.T. WAS EDUCATED
IN THE BRITISH SCHOOLS.
MING MENTIONED THE OLDER
BROTHERS WERE SENT TO BOARDING
SCHOOLS IN IRELAND, SO THE OLDER
BROTHERS HAD IRISH ACCENTS AND
K.T. GREW UP WITH A BRITISH
ACCENT.
BUT THE FAMILY MOVED TO THE U.S.
WHEN K.T. WAS 12, AND HE REALLY
WENT TO SCHOOL IN ARIZONA AND
THEN HOUSTON.
AND WHEN HE WAS IN HIGH SCHOOL,
SAID, I WANT TO GO TO M.I.T.,
AND THE FAMILY REACTED BY
WONDERING HOW ARE WE GOING TO
PAY FOR M.I.T., IT'S VERY
EXPENSIVE.
SO IT WAS K.T. WHO LOOKED INTO
THE FACT THAT YOU COULD GET
SCHOLARSHIPS TO GO THERE, AND
THAT'S HOW HE ENDED UP STUDYING
AT M.I.T., WHERES WEIGH ON E
DEBATE TEAM.
HIS COACH EVENTUALLY BECAME
SECRETARY OF THE TREASURY AND
PRESIDENT OF HARVARD.
THIS IS A PICTURE OF THE FAMILY
I RECEIVED.
HERE'S K.T. AND HERE'S A PICTURE
OF HIM BACK WHEN HE WAS A
STUDENT AT M.I.T.
SO AS ROLAND HAD MENTIONED, WHEN
HE WAS 18, HE DECIDED THAT HE
WANTED TO ENROLL IN AN M.D. PH
D. PROGRAM AND WAS ACCEPTED AT
JOHNS HOPKINS, SO HE GOT
HIS DEGREES THERE, DID AN
INTERNSHIP IN MEDICINE AND
DECIDED THAT HE REALLY WANTED TO
DO A RESEARCH FELLOWSHIP AND
BUILD A CAREER IN ACADEMICS.
SO HE CAME HERE TO NIH.
AND EVENTUALLY HE BECAME CHIEF
AS ROLAND SAID OF THE MOLECULAR
VIROLOGY SECTION OF THE NIAID
LABORATORY OF MEDICAL
MICROBIOLOGY.
HE BECAME THE FOUNDING EDITOR OF
RETROVIROLOGY, AND WAS A
PROPONENT OF OPEN ACCESS
PUBLICATION LONG BEFORE IT
BECAME HIP, AND POPULAR, AND WAS
A REAL ADVOCATE FOR CAREER
DEVELOPMENT OF A YOUNGER
SCIENTIST AND MID CAREER
SCIENTISTS.
THIS PICTURE I GOT FROM DIANE,
HIS WIFE, WHO'S A VETERINARIAN
AT THE FDA, AND THE TWO OF THEM
MET AT JOHNS HOPKINS, AND HIS
CHILDREN ARE NOW ADULTS.
DAVID, JOHN AND DIANA.
AND JUST GOING BEYOND THAT, HE
WAS INTERESTED IN A LOT OF
SERVICE ACTIVITIES.
AS ROLAND MENTIONED, HE WAS
PRESIDENT OF THE SOCIETY OF
CHINESE BIOSCIENTISTS IN
AMERICA, AND HERE'S A PICTURE OF
THAT CREW, HE WAS AN EDITOR AT
CELL AND BIOSCIENCE, WHICH IS
THE SBCA'S JOURNAL AND ASSOCIATE
EDITOR OF CANCER RESEARCH.
HE WAS A MEMBER OF ACADEMIA
SINICA, TAIWAN, ACADEMY OF
SCIENCE.
THIS IS HIS NAMEPLATE FROM THAT.
THE THING THAT WE REALIZED IS
THE CHINESE CHARACTER FOR THE
NAME JEANG IS THE SAME AS MY
LAST NAME.
WE ROMANIZE IT DIFFERENTLY SO MY
NAME IS SPELLED C, H, I, A, N,
G.
BUT THEN RECEIVED AWARDS AT
JOHNS HOPKINS FOR PUBLIC
SERVICE.
ALSO THIS IS A PICTURE OF KT AND
MICHAEL GOTTESMAN THAT WAS SHOWN
BEFORE WHEN HE GAVE THE GEORGE
KHOURY LECTURE IN 2012.
AS SAID BEFORE, HE WAS A REAL
ADVOCATE FOR LEADERSHIP
OPPORTUNITIES FOR SCIENTISTS OF
ASIAN DESCENT AND TESTIFIED
BEFORE THE U.S. EQUAL EMPLOYMENT
OPPORTUNITY COMMISSION IN 2008,
AND EVENTUALLY GOT AN AWARD FROM
THE NIH EEO, HE GOT THE NIH EEO
AWARD POST HUMESLY POSTH.
SO I THOUGHT A LOT ABOUT THIS
AND IN TRYING TO PUT THIS
TOGETHER TO HONOR DR. JEANG'S
LEGACY, I THOUGHT I WOULD TALK
ABOUT NUMBER ONE ARTIFICIAL
INTELLIGENCE AND OPHTHALMOLOGY
BECAUSE THAT'S WHAT I DO
PROFESSIONALLY, BUT I ALSO
THOUGHT I WOULD SPEND ABOUT HALF
THE TIME TALKING ABOUT SOME OF
MY OWN THOUGHTS ABOUT BEING AN
ASIAN AMERICAN
CLINICIAN-SCIENTIST, BECAUSE I
FELT THAT THAT WOULD BE AN
APPROPRIATE WAY TO REFLECT ON
WHAT DR. JEANG'S LEGACY IS HERE.
SO I THOUGHT I WOULD JUST START
BY SAYING A LITTLE ABOUT WHAT MY
BACKGROUND IS.
SO LIKE K.T.'S FAMILY, MY
PARENTS WERE FROM TAIWAN.
THEY CAME TO THE U.S. IN THE
1960s, AND ORIGINALLY SETTLED
IN SOUTH CAROLINA AT A TIME WHEN
THE SOUTH AND THE U.S. WAS
SEGREGATED.
SO IT WAS A VERY INTERESTING AND
ODD INTRODUCTION TO THIS COUNTRY
FOR THEM.
WITHIN A FEW YEARS, THEY MOVED
TO PITTSBURGH, PENNSYLVANIA,
BECAUSE MY DAD WAS A GRADUATE
STUDENT AT CARNEGIE MELLON
UNIVERSITY AND THAT'S WHERE I
WAS BORN, IN PITTSBURGH.
SO WITHIN THE FIRST FEW YEARS OF
MY LIFE, WE MOVED TO DETROIT,
MICHIGAN, WHICH IS WHAT I WOULD
CONSIDER MY HOMETOWN, AND THE
REASON FOR THAT IS THAT MY
FATHER GOT A JOB AT FORD MOTOR
COMPANY, AND THE U.S. AUTO
INDUSTRY IS ALL IN DETROIT,
MICHIGAN.
SO I WOULD SAY THAT I HAD A VERY
CLASSIC MIDWESTERN UPBRINGING.
AMERICAN CULTURE BUILT ON THE
AUTOMOBILE AND IN MANY WAYS A
REALLY WARM AND FAMILY-ORIENTED
ATMOSPHERE IN THE MIDWEST.
NOW, IT TURNS OUT THAT WHEN I
WAS PROBABLY 12 YEARS OLD, I
REMEMBER MY DAD COMING HOME A
LITTLE BIT GRUMPY FROM WORK.
IT TURNED OUT HE HAD A
PERFORMANCE REVIEW, AND WHAT HIS
SUPERVISOR SAID AT THE TIME IS
THAT, YOU ARE REALLY AN
OUTSTANDING ENGINEER BUT YOU
HAVE TO IMPROVE YOUR
COMMUNICATION IN ENGLISH IF
YOU'RE GOING TO ADVANCE IN THE
ORGANIZATION AT FORD MOTOR
COMPANY.
AND HE WAS REALLY FRUSTRATED
ABOUT THAT.
BUT WE DIDN'T HAVE THE SORT OF
FAMILY WHERE, YOU KNOW, OUR
PARENTS WOULD TALK TO US ABOUT
THEIR PROBLEMS.
SO IT TURNED OUT THAT PROBABLY A
YEAR OR TWO LATER, I WAS IN THE
FAMILY CAR, AND THERE WAS A
CASSETTE TAPE HERE, AND I PUT
THE CASSETTE TAPE INTO THE RADIO
PLAYER, AND INSTEAD OF MUSIC,
WHAT CAME ON WAS A VOICE THAT I
RECOGNIZE AS WALTER CRONKITE'S
VOICE.
NOW, FOR THOSE OF YOU WHO DON'T
KNOW, WALTER CRONKITE WAS
PROBABLY THE MOST WELL-KNOWN
U.S. NEWS BROADCASTER IN THE
1970s AND 1980s, AND HE WAS
FOR A WHILE CONSIDERED THE MOST
TRUSTED PERSON IN AMERICA.
I DIDN'T REALIZE THIS UNTIL 10
YEARS LATER BUT THE REASON
WALTER CRONKITE'S VOICE WAS ON
THIS CASSETTE TAPE WAS BECAUSE
MY FATHER WAS TRYING TO LEARN TO
COMMUNICATE BETTER IN ENGLISH
AND HE THOUGHT WALLER IT
CRONKITE WAS THE BEST
COMMUNICATOR HE KNEW SO HE WOULD
TRY TO PATTERN HIS SPEECH AFTER
WALTER CRONKITE'S.
SO I'M GOING TO COME BACK AND
TALK ABOUT THAT.
SO THOSE ARE ALL SOME OF THE --
WHAT I'D SAY, YOU KNOW, WARM
THINGS ABOUT MY CHILDHOOD.
NOW, ONE OF THE OTHER THINGS I
WANT TO MENTION, THOUGH, IS THAT
THE DETROIT SUBURBS, IN THE
1980s, WERE VIRTUALLY 100%
WHITE.
IT'S AN AUTO TOWN.
IN THE 1980s, THERE WAS
SIGNIFICANT ANTI-ASIAN SENTIMENT
IN DETROIT, AND THE REASON FOR
THAT WAS JAPANESE COMPETITION
WITH THE U.S. AUTO INDUSTRY.
AND I PERSONALLY SAW SCENES LIKE
THIS, WHERE THERE WERE AUTO
WORKERS HERE DESTROYING A
JAPANESE CAR TO TAKE OUT THEIR
FRUSTRATIONS.
SO I THINK IN MANY WAYS, IT WAS
ONE OF PROBABLY THE MORE
DIFFICULT PLACES TO BE AN ASIAN
IN THIS COUNTRY BACK IN THE
1980s.
AND I FOUND THIS YOUTUBE CLIP.
THERE WAS A SHOW THAT WE USED TO
WATCH ON TV EVERY SINGLE WEEK,
AND I THOUGHT I WOULD JUST SHOW
A COMMERCIAL.
>> HELLO, EVERYONE IN TELEVISION
LAND.
IT'S ME, YOUR HOST OF MARTIAL
ARTS THIGH TER RIGHT HERE Y
NIGHT AT 11:00 P.M., JOIN ME FOR
MARTIAL ARTS THEATER, WE SHOW
CLASSIC MARTIAL ARTS FILMS AND
ALSO PATTI HERE WILL HAVE A
DEMONSTRATION OF ACTUAL MARTIAL
ARTS FOR YOU EVERY WEEK.
BUT DON'T WORRY, BECAUSE NONE OF
THIS IS DANGEROUS.
YOU'RE KILLING ME!
>> SO THIS WAS THE OTHER SIDE OF
MY HOMETOWN.
AND AGAIN, WE WATCHED THIS SHOW
EVERY SINGLE WEEK, AND YOU KNOW,
REALLY SORT OF ENJOYED THE
MOVIES.
BUT IN THAT BACKGROUND, IN 1982,
THERE WAS THE VINCENT CHIN CASE.
FOR THOSE OF YOU WHO AREN'T
AWARE OF THAT CASE, IT WAS A
RACIALLY MOTIVATED MURDER THAT
OCCURRED RIGHT OUTSIDE DETROIT.
VINCENT CHIN WAS 27 YEARS OLD AT
HIS BACHELOR'S PARTY AND GOT
INTO AN ALTERCATION WITH TWO
PEOPLE WHO WERE UNEMPLOYED AUTO
WORKERS WHO BASICALLY BLAMED HIM
FOR LOSING THEIR JOBS.
HE WAS MURDERED WITH A BASEBALL
BAT THAT EVENING.
WHEN THE CASE WENT TO TRIAL,
THERE WAS NEVER ANY DOUBT THAT
THE TWO PEOPLE WERE GUILTY, BUT
THE JUDGE BASICALLY SAID THAT
THESE WEREN'T THE TIME OF MEN
THAT YOU SEND TO JAIL, AND HE
SENTENCED THEM TO THREE YEARS OF
PROBATION AND A $3,000 FINE.
SO THEY NEVER REALLY WENT TO
JAIL FOR THAT.
AND THAT SITUATION CAUSED A LOT
OF DISAPPOINTMENT AND
FRUSTRATION WITHIN THE ASIAN
AMERICAN COMMUNITY, INITIALLY
WITHIN DETROIT AND THEN
NATIONALLY, AND IT WAS REALLY
THE BEGINNING OF WHAT I WOULD
CALL AN ASIAN AMERICAN ACTIVIST
MOVEMENT.
AND SEVERAL OF MY VERY CLOSE
FAMILY FRIENDS WERE INVOLVED IN
THIS.
AND WHAT I LEARNED FROM THAT IS
THAT YOU REALLY NEED TO SPEAK UP
INDIVIDUALLY AND AS A COMMUNITY
ABOUT THINGS LIKE THIS.
THE OTHER THING I NOTICED EVEN
AS A CHILD WAS THAT THERE WAS
VERY STRONG SUPPORT FROM THE
BLACK COMMUNITY, THE JEWISH
COMMUNITY, AND OTHER GROUPS FOR
THIS MOVEMENT BECAUSE THEY FELT
SOLIDARITY FROM THEIR OWN
HISTORICAL CHALLENGES.
AND I'LL COME BACK AND TALK A
LITTLE BIT MORE ABOUT THAT.
SO WITHIN THAT SETTING, THIS WAS
THE OTHER ELEMENT OF MY
CHILDHOOD.
AND I WOULD SAY THAT OVERALL, I
HAD A VERY GOOD -- ACTUALLY A
GREAT CHILDHOOD EXPERIENCE.
BUT ONE OF THE -- I ALWAYS GREW
UP PLAYING A LOT OF SPORTS, I
WAS THE LEAD IN THE SCHOOL
PRODUCTION OF MCBETH WHICH I
MENTIONED TO A FEW PEOPLE AT NEI
AND NOBODY BELIEVED ME, BUT IT
WAS REAL.
BUT AS A HIGH SCHOOL STUDENT, MY
DAD, TOWARD THE END OF HIS
CAREER, HAD A VERY HIGH ADD
ADMINISTRATIVE POSITION AT FORD,
AND HIS YOUNGER BROTHER, WHO'S
MY UNCLE, HAD ALSO A VERY HIGH
ADMINISTRATION POSITION AT ONE
OF THE SEMICONDUCTOR -- MAJOR
SEMICONDUCTOR COMPANIES.
AND THEY WERE BOTH TRYING TO
TELL ME, WHY DON'T YOU RUN FOR
CLASS PRESIDENT IN HIGH SCHOOL,
AND I WOULD ALWAYS RESPOND,
WELL, I DON'T WANT TO BECOME
CLASS PRESIDENT.
I JUST WANT TO DO THOSE THINGS
THAT INTEREST ME.
AND PLUS, I DON'T THINK I WOULD
EVER HAVE A CHANCE OF WINNING IN
THIS SETTING.
SO I NEVER REALLY SERIOUSLY
CONSIDERED DOING THAT.
BUT I'LL COME TALK A LITTLE BIT
MORE ABOUT THAT.
SO WITH THAT SAID, ABOUT MY
BACKGROUND, I EVENTUALLY WENT
TO -- EVENTUALLY BECAME AN
ENGINEER, BECAME A DOCTOR, AND
THEN WENT TO OPHTHALMOLOGY.
SO I WAS DOING OPHTHALMOLOGY
RESIDENCY AT JOHNS HOPKINS, AND
THIS IS 1998.
EVERY SINGLE DAY, WE WOULD HAVE
LECTURES.
SO ONE OF THE PEOPLE WOULD SPEAK
AND THIS HAPPENS TO BE
OCTOBER 1998.
AND YOU KNOW, SOMEBODY WOULD
COME IN AND SPEAK EITHER FROM
JOHNS HOPKINS OR FROM THE
COMMUNITY.
AND IT TURNED OUT THAT IN 1999,
WE HAD TWO GUEST SPEAKERS.
ONE OF THEM WAS EMILY CHU, WHO
WAS AT THE NEI AND I'M PROUD TO
SAY IS ONE OF MY COLLEAGUES NOW
AT THE NEI, AND THE SECOND GUEST
SPEAKER A FEW MONTHS LATER WAS
SOMEBODY NAMED PAUL LI, WHO WAS
THEN AT DUKE UNIVERSITY AND IS
NOW THE CHAIR OF OPHTHALMOLOGY
AT THE UNIVERSITY OF MICHIGAN.
BUT WHEN THOSE TWO SPOKE, IT
OCCURRED TO ME THAT I HAD NEVER
LISTENED TO A LECTURE FROM AN
ASIAN SPEAKER BEFORE IN ALL
THESE YEARS OF BEING AN
OPHTHALMOLOGY RESIDENT.
SO THAT REALLY STRUCK ME, THAT
I'D NEVER SEEN ANYBODY WHO
LOOKED LIKE ME AT THE PODIUM.
AND THEN IT TURNED OUT THAT TWO
YEARS LATER, WHEN I GOT MY FIRST
JOB, MY FIRST CHAIR WAS A
RETINAL SPECIALIST NAMED STANLEY
CHANG AT COLUMBIA UNIVERSITY.
AND IT REALLY GAVE ME AN
INTERESTING PERSPECTIVE TO HAVE
A CHAIRMAN WHO WAS ASIAN
AMERICAN.
AND I'LL BUILD ON SOME OF THAT
AFTERWARDS.
BUT IT WAS REALLY IN THIS
SETTING THAT I STARTED MY
CAREER.
AND AS DR. GOTTESMAN SAID, YOU
KNOW, I STARTED MY CAREER
STUDYING A DISEASE CALLED
RETINOPATHY OF PREMATURITY.
I WANT TO TALK A LITTLE BIT
ABOUT THAT SCIENCE AND HOW THIS
LEADS INTO ARTIFICIAL
INTELLIGENCE AND SOME OF THE
THINGS THAT WE DID IN A.I. FOR
OPHTHALMOLOGY, BUT WHAT I WANT
TO TRY TO DO IS TO KEEP THE
GENERAL PRINCIPLES EVIDENT, SO
ASSUMING THAT YOU'RE NOT
INTERESTED IN THIS PARTICULAR
DISEASE, THAT I'M HOPING TO MAKE
SOME PRINCIPLES COME OUT THAT
ARE REALLY GOING TO APPLY TO
ANYBODY WHO'S DOING SCIENCE OR
CLINICAL CARE.
THIS PARTICULAR DISEASE, WHY
DOES IT MATTER, WELL, IT'S ONE
OF THE LEADING CAUSES OF
CHILDHOOD BLINDNESS AROUND THE
WORLD.
STEVIE WONDER IS SOMEBODY WHO
WENT BLIND FROM RETINOPATHY OF
PREMATURITY IN THE 1950s
BEFORE THERE WAS A TREATMENT.
NOW ALMOST ALL THE CASES OF
BLINDNESS ARE PREVENTABLE IF
THEY'RE DIAGNOSED EARLY AND
TREATED APPROPRIATELY.
THERE HAVE BEEN TENS OF MILLIONS
OF DOLLARS OF NIH FUNDED
CLINICAL TRIALS THAT HAVE GONE
INTO DEMONSTRATING THIS.
THE PROBLEM IS THAT WE'RE NOT
VERY GOOD AT DIAGNOSING
RETINOPATHY OF PREMATURITY.
THE WAY THAT YOU DIAGNOSE SEVERE
DISEASE IS THAT YOU LOOK AT THE
BLOOD VESSELS AND IF THEY'RE
DILATED, THICKER AND TORE TEU ,
WIGGLY, THAT'S SOMETHING CALLED
PLUS DISEASE AND THAT'S BAD.
ACCORDING TO THESE TENS OF
MILLIONS OF DOLLARS OF NIH
TRIALS, IN YOU SEE PLUS DISEASE
IN BABIES AT HIGH RISK OF GOING
BLIND, YOU NEED TO TREAT THAT
BABY WITH LASER OR SOME OTHER
TYPE OF DRUG INJECTION TO
PREVENT THAT BLINDNESS AND YOU
NEED TO DO IT REALLY QUICKLY.
IN 2007 WE DID WHAT I THOUGHT
WAS AN EMBARRASSINGLY BASIC
STUDY WHERE WE PUT SOME PICTURES
UP ON THE WEB AND WE ASKED
21 WORLD EXPERTS TO GO IN, LOG
IN AND YOU DIAGNOSE EACH IMAGE
AS PLUS, PRE-PLUS OR NOT PLUS.
IN OTHER WORDS, BAD,
INTERMEDIATE OR NORMAL.
AND HERE'S AN EXAMPLE.
50% CALLED THIS ONE PLUS, 50%
CALLED IT NOT PLUS.
WE HAD IMAGES THAT SPLIT 50/50,
60/40.
OBVIOUSLY THIS IS A AN INTUITIVE
PROBLEM IF THIS IS SUCH A BAD
DISEASE AND CAUSES BABIES TO GO
BLIND, WHY ARE WORLD EXPERTS
SPLITTING 50/50 IN TERMS OF HOW
THEY DIAGNOSE, SO DISAGREEMENT
IN DIAGNOSIS.
I WOULD ARGUE WE SEE THIS IN
EVERY AREA OF MEDICINE.
NOW, I WANT TO TALK ABOUT
DISAGREEMENT IN PROCESS AS WELL,
THE DIAGNOSTIC PROCESS.
THIS IS GOING TO LEAD INTO WHAT
I CALL THE SCIENCE AND THE ART
OF MEDICAL CARE.
SO THREE YEARS AFTER THE
PREVIOUS SLIDE, I WAS ON A PANEL
AT ONE OF THE NATIONAL MEETINGS
AND WE WERE TALKING ABOUT PLUS
DISEASE AND HOW DO YOU DIAGNOSE
IT.
ONE OF THE EXPERT PANELISTS MADE
THE ANALOGY TO A SUPREME COURT
CASE IN THE 1960s.
THERE WAS A SUPREME COURT
JUSTICE, POTTER STEWART, AND
THEY WERE ARGUING A CASE ON
PORNOGRAPHY, AND POTTER STEWART
SAID, WELL, I CAN'T DEFINE
PORNOGRAPHY, BUT I KNOW IT WHEN
I SEE IT BECAUSE IT JUST LOOKS
BAD.
SO THAT EXPERT PANELIST'S
ANALOGY WAS THAT PLUS DISEASE IS
LIKE PORNOGRAPHY, THAT IT'S
REALLY, REALLY HARD TO
CHARACTERIZE WHAT IT MEANS TO
HAVE PLUS DISEASE, BUT YOU KNOW
IT WHEN YOU SEE IT BECAUSE IT
JUST LOOKS BAD.
THAT COMMENT BOTHERED ME FOR
MONTHS BECAUSE I THOUGHT, WELL,
WE'RE TRYING TO BE SCIENTIFIC
ABOUT THINGS, HOW CAN WE JUST BE
SAYING THAT THINGS JUST LOOK
BAD?
ON THE OTHER HAND, EVERY
CLINICIAN WILL TELL YOU THAT
SOMETIMES THINGS JUST LOOK BAD.
SOMETIMES THE RETINA LOOKS
AGGRESSIVE, IF AN ICU DOCTOR --
SOMETIMES THAT PATIENT JUST
LOOKS SICK IN A WAY THAT EXPERTS
CAN'T ALWAYS ARTICULATE.
SO WE BECAME INTERESTED IN THIS
QUESTION, COULD VARIABILITY IN
THE DIAGNOSTIC PROCESS, IN OTHER
WORDS, IF DIFFERENT PEOPLE HAVE
DIFFERENT CONCEPTS OF WHAT IT
MEANS TO JUST LOOK BAD, COULD
THAT EXPLAIN SOME OF THE
VARIABILITY THAT WE'RE SEEING ON
THE PREVIOUS SLIDE.
SO WE DESIGNED A SET OF STUDIES
TO TRY TO CAPTURE AND ENCODE THE
DETAILED QUALITATIVE THOUGHTS OF
THE SEVEN MOST SEASONED ROP
EXPERTS IN THE WORLD.
THERE WERE PEOPLE WHO ORIGINALLY
CHARACTERIZED THE DISEASE AND WE
THOUGHT LET'S VIDEOTAPE THEM
WHEN THEY'RE MAKING THESE
DIAGNOSES.
WE SPENT MONTHS PICKING IMAGES,
SCRIPTING QUESTIONS, AND WE HAD
THEM ANNOTATE WITH A PEN THE
AREAS ON THE IMAGES THAT EITHER
WORRIED THEM OR REASSURED THEM,
AND WE THEN HAD A COGNITIVE
PSYCHOLOGIST GO BACK AND ANALYZE
HOURS OF VIDEOTAPE TRANSCRIPT TO
TRY TO MODEL THEIR THOUGHT
PROCESS.
SO HERE'S AN EXAMPLE.
THERE'S ONE IMAGE, AND THREE
EXPERTS.
EXPERT NUMBER ONE DIAGNOSED AS
PLUS DISEASE, EXPERT NUMBER TWO
DIAGNOSED IT AS PRE-PLUS
DISEASE, EXPERT NUMBER THREE
DIAGNOSED IT AS NORMAL, AND
THEY'RE ALL LOOKING AT DIFFERENT
PARTS OF THE RETINA WHEN THEY DO
THAT.
SO THE DIFFERENCE IN THE DYING
DIAGNOSTIC PROCESS AS WELL AS
THE OVERALL DIAGNOSIS.
AND MINA HEWING WAS A VISITING
RESEARCHER FROM GERMANY WHO
WROTE A REALLY NEAT PAPER ON
THIS.
WHEN WE LOOKED BACK AT THE
LITERATURE, THERE'S REALLY GOOD
EVIDENCE THIS HAPPENS IN EVERY
AREA OF MEDICINE.
DIFFERENCE IN THE DIAGNOSTIC
PROCESS AS WELL AS THE OVERALL
DIAGNOSIS.
NOW, IN FACT, WHEN YOU GET
DIFFERENT EXPERTS, THIS IS EIGHT
WORLD EXPERTS, TO LOOK AT
DIFFERENT IMAGES, THIS IS 100
DIFFERENT RETINAL IMAGES, THESE
COLUMBUS, A LOT OF INTERESTING
THINGS HAPPEN.
PETE CAMPBELL WAS A JUNIOR
COLLEAGUE IN OREGON, AND KRAMER
IS A LONG TIME COLLABORATOR.
WE WORKED TOGETHER FOR 10 YEARS
AND SHE SPOKE AT NIH VIRTUALLY
JUST A WEEK AGO ON ARTIFICIAL
INTELLIGENCE.
AND THEY WROTE A REALLY NICE
SERIES OF PAPERS ON THIS.
IF THAT EXPERT DIAGNOSED THAT
IMAGE AS PLUS DISEASE, IN OTHER
WORDS, REALLY SEVERE, THE BOX IS
IN RED.
IF THEY DIAGNOSE PRE-PLUS
DISEASE, INTERMEDIATE, THE BOX
IS YELLOW.
IF THEY DIAGNOSE IT AS NORMAL,
THE BOX IS IN GREEN.
SO THE FIRST THING TO NOTICE
HERE IS EXPERTS 7 AND 8, ALL
WORLD EXPERTS, DIAGNOSE PLUS
DISEASE SIX TIMES MORE OFTEN
THAN EXPERT NUMBER ONE.
IF YOU GO TO A DIRN DIFFERENT
DATASET, 34 IMAGES, IT'S THAT
EXACT SAME 6:1 RATIO.
SO YOU'VE GOT OVER AND
UNDERCALLERS.
WE SEE THIS IN EVERY FIELD OF
MEDICINE, THERE ARE SOME DOCTORS
THAT ARE MORE AGGRESSIVE AND
ALWAYS RECOMMEND SURGERY AND
THERE ARE OTHERS THAT WILL
OBSERVE YOU AND RECOMMEND
PHYSICAL THERAPY.
SO THIS IS WHAT IT LOOKS LIKE ON
A GRID.
ON THE OTHER HAND, IF YOU GO
DOWN EACH COLUMN AND CALCULATE
AN AVERAGE SCORE FOR EACH IMAGE,
SO IF YOU GIVE IT ONE POINT FOR
A NORMAL DIAGNOSIS, TWO POINTS
FOR A PRE-PLUS DISEASE AND THREE
POINTS FOR A PLUS DISEASE
DIAGNOSIS AND CALCULATE TO AN
AVERAGE SCORE AND CONVERT TO A
COLOR, THAT'S THIS ROW, THE
AVERAGE SCORE.
SO ON THE RIGHT SIDE, THESE
IMAGES ARE VERY GREEN BECAUSE
EVERYBODY DIAGNOSED NORMAL, ON
THE LEFT SIDE THEY'RE VERY RED
BECAUSE EVERYONE DIAGNOSED PLUS.
HERE IN THE MIDDLE, THERE'S
EVERY SHADE IN BETWEEN.
SO THE POINT I'M TRYING TO MAKE
WITH THIS IS THAT THE REAL WORLD
IS A CONTINUOUS SPEC SPECTRUF
ABNORMALITY.
EVIDENCE-BASED MEDICINE MAKES IT
SOUND LIKE PEOPLE COME IN
BUCKETS.
NORMAL, MILD, MODERATE, SEVERE,
AND PEOPLE DO NOT COME IN
BUCKETS.
THEY COME ON A CONTINUOUS
SPECTRUM.
AND IT'S A CLINICIAN'S JOB TO
DRAW THE LINES AND PUT PEOPLE
INTO BUCKETS.
BUT THE PROBLEM IS THAT PEOPLE
DON'T DO THAT CONSISTENTLY.
SOME PEOPLE DRAW THOSE LINES
HERE, SOME PEOPLE DRAW THEM
HERE, AND THAT'S REALLY ONE OF
THE AREAS WHERE ARTIFICIAL
INTELLIGENCE CAN HELP US IMPROVE
DIAGNOSTIC ACCURACY AND
CONSISTENCY.
THERE'S BEEN ENORMOUS AMOUNT OF
WORK IN ARTIFICIAL INTELLIGENCE
FOR OPHTHALMOLOGY, AND I
CAPTURED THIS TWEET FROM ERIC
TOPOL WITH TWO YEARS AGO, OF
ALL THE MEDICAL SPECIALTIES,
MOST PEOPLE THINK RADIOLOGY IS
LEADING THE A.I. MOVEMENT BUT
IT'S ACTUALLY OPHTHALMOLOGY.
WHY IS THAT?
BECAUSE THERE'S SO MUCH
IMAGE-BASED TATA DATA AND SOH
ACCOMPANIES CLINICAL DATA.
FOR EXAMPLE, THE FIRST
FDA-APPROVED AUTONOMOUS
ARTIFICIAL INTELLIGENCE SYSTEM
IN ANY FIELD OF MEDICINE WAS
DEVELOPED BY MICHAEL ABRAMOFF AT
UNIVERSITY OF IOWA AND IT
DIAGNOSES DIABETIC RETINOPATHY.
THE FOLKS AT GOOGLE HAVE DONE
SOME BEAUTIFUL WORK INVOLVING
KNOWLEDGE DISCOVERY REGARDING
SYSTEMIC HEALTH, SO THEY WERE
ABLE TO TRAIN DEEP LEARNING
SYSTEMS THAT, FOR EXAMPLE, TOOK
RETINAL IMAGES AND WERE ABLE TO
TELL DID THAT IMAGE COME FROM A
MALE PATIENT OR FEMALE PATIENT.
THEY CAN DO THIS WITH ALMOST
100% ACCURACY.
WHEN THAT PAPER WAS PUBLISHED
FOUR YEARS AGO, IT WAS STUNNING
TO THE MEDICAL COMMUNITY BECAUSE
NONE OF US CAN LOOK AT AN IMAGE
LIKE THIS AND TELL, IS IT MALE
OR FEMALE, BUT PRESUMABLY
THERE'S SOME DATA IN THE PIXELS
THAT ASSOCIATES WITH GENDER.
SO MAYBE SOMEDAY WE CAN TAKE
ADVANTAGE OF THAT FOREKNOWLEDGE
DISCOVERY.
AND IN MY PIECE OF THE WORLD, WE
WORKED ON SYSTEMS -- ARTIFICIAL
INTELLIGENCE SYSTEMS FOR PLUS
DISEASE DIAGNOSIS IN RETINOPATHY
OF PREMATURITY.
AND THAT WAS WITH THREE
INVESTIGATORS.
IN A NUTSHELL, HOW WE TRIED TO
APPROACH THIS IS A TWO-LEVEL
CONVOLUTIONAL NEURAL NETWORK.
THE FIRST LEVEL WAS SEGMENTING
THE IMAGE.
IN OTHER WORDS, GOING FROM THAT
IMAGE TO IDENTIFY WHAT THE
VESSELS ARE.
A VESSEL MAP.
NOW WHY DO WE WANT TO DO THIS?
AT THE TIME, THIS WAS 2016,
2017, WE WERE CONCERNED THAT
THERE ARE DARKLY PIGMENTED
PATIENTS AND LIGHTLY PIGMENTED
PATIENTS, AND WE DIDN'T WANT THE
A.I. SYSTEMS TO LEARN RACE AS
PART OF THE DIAGNOSIS.
SO WE THOUGHT, LET'S JUST
CALCULATE THE VESSEL MAPS.
AND THEN WE'LL GO FROM THE
VESSEL MAPS TO A SECOND
CONVOLUTIONAL NEURAL NETWORK
THAT WILL THEN MAP TO A FUTURE
SPACE AND COME UP WITH WHAT'S
YOUR PROBABILITY OF PLUS VERSUS
PRIEST-PLUS VERSUS NORMAL?
IN FACT WHEN DO YOU THAT, OUR
SYSTEMS HAD VERY, VERY HIGH
ACCURACY FOR DIAGNOSING, IN FACT
IT COMPARED TO THE EXPERTS THAT
WE TESTED, IT BEAT ALL OF THE
EXPERTS EXCEPT FOR ONE.
SO WHERE ARE WE NOW?
WE HAVE FDA BREAKTHROUGH
DESIGNATION SO THERE'S NOT A
PRODUCT ABOUT THIS, BUT HAVING
THOSE DISCUSSIONS.
AND I DON'T HAVE ANY FINANCIAL
INTEREST IN THIS, AND IF I DID,
ROP WOULD NOT THE BE THE DISEASE
TO BE MAKING MONEY OFF.
SO ANYWAY, ONE OF THE QUESTIONS
THAT PEOPLE ALWAYS ASK IS, WHAT
ABOUT IMAGE QUALITY?
THE ENGINEERS WILL SAY, WELL,
GARBAGE IN, GARBAGE OUT, AND HOW
DO YOU KNOW YOU'VE GOT A GOOD
ENOUGH IMAGE TO MAKE A
DIAGNOSIS?
ONE OF THE PH.D. STUDENTS THAT I
WORKED WITH IN OREGON, AARON
COYNER, WORKED ON A SET OF
PROJECTS INVOLVING THAT
QUESTION.
THIS QUESTION COMES UP ALL THE
TIME IN MEDICAL DIAGNOSIS.
AND SO WE THOUGHT, LET'S DEVELOP
A CONVOLUTIONAL NETWORK TO TRAIN
BASED ON QUALITY OF IMAGES.
SO BASICALLY WE HAD THOUSANDS OF
IMAGES THAT WERE LABELED BY
EXPERTS, IS THAT IMAGE
ACCEPTABLE FOR DIAGNOSIS,
POSSIBLY ACCEPTABLE OR NOT
ACCEPTABLE?
WE USE THAT AS A BASIS FOR
TRAINING AND VALIDATING THESE
NEURAL NETWORKS.
SO IT TURNED OUT WHEN YOU DO
THAT, THEY HAD VERY HIGH
ACCURACY COMPARED TO EXPERTS FOR
IDENTIFYING IMAGE QUALITY.
AND IN FACT, WHEN WE HAD THAT
A.I. SYSTEM BASICALLY RANK 30
IMAGES FROM LOWEST QUALITY TO
HIGHEST QUALITY, IT ACTUALLY
CORRELATED FAIRLY WELL WITH WHAT
EXPERTS ORDERED IN TERMS OF
QUALITY, THE CORRELATION
COEFFICIENT WAS ABOUT .9.
SO RIGHT NOW WHERE ARE WE WITH
THAT, TRYING TO INCORPORATE INTO
OPERATIONAL A.I. SYSTEM.
SO A LOT OF WORK TO BE DONE.
SO LASTLY I JUST WANT TO SAY
THAT WE TALKED ABOUT CLINICAL
DIAGNOSIS BEING SUBJECTIVE AND
QUALITATIVE IN ROP AND REALLY SO
MANY OTHER AREAS IN
OPHTHALMOLOGY AND MEDICINE, AND
THE QUESTION IS, CAN WE MAKE IT
MORE QUANTITATIVE AND OBJECTIVE.
HERE'S ONE OF OUR ATTEMPTS TO DO
THAT.
WE TAKE THE OUTPUTS OF THOSE
A.I. SYSTEMS AND WE CONVERT TO A
1 TO 9 SCALE.
SO 1 IS MOST NORMAL, 9 IS MOST
SEVERE.
AND WHO DID THE WORK?
CAMPBELL, KIM, VISITING
RESEARCHER FROM KOREA, PAUL
CHAN, AND KRAMER DID THE BULK OF
THIS.
SO YB DO WE WHY DO WE WANT?
FROM A CLINICAL PERSPECTIVE WE
TALKED ABOUT THIS CONTINUOUS
SPECTRUM OF ABNORMALITY AND
THOUGHT IF WE GIVE NINE LEVELS,
THAT WILL MIMIC THAT SPECTRUM.
ON THE OTHER HAND, WHAT A REAL
DOCTOR WANTS TO KNOW AT THE
BEDSIDE IS, IS MY PATIENT
GETTING BETTER, WORSE OR ABOUT
THE SAME COMPARED TO THE LAST
EXAM?
YOU CAN'T DO THAT WITH THE
CURRENT THREE-LEVEL SCALES,
PLUS, PRE-PLUS, NORMAL.
IT'S JUST NOT GRANULAR ENOUGH.
SO NINE LEVELS WE THOUGHT WOULD
BE A REAL WAY TO BE ABLE TO
COMPARE.
NOW FROM A PRACTICAL
PERSPECTIVE, ONE THING THAT I'VE
HEARD A LOT IN GIVING TALKS IS,
A DOCTOR WILL SAY, WELL, AM I AT
MEDICAL-LEGAL LIABILITY IF A
COMPUTER TELLS ME THAT MY BABY
HAS PLUS DISEASE BUT I DECIDE
NOT TO TREAT THE BABY, WHAT IF
SOMETHING BAD HAPPENS AND AM I
GOING TO GET SUED BECAUSE OF
THAT?
AND I DON'T NECESSARILY BELIEVE
THAT.
BUT I'VE HEARD IT SO MANY TIMES.
THE RATIONALE HERE IS THAT FROM
A PRACTICAL PERSPECTIVE, THE
MACHINE WOULD THEN GIVE THE
DOCTOR A NUMBER, JUST LIKE ANY
OTHER NUMBER.
BLOOD PRESSURE, ANY OTHER LAB
TEST, AND THE CLINICIAN WOULD
THEN DECIDE BASED ON THAT NUMBER
WHAT TO DO WITH IT.
AND IN FACT, THERE MAY BE
NUANCES THAT IF YOU GO FROM A
5 TO A 6, IT MAY NOT BE AS BAD
AS IF YOU GO FROM A 2 TO A
6 DURING THE SAME TIME PERIOD.
SO THERE MAY BE JUST INFORMATION
THAT YOU COULD THEN GIVE TO THE
DOCTOR TO MAKE THAT DIAGNOSIS
AND MAKE THAT MANAGEMENT PLAN.
SO I THINK THAT WE'RE REALLY
MOVING FROM A PARADIGM IN ALL OF
MEDICAL CARE FROM QUALITATIVE TO
QUANTITATIVE, AND WE'LL COME
BACK AND TALK MORE ABOUT THAT.
SO I'M GOING TO STOP THIS FIRST
SECTION OF THE TALK BY SAYING
JUST WHAT HAVE WE LEARNED ABOUT
THIS PARTICULAR DISEASE.
WELL, I WOULD ARGUE THAT THERE'S
SCIENCE, THERE'S ART AND THERE'S
TECHNOLOGY.
IN TERMS OF THE SCIENCE, ALL OF
THESE NIH FUNDED CLINICAL
TRIALS, THEY'VE REVOLUTIONIZED
CLINICAL CARE IN ROP BUT THEY
ARE NOT PERFECT.
FOR EXAMPLE, PLUS DISEASE
DIAGNOSIS, WHICH IS THE BASIS OF
THOSE TRIALS, IS SUBJECTIVE AND
QUALITATIVE.
THAT REALLY GETS INTO THE ART OF
MEDICINE.
THAT EVIDENCE-BASED PRACTICE
CANNOT CAPTURE ALL THAT
QUALITATIVE NUANCE OF DISEASE.
IN OTHER WORDS, WHAT IT MEANS TO
JUST LOOK BAD.
AND I THINK THAT TAKES US INTO
THE TECHNOLOGY, AND IN THIS
CASE, ARTIFICIAL INTELLIGENCE
METHODS CAN HELPFULLY HOPE
CLINICIANS ONE DAY TO IMPROVE
OBJECTIVITY OF DIAGNOSIS AND
MOVE FROM QUALITATIVE TO
QUANTITY ITIVE.
I HOPE THAT'S A GOOD PARADIGM
FOR ALL OF US AT NIH, THAT WE'RE
TRYING TO DO THINGS THAT ARE
GOING TO IMPROVE HUMAN HEALTH,
AND GOOD MEDICAL CARE IS GOING
TO REQUIRE ALL THREE OF THOSE
THINGS: SCIENCE, ART AND
TECHNOLOGY.
AND I HOPE THAT'S SOMETHING THAT
WE CAN JUST KEEP IN MIND IN
EVERYTHING THAT HE WITH WE .
WITH SO WITH THAT SAID, I WANT
TO MOVE INTO TALKING A LITTLE
BIT ABOUT WHAT'S MISSING, IN
OTHER WORDS, WHY HASN'T A.I.
REVOLUTIONIZED MEDICAL CARE.
I WOULD JUST TRY TO SUMMARIZE
SOME OF THE FEW UNANSWERED
RESEARCH QUESTIONS.
THIS ISN'T ALL OF THE UNANSWERED
QUESTIONS BUT IT'S JUST A FEW
THAT DOVETAIL WITH WHAT WE'VE
TALKED ABOUT UP UNTIL NOW.
ONE OF THEM IS THAT I WOULD SAY
THE PERFORMANCE OF AI SYSTEMS IN
OPHTHALMOLOGY, RADIOLOGY,
EVERYTHING ELSE IMPROVES AS YOUR
TASK NARROWS.
FOR EXAMPLE, IF YOU ASK A
SYSTEM, RULE OUT PLUS DISEASE IN
RETINOPATHY OF PREMATURITY,
THEY'RE REALLY, REALLY GOOD.
BUT THE PROBLEM IS THAT CLINICAL
DIAGNOSIS ISN'T JUST INDIVIDUAL
DISCRETE QUESTIONS LIKE THIS.
CLINICAL DIAGNOSIS INVOLVES
NUMEROUS PARALLEL TASKS.
PATIENTS HAVE MULTIPLE DISEASES,
MULTIPLE FINDINGS, AND YOU
TYPICALLY NEED TO INTEGRATE
IMAGE DATA TOGETHER WITH
CLINICAL DATA, AND SO IT'S NOT
ENOUGH JUST TO BE ASKING THESE
NARROW QUESTIONS, WE'VE GOT TO
FIND WAYS TO INTEGRATE THESE
DIAGNOSTIC SYSTEMS TOGETHER, AND
ALSO COME UP WITH HOW ARE THESE
SYSTEMS REALLY GOING TO HELP
DOCTORS WITH THE INFORMATION
THEY NEED.
SO BIG CHALLENGES IN THIS AREA.
ANOTHER CHALLENGE IS WHAT I
WOULD CALL GENERALIZABILITY AND
BIAS.
MANY OF THESE STUDIES ARE DONE
IN ACADEMIC CENTERS.
THE POPULATIONS ARE OFTEN
RELATIVELY HOMOGENEOUS.
I USED TO WORK IN PORTLAND,
OREGON WHERE THE POPULATION IS
JUST NOT THAT DIVERSE.
THE POINT OBVIOUSLY IS THAT THE
REAL WORLD IS HETEROGENEOUS.
THERE'S DIFFERENT RACES,
DIFFERENT POPULATIONS, DIFFERENT
IMAGING DEVICES.
SO IN GENERAL, YOU CANNOT TAKE
RESULTS FROM ONE MACHINE AND
THEN JUST EXTRAPOLATE TO ANOTHER
MACHINE OR TAKE ONE POPULATION
AND EXTRAPOLATE THOSE TO ANOTHER
POPULATION.
ON TOP OF THAT, REAL WORLD DATA
IS OFTEN MUCH POORER QUALITY
THAN DATA THAT COMES IN CLINICAL
RESEARCH.
IT'S SUBJECT TO BIASES IN DATA
COLLECTION.
IN FACT, THERE'S A PAPER
PUBLISHED THAT ONLY 6% OF DEEP
LEARNING STUDIES HAD ANY
EXTERNAL VALIDATION.
AND WE ALL SAW THIS IN THE COVID
ERA, WHERE ARTIFICIAL
INTELLIGENCE WAS SUPPOSED TO
SOLVE ALL OF OUR PROBLEMS WITH
COVID DIAGNOSIS AND PROGNOSIS,
AND IT REALLY DIDN'T.
SO HOW DO WE APPROACH THIS?
LARGER DATASETS, MORE DATASETS
AND VALIDATION ACROSS
POPULATIONS AND DEVICES, DATA
SHARING.
LASTLY, UNCLEAR GROUND TRUTH.
WE TALKED ABOUT THIS A LOT WITH
PLUS DISEASE DIAGNOSIS AND IT'S
TRUE FOR EVERY AREA IN MEDICINE.
THERE'S A SCIENCE AND THERE'S AN
ART.
AND THERE'S DIAGNOSTIC
VARIABILITY.
SOMETIMES THERE'S JUST GREY
ZONES, LIKE THE LINE BETWEEN
MODERATE DISEASE AND MILD
DISEASE CAN JUST BE A LITTLE BIT
BLURRY SOMETIMES.
SO ON TOP OF THAT, THERE ARE AFN
VERY
OFTEN VERY UNCLEAR DEFINITIONS
OF WHAT I WOULD CALL WHAT DOES
IT MEAN TO HAVE MODERATE
DISEASE, SEVERE DISEASE,
REGARDLESS OF WHAT DISEASE IT
IS, AND THAT GETS INTO THAT
WHOLE ISSUE OF DYING DIAGNOSC
PROCESS.
DIFFERENT PEOPLE MAY HAVE
DIFFERENT DIAGNOSTIC PROCESSES.
SO I THINK THERE'S A LOT OF WORK
THAT COMMUNITY NEEDS TO DO IN
TERMS OF CONSENSUS DEFINITIONS
FOR DISEASE, AND FOR EXAMPLE,
HOW ARE WE GOING TO CROWD SOURCE
DIFFERENT GRADINGS IF YOU NEED
TO HAVE MULTIPLE PEOPLE MAKING
DIAGNOSES TO CREATE THAT GROUND
TRUTH.
SO A LOT OF DIFFERENT
CHALLENGES.
WITH THOSE CHALLENGES I WANT TO
SPEND THE NEXT FEW MINUTES
TALKING ABOUT WHAT ARE WE DOING
AT THE NATIONAL EYE INSTITUTE TO
ADDRESS THOSE CHALLENGES AND
WHAT ARE WE DOING NIH WIDE IN
SOME OF THOSE AREAS.
SO I THINK ONE OF THE THINGS
THAT WE NEED IS WHAT I WOULD
CALL DATA SHARING AND
HARMONIZATION.
AND THE PREMISE FOR THIS IS THAT
MODERN RESEARCH RELIES ON BIG
DATA.
IT DOESN'T MATTER IF YOU'RE
DOING ARTIFICIAL INTELLIGENCE
RESEARCH, MECHANISTIC RESEARCH
INVOLVING GENETICS AND OMICS AND
DISEASE MECHANISMS.
YOU KNOW, I THINK THAT YOU NEED
LARGE DATASETS AND THAT'S GOING
TO REQUIRE DATA SHARING.
WE'VE GOT A REALLY, REALLY
COMMON PARADIGM IN THIS COUNTRY
WHERE THERE ARE MULTIPLE CENTERS
AROUND THE COUNTRY WHERE PEOPLE
ARE WORKING ON THE EXACT SAME
PROBLEM BUT THOSE STUDIES ARE
ALL INDIVIDUALLY UNDERPOWERED TO
TRULY ANSWER THAT QUESTION AND
MOVE THE FIELD FORWARD.
IN FACT, NUMBER ONE, THOSE
INVESTIGATORS DON'T ALWAYS FEEL
INCENTIVIZED TO SHARE DATA, IN
FACT, THEY MAY FEEL
DISINCENTIVIZED TO SHARE DATA.
BUT EVEN IF THEY WANTED TO
SHARE, IN GENERAL, THEY CAN'T
JUST SHARE OUTSIDE THE BOX,
BECAUSE THOSE DATA WERE ALMOST
ALWAYS COLLECTED DIFFERENTLY AND
USING DIFFERENT DATA
REPRESENTATIONS.
SO I THINK THAT WE'RE LOSING A
LOT OF OPPORTUNITIES TO ADVANCE
THE FIELD BECAUSE OF LACK OF
DATA SHARING AND LACK OF
HARMONIZATION.
NOW, AT THE NIH, ALMOST ALL OF
YOU KNOW THAT WE'VE GOT A DATA
SHARING POLICY THAT'S GOING TO
GO INTO EFFECT NEXT YEAR AND
THAT'S A STICK.
WE'RE NOT GOING TO FUND YOUR
RESEARCH UNLESS YOU SHARE DATA.
I AND MANY PEOPLE AT NIH BELIEVE
WE REALLY NEED INCENTIVES FOR
DATA SHARING TO TRULY MOVE THE
FIELD FORWARD.
ONE OF THE THINGS WE DID IN MY
PART OF THE WORLD IS THAT I
WORKED WITH SOMEONE, MARCO
ZARBAN, EDITOR-IN-CHIEF OF ONE
OF THE MAJOR VISION JOURNALS
TOGETHER WITH TWO PEOPLE ON THE
EDITORIAL BOARD, TO CREATE A NEW
PUBLICATION TYPE CALLED A
DATASET/SOFTWARE LIBRARY.
AND THE POINT IS THAT YOU CAN
WRITE A PAPER THAT'S PURELY
BASED ON YOUR DATASET, AND
HOPEFULLY THAT WOULD BE ONE OF
THOSE THINGS THAT COULD
INCENTIVIZE DATA SHARING AND
TEAM SCIENCE WITHIN THE VISION
COMMUNITY.
SO IN OTHER WORDS, PEOPLE WOULD
GET ACADEMIC CREDIT, THEIR
DATASET WOULD BE CITED AND IT
WOULD BE FINDABLE USING PUBMED
SEARCH.
SO I THINK WE'RE GOING TO NEED
MORE OF THOSE CARROTS.
ALONG THE LINES OF DATA
HARMONIZATION, THINK WE NEED A
COMMON DATA MODEL FOR
OPHTHALMOLOGY, THAT'S PRETTY
CLEAR.
AND WE AT THE NEI HAVE STARTED
WORKING WITH THE AMERICAN
ACADEMY OF OPHTHALMOLOGY TO TRY
TO DEVELOP THAT DATA MODEL.
AND THERE'S SOME FOLKS HERE WHO
ARE DOING THE WORK, SALLY BAXTER
FROM UC SAN DIEGO, KERRY GETS GZ
AND A WHOLE OTHER GROUP OF
PEOPLE WHO'S WORKING ON THIS.
SO DATA SHARING AND HARM
?IIZATION, I THINK REALLY,
REALLY IMPORTANT FOR THE FUTURE
OF CLINICAL RESEARCH AND JUST
RESEARCH IN GENERAL.
ANOTHER THING THAT I THINK WE'RE
REALLY GOING TO NEED IS IMAGING,
IMAGE STANDARDS.
NOW, IMAGING WE TALKED ABOUT AS
AVAILABILITY OF THESE IMAGES IS
ONE REASON THERE'S BEEN SUCH A
REVOLUTION IN A.I. AND VISION
SCIENCE.
THESE IMAGES COME FROM MACHINES
LIKE THIS.
AND THEY GENERATE PICTURES AND
NUMBERS.
THIS IS A GOLD MINE FOR
ARTIFICIAL INTELLIGENCE AND
OTHER SORT OF CLINICAL RESEARCH
IN GENERAL.
THE PROBLEM IS THAT IN GENERAL,
YOU CANNOT GET DATA OUT OF THESE
MACHINES EASILY AT ALL, OR AT
ALL BECAUSE THEY'RE LOCKED IN
PROPRIETARY STANDARDS THAT ARE
DEFINED AND REALLY OWNED BY THE
VENDORS.
SO THIS IS A HUGE PROBLEM.
IT'S A HUGE PROBLEM FOR RESEARCH
AND IT'S A HUGE PROBLEM FOR
CLINICAL CARE.
WHEN I WAS IN OREGON, I WORKED
WITH A PH.D. STUDENT WHO WANTED
TO DO MACHINE LEARNING FOR HIS
PH.D. THESIS, AND THE MACHINE
LEARNING DATA WAS GOING TO COME
OUT OF MACHINES LIKE THIS.
HE SPENT A YEAR, COULDN'T GET
ACCESS TO THE DATA, AND SO JUST
MOVED ON TO SOMETHING ELSE.
SO BIG, BIG PROBLEM.
NOW, I'VE REALLY BEEN TRYING TO
SOLVE THIS PROBLEM, AND YOU
KNOW, WE'VE WORKED OVER THE PAST
NINE MONTHS WITH A GROUP FROM
THE ONC, THE OFFICE OF NATIONAL
COORDINATOR FOR HEALTH I.T., AND
THAT'S LED BY MICKEY TROPOTHY.
WHY THE ONC?
THEY HAVE PURVIEW OVER
CERTIFYING DATA EXCHANGE.
WE'VE ALSO WORKED WITH THE FDA,
MEL VENA EDELMAN, AND WHY THE
FDA, BECAUSE THEY REGULATE
DEVICES, WHY US, BECAUSE WE
REGULATE GRANTS BASICALLY.
SO WHAT WE'VE BEEN TRYING TO GO
THROUGH IS WHAT CAN WE DO AND
WHAT ARE OUR LEVERS IN THE
FEDERAL GOVERNMENT ABOUT HOW WE
CAN MOVE THE FIELD FORWARD.
AND IT MAY BE A COMBINATION OF
STICKS AND CARROTS.
WE HAD A WORKSHOP ABOUT TWO
WEEKS AGO ABOUT THIS, AND IT WAS
REALLY, REALLY PRODUCTIVE, AND
HOPEFULLY WE'LL LEARN SOMETHING
THAT WILL HELP OUR COMMUNITY IN
VISION RESEARCH AS WELL AS OTHER
COMMUNITIES, BECAUSE THERE ARE
MANY, MANY COMMUNITIES AT NIH
AND AROUND THE COUNTRY THAT DEAL
WITH THIS EXACT PROBLEM WITH
IMAGING AND PROPRIETARY
STANDARDS.
SO IMAGING STANDARDS, REALLY
IMPORTANT.
ANOTHER THING I THINK WE NEED IS
JUST LARGER A.I.-READY DATASETS.
I JUST WANT TO MAKE SURE THAT
EVERYBODY HERE IS AWARE OF TWO
DIFFERENT PROGRAMS THAT ARE
TRANS-NIH.
ONE OF THEM IS A COMMON FUND
BRIDGE2AI.
THERE'S FIVE OF THESE I.C.s
THAT REALLY HELPED TO DEVELOP
THAT ORIGINAL PROJECT, BUT IT'S
GOING TO BE A $100 MILLION
PROJECT OVER FOUR YEARS, AND
ALMOST ALL THE WORK THAT WE'VE
DONE HISTORICALLY AT NIH HAS
BEEN WHAT I'D CALL
HYPOTHESIS-DRIVEN RESEARCH.
BUT BRIDGE 2AI IS DIFFERENT.
THE FUNDING HERE IS TO BUILD
DATASETS FROM MULTIPLE SOURCES.
SO IT'S IN A WAY
HYPOTHESIS-GENERATING.
A REALLY, REALLY DIFFERENT
PARADIGM FOR NIH.
SO HOPEFULLY ANNOUNCEMENTS ABOUT
THIS COMING UP RELATIVELY SOON.
ANOTHER PROJECT THAT I WANT TO
MAKE SURE PEOPLE ARE AWARE OF IS
AIM AHEAD.
AIM AHEAD IS RUN THROUGH THE
OFFICE OF DATA SCIENCE STRATEGY
THAT'S LED BY SUSAN GREGURICK,
AND JAMES, OUR PROGRAM OFFICER
INVOLVED WITH THIS PROJECT.
THERE ARE OBVIOUSLY MANY PROJECT
OFFICERS INVOLVED, BUT THE GOAL
OF BRIDGE2AI IS TO USE
ARTIFICIAL INTELLIGENCE TO
ADDRESS CHALLENGES OF HEALTH
DISPARITIES IN MINORITY HEALTH
IN THIS COUNTRY.
THERE'S MANY WAYS OF DOING THAT.
THE ONLY FEW EXAMPLES THAT I'LL
PUT ON THIS SLIDE ARE HOW MIGHT
YOU DO THAT?
WELL, NUMBER ONE, DEVELOPING
DIVERSE DATASETS THAT TRULY
REPRESENT THE AMERICAN
POPULATION.
NUMBER TWO, HELPING TO TRAIN A
DIVERSE WORKFORCE.
MANY OF THE COMMUNITY HEALTH
CLINICS THAT TAKE CARE OF
PATIENTS WHO ARE IN MEDICALLY
UNDERSERVED AREAS MAY NOT EVEN
HAVE ELECTRONIC HEALTH RECORD
SYSTEMS NOW, LET ALONE USING
ARTIFICIAL INTELLIGENCE.
SO ANOTHER WAY IS BY DETECTING
A.I. APPROACHES TO DETECT BIAS.
WE DEFINITELY DON'T WANT A WORLD
WHERE A.I. SYSTEMS REINFORCE
BIAS.
NOW JUST A WORD ABOUT BIAS.
BIAS CAN GET INTO THE MEDICAL
RECORD IN MANY DIFFERENT WAYS
AND ONE OF THEM IS DOCTORS AND
NURSES MAY JUST WRITE
OBSERVATIONS IN CHARTS THAT ARE
PURELY SUBJECTIVE.
YOU KNOW, "MY PATIENT WAS
NONCOMPLIANT."
WELL, WHAT DOES THAT EVEN MEAN?
SO I THINK IT'S VERY HARD TO
STUDY BIAS AND UNDERSTAND THE
SOURCES OF BIAS WHEN YOU'RE
LOOKING AT DATA THAT MAY BE
INHERENTLY BIASED AND HOW DO YOU
KNOW WHAT'S TRUE?
SO I HOPE THAT THINGS LIKE
IMAGES ARE GOING TO BE ONE WAY
TO PROVIDE OBJECTIVE DATA THAT
CAN REALLY ANCHOR THAT AND
PROVIDE MEANINGFUL WAYS TO STUDY
BIAS AND FIGURE OUT HOW CAN WE
DO A BETTER JOB.
SO OVERALL, WE NEED LARGER
DATASETS, A.I.-READY DATASETS.
THE LAST THING THAT I THINK THAT
WE NEED IS MEDICAL EDUCATION IN
ARTIFICIAL INTEL JESS INTA
SCIENCE.
I ASK THE QUESTION, WHAT'S THE
ONE TOOL USED BY EVERY SINGLE
DOCTOR AND SCIENTIST IN THIS
COUNTRY?
IT'S NOT A STETHOSCOPE, IT'S NOT
EVEN AN OPHTHALMOSCOPE IF YOU'RE
AN OPHTHALMOLOGIST.
I WOULD ARGUE THAT IT'S THE
COMPUTER, AND IT'S COMPUTING
TECHNOLOGY.
AND DESPITE THAT, THERE IS
BASICALLY NO SYSTEMATIC TRAINING
OR FRAMEWORK THAT PEOPLE GET
ABOUT WHAT THE IMPLICATIONS ARE
OF THIS TECHNOLOGY OR HOW TO USE
IT.
I WOULD COMPARE THAT TO MEDICAL
STUDENTS WILL SPEND MONTHS TO
YEARS OF FOCUSED DEDICATED TIME
LEARNING HOW TO TAKE A HISTORY,
HOW TO DO A PHYSICAL
EXAMINATION, HOW TO AUSCULTATE
THE HEART.
YET FOR THE ONE TOOL THAT EVERY
SINGLE PERSON USES, TYPICALLY
IT'S ZERO TIME THAT'S DEDICATED
TO LEARNING HOW TO DO IT AND
PEOPLE LEARN BY TRIAL AND ERROR
BASICALLY.
AND YOU KNOW, THIS IS
FUNDAMENTALLY CHANGING THE
PRACTICE AND THE SCIENCE OF
MEDICINE.
THE OTHER THING I WOULD POINT
OUT IS THAT THERE'S A PAIR TIME
IN EDUCATION WHERE THE PERSON
WITH MORE GREY HAIR TEACHES THE
PERSON WITH LESS GREY HAIR HOW
TO DO THINGS, YET IN THIS CASE,
IT'S THE PERSON WITH MORE GREY
HAIR WHO KNOWS LESS ABOUT THE
TECHNOLOGY.
AND SO WE'VE GOT TO FIGURE OUT
WAYS TO DO A BETTER JOB WITH
THIS.
SO I'M GOING TO CLOSE UP THIS
SECTION AND BASICALLY SAY WHAT
HAVE I LEARNED ABOUT THE FUTURE
OF MEDICINE?
WELL, I WOULD SAY THAT WE'VE
TALKED ABOUT THIS THEME OF GOING
FROM QUALITATIVE TO
QUANTITATIVE.
RIGHT?
AND THAT'S TRUE IN ARTIFICIAL
INTELLIGENCE, IT'S TRUE IN
MEDICAL IMAGING AND
EVIDENCE-BASED PRACTICE
GUIDELINES.
I ALSO WILL THINK ABOUT
QUESTIONS FROM THIS FRAMEWORK OF
WHAT IS YOUR ADDED VALUE?
WHEN I WAS LEARNING TO BECOME AN
OPHTHALMOLOGIST, I SPENT SEVERAL
YEARS USING THIS DEVICE, A
CONTACT LENS YOU PUT UP TO
SOMEBODY'S EYE, AND I LEARNED TO
EXAMINE THE NUANCE OF THE
PATIENT'S OPTIC NERVE AND THE
RETINA BY LOOKING AT THOUSANDS
OF PEOPLE.
AND THAT WAS MY ADDED VALUE,
THAT I COULD DETECT NUANCE IN
THE STRUCTURE OF THE RETINA.
NOBODY DOES THAT ANYMORE.
WE GET TESTS LIKE OCT IMAGES
FROM THE RETINA, AND THOSE TESTS
ARE QUANTITATIVE AND THEY SPIT
OUT NUMBERS.
SO THE QUESTION I WONDER
SOMETIMES IS THAT WHAT'S YOUR
ADDED VALUE?
IF YOU'RE A RETINAL
SUBSPECIALIST VERSUS A GENERAL
OPHTHALMOLOGIST VERSUS AN
OPTOMETRIST VERSUS A PATIENT
USING GOOGLE SEARCH AND YOU'RE
ALL SEEING THE EXACT SAME
NUMBERS, HOW DO WE GET ADDED
VALUE IN CLINICAL CARE?
SO DOCTORS DIAGNOSE PATIENTS.
WHERE A.I. SYSTEMS ARE REALLY
PROMISING, BUT YOU NEED TO HAVE
CLINICAL JUDGMENT TO COLLECT
THAT DATA.
THEY ALSO MANAGE DISEASE.
THEY MANAGE PEOPLE.
IF SOMEBODY HAS CANCER, ARE WE
GOING TO START CHEMOTHERAPY,
WHICH MAY GIVE YOU A LONGER
LIFESPAN ON AVERAGE, SLIGHTLY,
BUT MAY MAKE YOU MISERABLE FROM
SIDE EFFECTS, VERSUS ARE WE
GOING TO PUT YOU IN HOSPICE CARE
WHERE YOU CAN SPEND TIME WITH
YOUR FRIENDS AND FAMILY.
THOSE ARE THANK YOU MAN HUMAN DE
THERE'S NOT HE NECESSARILY A
RIGHT OR WRONG.
ADDING DOCTORS IN AN
INCREASINGLY QUANTITATIVE WORLD,
I THINK THERE MAY BE CONCERNS IF
THINGS LIKE ELECTRONIC HEALTH
RECORDS ARE SEPARATING PATIENTS
FROM DOCTORS, AND YOU KNOW,
DOCTORS ARE ONLY LEARNING TO
TALK TO SCREENS INSTEAD OF
TALKING TO PEOPLE.
SO I THINK THERE'S A LOT OF
IMPLICATIONS HERE OF WHAT THE
FUTURE IS OF MEDICINE.
SO I'M JUST GOING TO CLOSE UP
WITH A FEW ANECDOTES ABOUT GOING
BACK TO THE CONCEPT OF WHAT IT
HAS MEANT TO BE TO BE AN ASIAN
AMERICAN SCIENTIST.
SO ANECDOTES NOW.
IT WAS 2012.
AT THE TIME I WAS UK TA TALKY
FATHER.
BECAUSE MY FATHER IS AN ASIAN
PARENT, HE IN 2012 WAS STILL
TRYING TO TELL ME HOW TO DO MY
JOB, EVEN THOUGH HE'S NOT A
PHYSICIAN.
BUT SO HIS QUESTION TO ME WAS,
WHAT DO YOU DO DURING AN
ACADEMIC TALK?
YOU GO VISIT A PLACE FOR AN
HOUR, AND SO I TOLD HIM THAT
WHAT I DO IS I TALK FOR 45
MINUTES, AND THERE'S 15 MINUTES
OF QUESTION AND ANSWER AT THE
END.
SO HE PAUSES AND HE SAYS, WELL,
WHY DO YOU DO THAT?
THAT MAKES NO SENSE.
SHOULDN'T IT BE BACKWARDS?
LIKE SHOULDN'T YOU TALK FOR 15
MINUTES AND THEN DISCUSS FOR 45
MINUTES, BECAUSE THE DISCUSSION
AND THE Q & A IS WHERE YOU
REALLY LEARN AND EXCHANGE IDEAS,
AND WHERE YOU CAN REALLY TELL,
DOES A SPEAKER REALLY KNOW WHAT
THEY'RE TALKING ABOUT.
I THOUGHT ABOUT THAT AND REALLY
THOUGHT THAT, YOU KNOW, IT MADE
SOME SENSE, BUT WE JUST DON'T DO
THINGS THAT WAY.
IT TURNS OUT THAT ONE OF THE
THINGS I THOUGHT BACK TO WAS
THAT THE PREVIOUS YEAR, I HAD
GONE TO TWO VERY SIMILAR TRIPS
INTERNATIONALLY.
IN AUGUST 2011, I WAS IN BRAZIL,
AND IN NOVEMBER 2011, I WAS IN
TAIWAN.
AND IN BOTH OF THOSE PLACES, I
WAS INVITED BY PEOPLE WHO I
KNEW, THEY'RE BOTH RETINA
SPECIALISTS, MET PEOPLE, MADE
SOME FRIENDS AND COLLEAGUES, AND
I GAVE A LOT OF TALKS.
AND AFTER THIS TALK IN BRAZIL,
AFTER 45 MINUTES, ASKED THE
QUESTION, OKAY, WELL, I'M DONE,
WHAT QUESTIONS DO PEOPLE HAVE?
AND SO MANY PEOPLE RAISED THEIR
HANDS AND WE HAD A DISCUSSION
THAT ACTUALLY WENT MUCH LONGER
THAN 15 MINUTES.
THREE MONTHS LATER, I GAVE THE
EXACT SAME TALK IN TAIWAN, AND
AT THE END, WHAT QUESTIONS DO
PEOPLE HAVE, NOT A SINGLE HAND
WENT UP.
AND I HAD NEVER HAD THAT HAPPEN
IN MY CAREER WHERE NOBODY ASKED
A QUESTION.
AND THOSE OF YOU IN THE AUDIENCE
WHO COME FROM BACKGROUNDS IN
ASIA WILL RECOGNIZE THAT THAT'S
NOT ALWAYS ENCOURAGED, THAT
SOMETIMES PEOPLE DON'T WANT TO
LOOK BAD IF THEY ASK A QUESTION.
THE PARADIGM MAY BE THAT THE
PERSON WITH MORE GREY HAIR IS
CONSIDERED AN EXPERT AND YOU
DON'T WANT TO QUESTION THEIR
EXPERTISE.
EVEN WHEN THE EXPERT WAS 41
YEARS OLD IN 2011, YOU KNOW,
THEY MAY NOT HAVE WANTED TO ASK
ANYTHING.
SO I THOUGHT, WELL, ISN'T THAT
INTERESTING, THAT MY PARENTS
AFTER HAVING BEEN IN THIS
COUNTRY MUCH LONGER THAN THEY
WERE IN TAIWAN, ASKED THIS
QUESTION, WELL, MAYBE THE WAY
THAT WE DO THINGS IN THE U.S.
SHOULD BE MORE FOCUSED ON
EXCHANGE OF IDEAS, LET ALONE THE
WAY THEY WERE DOING THINGS IN
TAIWAN, IN NOVEMBER 2011.
SO IT TURNED OUT THAT THREE
YEARS LATER, BACK IN TAIWAN,
GIVING A SERIES OF TALKS JUST
LIKE BEFORE, SAW A LITTLE BIT OF
CULTURAL SITES, BUT IT TURNED
OUT THAT WE DID ONE THING THAT
WAS DIFFERENT.
WE BASICALLY SPENT MONTHS
SCRIPTING AN EVENT WHERE WE HAD
PEOPLE THERE COME UP WITH
RESEARCH PROPOSALS.
SO IT DOESN'T MATTER IF YOU'RE
THE DEPARTMENT CHAIR OR IF
YOU'RE A RESEARCH FELLOW,
EVERYBODY WOULD SIT AROUND THE
CIRCLE HERE AND WE PUT THESE UP
ON THE SCREEN AND DISCUSS THEM,
AND WE'D CRITICIZE THEM.
WHY ARE YOU DOING THINGS THIS
WAY, WHY ARE YOU -- AND MY JOB
WAS TO MODERATE THAT DISCUSSION.
AND IT WAS FASCINATING BECAUSE I
WOULD BASICALLY TAKE WHAT I
THOUGHT THAT I'VE LEARNED, AND
MAYBE WE NEED 45 MINUTES OF
DISCUSSION AND 15 MINUTES OF
PRESENTATION, AND TO TRY TO COME
UP WITH A BETTER SYSTEM.
I HOPE THAT'S SOMETHING WE CAN
REALLY DO AT NIH IN TERMS OF WE
INTEGRATE THE GOOD THINGS FROM
EVERY CULTURE AND COME UP WITH
WHAT'S THE BEST PROCESS FOR
DOING SCIENCE.
SO WITH THAT, I'M GOING TO GO UP
TO 2021.
IN SOME WAYS, WHAT WE SEE IN THE
NEWS NOW IS VIOLENCE DIRECTED
TOWARD ASIAN AMERICAN, STOP
ASIAN HATE MOVEMENT.
WE'VE GOT THINGS LIKE SHOOTINGS
IN ATLANTA.
AND SOMETIMES I FEEL LIKE WE'RE
GOING BACK TO THE 1980s, BACK
IN THE ERA OF VINCENT CHIN AND
MARTIAL ARTS THEATER.
AND ONE OF THE THINGS, AFTER THE
ATLANTA SPA SHOOTINGS, I SAW
MANY UNIVERSITIES HAD THEIR
STATEMENTS ABOUT WHAT DOES THIS
MEAN, WE'RE GOING TO SPEAK OUT
AGAINST THIS, AND THE ONE THAT
REALLY STUCK OUT AT ME THE MOST
ACTUALLY CAME FROM HOWARD
UNIVERSITY.
THIS IS A COPY OF THAT LETTER
FROM THE PRESIDENT OF HOWARD.
IT SAID, HERE, IT IS HOWARD'S
MOST SACRED DUTY TO STAND UP TO
HATE IN ALL FORMS AGAINST ALL
PEOPLE.
IT'S NOT ENOUGH TO REPUDIATE THE
SHOOTINGS, WE MUST ALSO CONDEMN
THE PHYSICAL ATTACKS, VERBAL
ASSAULTS, DEMONIZING BEHAVIOR
AND THE OTHERRING OF THE ASIAN
AMERICAN COMMUNITY.
OF COURSE AS A HISTORICALLY
BLACK COLLEGE AND UNIVERSITY,
THIS SORT OF TRAGEDY RESONATES
STRONGLY AMONG OUR CAMPUS.
A FEAR HAS BEEN EMBEDDED INTO
THEIR DNA.
I READ THIS AND REALLY THOUGHT,
WOW, WHAT MY HOPE IS FOR NIH,
THE DIFFERENT COMMUNITIES, THE
AA AND AANHPI COMMUNITY, ALSO TO
UNDERSTAND AND EMPATHIZE WITH
OTHER COMMUNITIES JUST AS I SAW
FROM THIS LETTER.
MY HOPE IS THAT IF WE CAN DO
THAT AT NIH, I DON'T KNOW IF I
COULD SAY IT BETTER THAN WAYNE
FREDERICK, WHO'S THE PRESIDENT,
A BETTER WORLD IS WITHIN OUR
GRASP IF WE UNITED, CAN
ERADICATE THAT WHICH DIVIDES US,
AND WEAVE MORE TIGHTLY THE
FABRICS THAT BIND US.
SO WITH THAT, ALMOST DONE, AND
WHAT HAVE I LEARNED AS AN ASIAN
AMERICAN CLINICIAN-SCIENTIST?
WELL, NUMBER ONE, IT'S GOOD TO
DRAW FROM DIFFERENT CULTURES.
IN ASIAN CULTURE, MY PARENTS
GREW UP IN, THERE'S THINGS LIKE
DISCIPLINE.
HERE WHAT I LEARNED IS THE
EXCHANGE OF IDEAS.
THE 15 MINUTES VERSUS 45
MINUTES.
AND WHAT I LEARNED AS A KID IS
MAYBE IT'S NOT THE WORST THING
TO BE DIFFERENT.
I FELT LIKE, WELL, I'M NOT GOING
TO BE CLASS PRESIDENT, BUT MAYBE
I NEVER FELT THAT NEED TO
CONFORM WITH OTHER PEOPLE AND
MAYBE THAT HELPED ME THINK
OUTSIDE THE BOX.
I WAS AN ENGINEER WHO BECAME A
DOCTOR, AND A DOCTOR WHO BECAME
A BIOMEDICAL INFORMATICS PERSON.
SO MAYBE THAT SORT OF HELPED ME.
I LEARNED THINGS LIKE THE
IMPORTANCE OF EFFECTIVE
COMMUNICATION, AND OF SPEAKING
UP, AND THAT WAS THE REASON I
TALKED ABOUT ANECDOTES LIKE
WALTER CON CITE CRONKITE AT
CHIN.
I WOULD SAY I'VE LEARNED THE
NEED FOR MENTORSHIP AND ROLE
MODELS AND PEOPLE WHO LOOK LIKE
YOU.
THIS IS REASONS I SHOWED SLIDES
OF CAREER DEVELOPMENT.
THESE ARE PICTURES FROM THE
CHINESE AMERICAN
OPHTHALMOLOGICAL SOCIETY.
HERE'S A PICTURE OF ME, HERE'S
EMILY CHU AT ONE OF THESE
DINNERS.
SIDE SAY THAT, YES, DEFINITELY
THE ROLE OF COLLABORATION, TEAM
WORK AND SCIENCE, AND THAT'S WHY
I SHOWED PICTURES OF PEOPLE FROM
OTHER BACKGROUNDS, REGARDLESS OF
WHETHER IT'S PEOPLE FROM OTHER
SCIENTIFIC BACKGROUNDS OR PEOPLE
LIKE THE PRESIDENT OF HOWARD
UNIVERSITY.
THOSE ARE THE REASONS THAT I'VE
PUT THOSE SLIDES UP.
AND SO MY VERY LAST SLIDE IS
WHAT IS OUR PAST, PRESENT AND
FUTURE?
SO WE STARTED THIS TALK WITH
K.T. JEANG, I WANT TO END IT
WITH K.T. JEANG.
ONE OF HIS BIG POINTS IN 2008,
2010 AND 2013 WAS ADVOCACY FOR
LEADERSHIP AMONG ASIAN AMERICAN,
AND THAT'S WHERE WE WERE IN THE
PAST.
NOW IN 2022, WE HAVE THREE I.C.
DIRECTORS OF ASIAN AMERICAN
DESCENT.
I THINK K.T. JEANG WOULD HAVE
BEEN, I HOPE, IMPRESSED BY THAT
PROGRESS.
AND THE LAST THING I WANT TO SAY
IN TERMS OF THE FUTURE IS THAT I
GAVE THAT ANECDOTE OF NEVER
WANTING TO BE CLASS PRESIDENT.
AND FOUR YEARS AGO, OUR DAUGHTER
CAME HOME FROM SCHOOL, SHE WAS A
JUNIOR IN HIGH SCHOOL BACK THEN,
SHE CAME BACK HOME, IT WAS
8:00 P.M., SHE RAN UPSTAIRS, WAS
DOING SOME WORK, DENT DIDN'TR
EATER DINNER.
WE SAID WHAT ARE YOU DOING?
SHE SAID, I WANT TO RUN FOR
CLASS PRESIDENT.
IT TURNED OUT SHE DECIDED TO RUN
FOR STUDENT BODY, VICE PRESIDENT
INSTEAD, AND SHE WON.
AND SO THIS WAS ERICA WHEN SHE
WAS IN HIGH SCHOOL WITH HER
FRIENDS.
AND TO ME, I THINK THAT WHEN I
LOOK AT THE CHANGE THAT OCCURRED
BETWEEN MY GENERATION AND MY
DAUGHTER'S GENERATION, THAT
REALLY GIVES ME A LOT OF
OPTIMISM FOR THE FUTURE.
SO I HOPE THAT THIS HAS REALLY
BEEN ABLE TO DO JUSTICE TO
DR. JEANG'S LEGACY, AND THANK
YOU VERY MUCH FOR THE PRIVILEGE
OF ALLOWING ME TO SPEAK HERE.
>> THANK YOU VERY MUCH FOR A
VERY INFORMATIVE TALK.
WE HAVE TIME FOR A LITTLE BIT OF
QUESTION AND ANSWER.
PEOPLE IN THE AUDIENCE CAN USE
THE SEND LIVE FEEDBACK BUTTON TO
ASK QUESTIONS.
WE HAVE ONE QUESTION ALREADY
FROM TOM SNYDER.
HAVE YOU CONNECTED WITH NIST AND
IEEE ABOUT THE DATA
STANDARDIZATION?
>> TOM, THANKS FOR ASKING ABOUT
THAT.
WE ACTUALLY HAVEN'T CONNECTED TO
NIST YET, AND HAVE WONDERED
ABOUT DOING THAT.
OF AND THANKS FOR THAT.
SO FAR WE'VE JUST WORKED WITH
ONCFDA ABOUT IT.
THERE ARE STANDARDS FOR
RADIOLOGY THAT HAVE BEEN ADOPTED
FOR DECADES, DICOM OBVIOUSLY,
AND SEVERAL OF THE SPEAKERS FROM
OUR WORKSHOP WERE FOLKS FROM THE
RADIOLOGY COMMUNITY, AND WE'LL
DEFINITELY THINK MORE ABOUT
THAT.
>> ONE QUESTION I'VE ALWAYS HAD,
YOU MENTION THE ABILITY OF THE
A.I. TO IDENTIFY SEX FROM THE
RETINAL PATTERNS.
WHY IS IT SO HARD TO FIGURE OUT
WHAT THE A.I. IS SEEING?
>> ROLAND, THERE'S NEVER BEEN A
GOOD WAY TO LOOK UNDERNEATH WHAT
THE EXPLANATIONS OF THESE DEEP
NEURAL NETWORKS ARE DOING, IN
OTHER WORDS, WHAT LAYER OF THESE
CONVOLUTIONAL NETWORKS IS REALLY
CORRESPONDING TO WHAT.
FOR A WHILE, EXPLAINABLE A.I.
WAS A VERY HOT TOPIC.
WHY DID THESE SYSTEMS COME UP
WITH THE CONCLUSION THEY COME UP
WITH.
IN FACT, MY VERY LAST NIH GRANT
HAD A SPECIFIC AIM THAT DEALT
WITH EXPLAINABLE A.I. IN ROP.
I'M NOT SO SURE HOW IMPORTANT
THAT'S GOING TO BE LOOKING
FORWARD.
MY GUESS IS I'M NOT SURE IT'S
BETTER THAN ANYBODY ELSE'S
GUESS.
BUT I THINK WE'RE AT A
TRANSITIONAL POINT IN MEDICINE
WHERE PEOPLE DON'T QUITE KNOW
WHAT TO DO WITH THESE
TECHNOLOGIES OR HOW TO INTERPRET
THEM.
AND THEY MAY NOT TRUST THEM AS A
RESULT.
ON THE OTHER HAND, HOW MANY
DOCTORS REALLY UNDERSTAND HOW AN
MRI MACHINE WORKS, OR HOW THAT
MACHINE WORKS THAT GIVES YOU
YOUR BLOOD COUNT, OR FOR THAT
MATTER, HOW MANY DOCTORS TRULY
UNDERSTAND THE INTRICACY OF WHAT
WENT INTO THAT STUDY DESIGN,
THAT CLINICAL TRIAL THAT PROVED
WHATEVER IT PROVED, AND IT
WOULDN'T SURPRISE ME THAT IF
THESE SYSTEMS REALLY HAVE VALUE,
THAT PEOPLE WILL JUST TRUST WHAT
THEY SAY, BUT IT'S GOING TO BE
UP TO THE RESEARCH COMMUNITY TO
PROVE THAT THEY REALLY WORK, AND
THAT THEY WORK ACROSS DIFFERENT
POPULATIONS.
SO IT'S A LONG ANSWER, AND I
KNOW IT'S A LITTLE BIT NUANCED
TO THAT QUESTION.
>> THAT ACTUALLY FEEDS INTO MY
NEXT QUESTION.
I GREW UP WATCHING A LOT OF
SCIENCE FICTION.
I DON'T KNOW IF YOU EVER SAW THE
BUCK RODGERS WITH DR. THEOPOLIS,
THE A.I. WHO WAS PART OF THE
LEADERSHIP TEAM, AND I THINK
ABOUT THE ANALOGIES BETWEEN
MANAGING HUMAN DIVERSITY WITHIN
A TEAM WITH DIVERSE PERSPECTIVES
AND JUST INCORPORATING THE A.I.
INTO THAT IS SORT OF JUST
EXTENDING THAT SPECTRUM OF
MANAGING DIVERSITY.
HOW DO YOU FEEL ABOUT THAT?
DO YOU THINK OF A.I. AS A
PARTNER?
>> SORROW LAND, THERE'S E
PARTS TO THAT QUESTION.
ONE OF THEM IS, IS THE A.I. A
PARTNER, AND I THINK -- I HOPE
THE ANSWER IS DEFINITELY YES.
THERE ARE A LOT OF PEOPLE IN THE
DOCTOR COMMUNITY WHO ARE WORRIED
THAT THE A.I. IS GOING TO TAKE
THEIR JOB, AND I DON'T SEE IT
THAT WAY AT ALL.
I SEE IT AS BEING A PARTNERSHIP.
I SEE IT AS BASICALLY GIVING
MORE DATA TO THE DOCTORS TO HELP
MAKE WHAT THEIR MANAGEMENT PLAN
IS.
THE SECOND ISSUE, THOUGH, IS A
QUESTION ABOUT WHERE DOES THE
A.I. STOP AND WHERE DOES THE
DOCTORING BEGIN.
I THINK THAT'S A LITTLE MORE OF
A NUANCED QUESTION.
SOME OF THOSE EXAMPLES THAT I
THINK YOU'RE GETTING AT, YOU
KNOW, DIVERSITY AMONG PEOPLE OR
GETTING PEOPLE TO WORK TOGETHER,
I'M NOT SURE I'M IMMEDIATELY
SEEING THAT AS AN A.I. TASK AS
MUCH AS IT IS A HUMAN TASK.
TO READ EMOTION OR NUANCE OF
BEHAVIOR.
BUT I THINK IN BROADER STROKES,
I WOULD SAY THAT THE REAL
QUESTION THAT WE HAVE TO FIGURE
OUT IS WHERE DOES COMPUTING STOP
AND WHERE DOES HUMAN DOCTORING
PICK UP.
AND HOW DO WE, ALONG THOSE SAME
LINES, TRAIN THE HUMAN DOCTORS
FOR THE SKILLSET THAT ARE GOING
TO BEST NEED TO DO WHAT
DOCTORING REQUIRES IN THE 21ST
CENTURY.
I THINK WE'RE A LONG WAY FROM
HAVING FIGURED THOSE THINGS OUT.
>> SO YOU MENTIONED THE
RETINOPATHY OF PREMATURITY AND
DIABETIC RETINOPATHY.
ARE THERE ANY OTHER EYE DISEASES
WHERE A.I. COULD BE PROMISING?
>> THERE ARE SO MANY DIFFERENT
EYE DISEASES THAT ARE BASED ON A
SIMILAR PARADIGM WHERE YOU LOOK
AT AN IMAGE AND YOU MAKE A
DIAGNOSIS, OR EVEN SOME WHERE
YOU DON'T LOOK AT AN IMAGE AND
YOU MAKE A DIAGNOSIS.
SO THERE ARE MANY, MANY
APPLICATIONS.
AS YOU CAN IMAGINE, I WOULD
ALMOST SAY COUNTLESS PAPERS THAT
HAVE BEEN PUBLISHED WITHIN THE
PAST FIVE YEARS.
BUT I'D SAY THE MOST COMPLOB
BLINDING COMMON ARE
GOING TO BE THINGS LIKE DIABETIC
RETINOPATHY, AGE-RELATED MACULAR
DEGENERATION AND GLAUCOMA.
WITHIN THOSE AREAS, THERE'S BEEN
A.I. WORK IN ALL OF THEM AT
VARYING LEVELS OF MATURITY.
I WOULD HIGHLIGHT WITHIN THE
NATIONAL EYE INSTITUTE, EMILY
CHU -- THEY'VE DONE SOME NICE
WORK FOR MACULAR DEGENERATION
WORK.
IT'S GOING TO BE HOW ARE WE
GOING TO TAKE THOSE AND APPLY
THEM TO REAL WORLD CARE.
IT'S GOING TO BE FINDING WHAT
THE FDA WOULD CALL INDICATIONS
FOR USE.
>> I THINK WE'LL WRAP IT UP WITH
A QUESTION FROM PETER KILMARKS.
HE SAYS GREAT TALK, MIKE.
CAN YOU COMMENT ON INTERNATIONAL
COLLABORATIONS?
SHOULD GETTING DIVERSE MATERIAL
INCLUDE POPULATIONS IN OTHER
COUNTRIES?
ALSO ARE THERE APPLICATIONS IN
RESOURCE-POOR SETTINGS WITH
LIMITED MEDICAL CARE IN THE U.S.
AND ABROAD?
>> YEAH, PETER, THANKS FOR
ASKING THAT.
I THINK THERE ARE TWO PARTS TO
YOUR QUESTION.
THERE'S A VALIDATION PART AND
THERE'S A APPLICATION PART.
IN THE VALIDATION PART, I THINK
DEFINITELY YES.
ONE OF THE -- ONE WAY TO LOOK AT
THAT WOULD BE IF WE WANT TO
VALIDATE THESE SYSTEMS ACROSS
DIFFERENT RACES, FOR EXAMPLE,
THERE ARE PEOPLE OF DIFFERENT
RACES IN DIFFERENT COUNTRIES
THAT WE COULD VALIDATE THESE
SYSTEMS ON.
ANOTHER WAY TO LOOK AT
VALIDATION IS THAT DISEASE ISN'T
JUST ABOUT WHO HAS THE DISEASE
AND HOW OLD THE PATIENT IS, WHAT
TYPE OF PERSON IT IS.
THERE MAY BE ENVIRONMENTAL
FACTORS THAT ARE DIFFERENT IN
ONE SETTING VERSUS ANOTHER
COUNTRY.
FOR EXAMPLE, IN THE DISEASE THAT
I STUDY, RETINOPATHY OF
PREMATURITY, OXYGEN MANAGEMENT
IS A BIG PART OF THAT DISEASE,
AND IT'S MANAGED DIFFERENTLY IN
THE HIGHLY RESOURCED WORLD
VERSUS THE LESS HIGHLY RESOURCED
WORLD.
THE DISEASE DOESN'T ALWAYS LOOK
THE SAME.
SO THERE'S A DIFFERENT COMPONENT
OF VALIDATION.
THE SECOND PART OF YOUR
QUESTION, PETER, WAS DELIVERY OF
CARE.
I REALLY HOPE THAT A.I. CAN BE
ONE OF THOSE AREAS THAT, LOOK,
IN THIS COUNTRY, I THINK THIS
PANDEMIC SHOWED US THAT WE'VE
GOT A LOT OF INEQUITIES IN HOW
TO DELIVER CARE AND MAYBE A.I.
CAN BE ONE OF THOSE AREAS THAT
HELPS US DELIVER BETTER CARE TO
A WIDER RANGE OF PEOPLE, AND
THAT SAME THING COULD BE SAID
FOR OTHER COUNTRIES.
SO I REALLY HOPE THERE'S
POTENTIAL FOR A LOT OF WORK THAT
COULD BE DONE.
>> ONE MORE QUESTION FROM ASAN
OOLA.
ARE THERE ANY INITIATIVES FOR
USE OF A.I. AND GENOMIC DATA AT
NEI OR AT OTHER PARTS OF NIH?
>> YEAH, ASAN, DEFINITELY YES.
WITHIN THE NEI INTRAMURALLY AN
ALSO WITHIN OUR BROADER WORLD.
I THINK YOU CAN LOOK AT A LOT OF
POSSIBLE APPLICATIONS, BUT MAYBE
ONE OF THEM IS JUST IN TERMS OF
ONE OF THE THINGS THAT WE DO IN
MY PIECE OF THE WORLD, I'M
SPEAKING PURELY AS A RESEARCHER
NOW, IS THAT WE TRY TO DEVELOP
RISK MODELS FOR DISEASE.
YOU KNOW, BASED ON YOUR
APPEARANCE TODAY, WHAT ARE YOU
LIKELY TO LOOK LIKE NEXT WEEK.
AND WE CAN DEVELOP PREDICTIVE
MODELS THAT ARE BASED ON THAT.
JUST PURELY FROM IMAGING DATA.
WE CAN DEVELOP MODELS BASED ON
IMAGING DATA PLUS CLINICAL DATA,
AND I SEE NO REASON WE SHOULDN'T
BE DEVELOPING RISK MODELS BASED
ON, FOR EXAMPLE, IMAGE PLUS
CLINICAL PLUS GENETIC DATA.
SO I THINK THAT THERE'S A LOT OF
POTENTIAL APPLICATIONS OF THAT
THAT COULD BE REALLY EXCITING
BOTH FROM A A PREDICTIVE VIEW D
ALSO MAYBE FROM A MECHANISTIC
VIEW.
>> OKAY.
SO THE HITS JUST KEEP ON COMING.
WE HAVE ONE FROM LUKE NELSON,
WHO'S A TRAINEE AT NIH.
, WHO ASKS, WHAT ADVICE DO YOU
HAVE FOR A MEDICAL STUDENT IN
OPHTHALMOLOGY WHO'S DOING SOME
CODING AND MIGHT BE INTERESTED,
WHAT'S A GOOD FUTURE DIRECTION
SKILLSET TO START TO BUILD THAT
WILL HELP SOMEBODY IN THE
FUTURE?
>> LUKE, I'M GLAD YOU ASKED THAT
QUESTION BECAUSE I THINK WE NEED
MORE PEOPLE IN MEDICINE WHO CAN
UNDERSTAND THE INTERSECTION
BETWEEN DATA SCIENCE AND
CLINICAL MEDICINE.
I THINK THE BEST WAY TO ANSWER
THAT CONCISELY WOULD BE THAT I
THINK THAT THE PATHWAY TO GET
THERE IS GOING TO DEPEND ON
WHERE YOU'D LIKE TO BE
EVENTUALLY.
I THINK THAT ON ONE IF I WERE TO
GO ON A SPECTRUM, ONE END WOULD
BE THAT YOU COULD BE THE
ELECTRONIC HEALTH RECORD PERSON
WITHIN YOUR CLINICAL PRACTICE AS
A DOCTOR, SOMEWHERE ELSE IN THE
MIDDLE WOULD BE THAT YOU COULD
BE THE PERSON WHO COLLABORATES
WITH ENGINEERS OR SCIENTISTS TO
APPLY MECHANISMS TO HEALTHCARE.
ON THE OTHER END OF THE
SPECTRUM, YOU COULD BE THAT
SCIENTIST WHO DEVELOPS THOSE
METHODOLOGIES AND THEN APPLIES
THEM TO HEALTHCARE, AND I THINK
THAT DEPENDING ON WHERE YOU ARE
IN THAT SPECTRUM WOULD REQUIRE
MORE OR LESS METHODOLOGICAL
TRAINING IN THINGS LIKE COMPUTER
SCIENCE AND DATA SCIENCE.
>> I THINK WE'RE GOING TO WRAP
IT UP.
THANK YOU FOR A -- HOLD ON.
YES.
THANK YOU FOR A WONDERFUL
SESSION.
WE HAD 200-PLUS VIEWERS OUT
THERE IN VIDEOCAST-LAND, AND ON
BEHALF OF DR. GOTTESMAN, ALL OF
NIH, OUR PARTNERS IN THE OFFICE
OF EQUITY, DIVERSITY AND
INCLUSION AND THE JEANG FAMILY,
WE THANK YOU VERY MUCH FOR A
WONDERFUL TALK.
>>THANK YOU VERY MUCH, ROLAND.