ABOUT THE SPEAKER
Pratik Shah - Medical technologist
Dr. Pratik Shah creates novel intersections between engineering, medical imaging, machine learning and medicine.

Why you should listen

Dr. Shah's research program at the MIT Media Lab develops scalable and low-cost diagnostics and therapeutics. His ongoing research areas at MIT include: 1) artificial intelligence and machine learning methods for detection of cancer biomarkers using standard photographs vs. expensive medical images; 2) unorthodox artificial intelligence and machine learning algorithms to design optimal and faster clinical trials and to reduce adverse effects on patients; and 3) low-cost and open source imaging devices, paper diagnostics, algorithms and mobile phones to improve public health and generate real-world data.

Clinical studies with Pratik's medical technologies have revealed "missing sick" patients, who otherwise remain undiagnosed in conventional healthcare settings. Dr. Shah's graduate and postdoctoral research contributed to the discovery of a vaccine component to prevent pneumococcal (Streptococcus pneumoniae) diseases; the identification of new pathways, technologies and metabolites as antimicrobials to target gastrointestinal infections; and a nonprofit to deploy a low-cost water quality test for the developing world.

Past recognition for Dr. Shah includes the American Society for Microbiology's Raymond W. Sarber national award, the Harvard Medical School and Massachusetts General Hospitals ECOR Fund for Medical Discovery postdoctoral fellowship, the AAAS-Lemelson Invention Ambassador Award and a TED Fellowship. Pratik has been an invited discussion leader at Gordon Research Seminars; a speaker at Cold Spring Harbor Laboratories, Gordon Research Conferences and IEEE bioengineering conferences; and a peer reviewer for leading scientific publications and funding agencies. Pratik has a BS, MS, and a PhD in Microbiology and completed fellowship training at The Broad Institute of MIT and Harvard, Massachusetts General Hospital and Harvard Medical School.

More profile about the speaker
Pratik Shah | Speaker | TED.com
TEDGlobal 2017

Pratik Shah: How AI is making it easier to diagnose disease

Pratik Shah: Como facilita a intelixencia artificial o diagnóstico de enfermidades

Filmed:
1,571,835 views

Hoxe en día os algoritmos de intelixencia artificial (IA) necesitan decenas de miles de custosas imaxes médicas para detectar unha doenza nun paciente. E se puidésemos reducir de forma drástica a cantidade de datos requiridos para adestrar un sistema de IA, facendo así posibles diagnósticos de baixo custo e máis eficientes? Pratik Shah, membro TED, está a traballar nun enxeñoso sistema para facer exactamente iso. Cun enfoque de IA pouco convencional, Shah creou unha tecnoloxía que só necesita 50 imaxes para desenvolver un algoritmo que funcione --mesmo con fotografías tomadas polo propio médico co seu móbil-- para obter un diagnóstico. Descubre máis sobre como esta nova maneira de analizar a información médica podería facer posible a detección máis temperá de enfermidades potencialmente mortais e estender o diagnóstico asistido por IA a moitos lugares do mundo.
- Medical technologist
Dr. Pratik Shah creates novel intersections between engineering, medical imaging, machine learning and medicine. Full bio

Double-click the English transcript below to play the video.

00:13
Computer algorithms today
are performing incredible tasks
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Hoxe en día, os algoritmos informáticos
realizan tarefas incribles
00:17
with high accuracies, at a massive scale,
using human-like intelligence.
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con gran precisión e a enorme escala,
amosando intelixencia similar á nosa.
00:21
And this intelligence of computers
is often referred to as AI
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A esta intelixencia informática
chámaselle a miúdo IA,
00:25
or artificial intelligence.
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é dicir, intelixencia artificial.
00:27
AI is poised to make an incredible impact
on our lives in the future.
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A IA está lista para ter no futuro
un impacto incrible nas nosas vidas.
00:32
Today, however,
we still face massive challenges
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Con todo, hoxe aínda temos que
enfrontarnos a enormes desafíos
00:36
in detecting and diagnosing
several life-threatening illnesses,
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para detectar e diagnosticar varias
enfermidades potencialmente mortais,
00:40
such as infectious diseases and cancer.
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como as infecciosas ou o cancro.
00:44
Thousands of patients every year
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Cada ano miles de pacientes
00:46
lose their lives
due to liver and oral cancer.
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perden a vida a causa
do cancro de fígado ou de boca.
00:49
Our best way to help these patients
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O mellor modo de axudar a estes pacientes
00:52
is to perform early detection
and diagnoses of these diseases.
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é detectar e diagnosticar
a enfermidade en fases temperás.
00:57
So how do we detect these diseases today,
and can artificial intelligence help?
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Como detectamos hoxe estas enfermidades?
Pode axudar a intelixencia artificial?
01:03
In patients who, unfortunately,
are suspected of these diseases,
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Cando se sospeita que, por desgraza,
un paciente padece unha destas doenzas,
01:07
an expert physician first orders
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un médico experto comeza por pedir
01:10
very expensive
medical imaging technologies
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probas carísimas baseadas
en tecnoloxías de imaxe,
01:12
such as fluorescent imaging,
CTs, MRIs, to be performed.
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como a microscopia de fluorescencia,
a tomografía ou a resonancia magnética.
01:17
Once those images are collected,
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Unha vez obtidas esas imaxes,
01:19
another expert physician then diagnoses
those images and talks to the patient.
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outro experto fai un diagnóstico
e fala co paciente.
01:24
As you can see, this is
a very resource-intensive process,
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Como vedes, é un proceso
que consome moitos recursos,
01:28
requiring both expert physicians,
expensive medical imaging technologies,
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ao requirir médicos expertos
e custosas tecnoloxías médicas de imaxe,
01:32
and is not considered practical
for the developing world.
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e non se considera práctico nos países
en vías de desenvolvemento.
01:35
And in fact, in many
industrialized nations, as well.
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De feito, tampouco en moitas
nacións industrializadas.
01:39
So, can we solve this problem
using artificial intelligence?
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Pódese resolver o problema
coa axuda da intelixencia artificial?
01:43
Today, if I were to use traditional
artificial intelligence architectures
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Se hoxe usásemos arquitecturas
de intelixencia artificial tradicionais
para resolver este problema,
01:47
to solve this problem,
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01:49
I would require 10,000 --
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faría falla xerar 10.000,
01:50
I repeat, on an order of 10,000
of these very expensive medical images
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repito, arredor de 10.000
destas imaxes médicas carísimas,
01:54
first to be generated.
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como primeiro paso.
01:56
After that, I would then go
to an expert physician,
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Logo acudiría a un médico experto,
01:59
who would then analyze
those images for me.
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que analizaría esas imaxes para min.
02:01
And using those two pieces of information,
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E con esas dúas fontes de datos,
02:03
I can train a standard deep neural network
or a deep learning network
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podo adestrar unha rede neural estándar
ou unha rede de aprendizaxe profunda
02:07
to provide patient's diagnosis.
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para que faga o diagnóstico.
02:09
Similar to the first approach,
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Ao igual que no primeiro método,
partir da intelixencia artificial
tradicional
02:11
traditional artificial
intelligence approaches
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02:13
suffer from the same problem.
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presenta o mesmo problema.
02:14
Large amounts of data, expert physicians
and expert medical imaging technologies.
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Moitísimos datos, médicos expertos
e tecnoloxías de imaxe especializadas.
02:20
So, can we invent more scalable, effective
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É posible inventar arquitecturas
de intelixencia artificial
02:24
and more valuable artificial
intelligence architectures
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ampliables, máis eficientes e máis útiles
02:27
to solve these very important
problems facing us today?
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para resolver estes importantes problemas
que temos hoxe?
Isto é precisamente o que fai o meu grupo
no Media Lab do MIT.
02:31
And this is exactly
what my group at MIT Media Lab does.
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02:34
We have invented a variety
of unorthodox AI architectures
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Levamos inventadas varias arquitecturas
de IA pouco convencionais
02:38
to solve some of the most important
challenges facing us today
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para resolver algúns dos desafíos
actuais máis importantes
02:41
in medical imaging and clinical trials.
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no campo da imaxe médica
e as probas clínicas.
02:44
In the example I shared
with you today, we had two goals.
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No caso que veño de describirvos
tiñamos dous obxectivos.
02:47
Our first goal was to reduce
the number of images
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O primeiro era reducir o número de imaxes
02:50
required to train
artificial intelligence algorithms.
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que cómpren para adestrar
algoritmos de intelixencia artificial.
02:53
Our second goal -- we were more ambitious,
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O segundo era máis ambicioso.
02:55
we wanted to reduce the use
of expensive medical imaging technologies
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Queríamos reducir o uso
de custosas tecnoloxías de imaxe médica
na detección de enfermidades.
02:59
to screen patients.
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03:00
So how did we do it?
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Como o fixemos?
03:02
For our first goal,
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Para o primeiro fin,
03:04
instead of starting
with tens and thousands
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no canto de comezar con decenas de miles
03:06
of these very expensive medical images,
like traditional AI,
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desas carísimas imaxes médicas,
como na IA tradicional,
03:09
we started with a single medical image.
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empezamos con só unha.
03:11
From this image, my team and I
figured out a very clever way
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A partir desta imaxe, o meu equipo
atopou un modo moi enxeñoso
03:15
to extract billions
of information packets.
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de extraer miles de millóns
de paquetes de datos.
03:17
These information packets
included colors, pixels, geometry
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Estes paquetes contiñan
cores, píxels, xeometría
03:21
and rendering of the disease
on the medical image.
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e representación da doenza
na imaxe médica.
03:24
In a sense, we converted one image
into billions of training data points,
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En certo sentido, convertemos unha imaxe
en miles de millóns de observacións
03:28
massively reducing the amount of data
needed for training.
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e reducimos moito a cantidade de datos
necesaria para adestrar o sistema.
03:32
For our second goal,
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Para o segundo obxectivo,
03:33
to reduce the use of expensive medical
imaging technologies to screen patients,
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reducir o uso de custosas tecnoloxías
de imaxe para detectar enfermidades,
03:37
we started with a standard,
white light photograph,
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comezamos cunha foto estándar
iluminada con luz branca,
03:40
acquired either from a DSLR camera
or a mobile phone, for the patient.
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tomada cunha cámara réflex dixital
ou cun móbil.
Entón, lembrades os miles de millóns
de paquetes de datos?
03:44
Then remember those
billions of information packets?
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03:46
We overlaid those from
the medical image onto this image,
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Superpuxemos ese datos obtidos
da imaxe médica sobre estoutra imaxe,
03:50
creating something
that we call a composite image.
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creando así o que chamamos
unha imaxe composta.
03:53
Much to our surprise,
we only required 50 --
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Para a nosa sorpresa, fixeron falla só 50,
03:56
I repeat, only 50 --
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insisto, só 50,
03:58
of these composite images to train
our algorithms to high efficiencies.
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desas imaxes compostas para adestrar
algoritmos ata taxas altas de eficiencia.
04:02
To summarize our approach,
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Para resumir o noso enfoque,
04:04
instead of using 10,000
very expensive medical images,
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no canto de empregar 10.000
imaxes médicas carísimas,
04:07
we can now train the AI algorithms
in an unorthodox way,
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agora podemos adestrar os algoritmos
de modo pouco convencional,
04:10
using only 50 of these high-resolution,
but standard photographs,
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usando só 50 destas fotografías,
de alta resolución pero estándares,
04:14
acquired from DSLR cameras
and mobile phones,
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tomadas con réflex dixitais
ou teléfonos móbiles,
04:17
and provide diagnosis.
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e obter un diagnóstico.
04:18
More importantly,
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E o que é máis importante,
04:19
our algorithms can accept,
in the future and even right now,
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os nosos algoritmos admiten,
no futuro e xa agora mesmo,
04:23
some very simple, white light
photographs from the patient,
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fotografías sinxelas, de luz branca,
feitas polo paciente,
04:25
instead of expensive
medical imaging technologies.
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no canto de tecnoloxías de imaxe médica
moi custosas.
04:29
I believe that we are poised
to enter an era
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Penso que estamos xa listos
para entrar nunha era
04:32
where artificial intelligence
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na que a intelixencia artificial
04:34
is going to make an incredible
impact on our future.
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vai ter un impacto incrible
no noso futuro.
04:36
And I think that thinking
about traditional AI,
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E en relación coa IA tradicional,
04:39
which is data-rich but application-poor,
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potente no manexo de datos pero
moi débil nas aplicacións,
04:42
we should also continue thinking
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deberíamos seguir pensando tamén
04:43
about unorthodox artificial
intelligence architectures,
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en arquitecturas de intelixencia
artificial pouco convencionais
04:46
which can accept small amounts of data
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que poden traballar con poucos datos
para resolver algúns
dos maiores problemas que temos hoxe,
04:48
and solve some of the most important
problems facing us today,
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04:51
especially in health care.
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sobre todo en sanidade.
04:52
Thank you very much.
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Moitas grazas.
04:54
(Applause)
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(Aplausos)
Translated by Mario Cal
Reviewed by Xusto Rodriguez

▲Back to top

ABOUT THE SPEAKER
Pratik Shah - Medical technologist
Dr. Pratik Shah creates novel intersections between engineering, medical imaging, machine learning and medicine.

Why you should listen

Dr. Shah's research program at the MIT Media Lab develops scalable and low-cost diagnostics and therapeutics. His ongoing research areas at MIT include: 1) artificial intelligence and machine learning methods for detection of cancer biomarkers using standard photographs vs. expensive medical images; 2) unorthodox artificial intelligence and machine learning algorithms to design optimal and faster clinical trials and to reduce adverse effects on patients; and 3) low-cost and open source imaging devices, paper diagnostics, algorithms and mobile phones to improve public health and generate real-world data.

Clinical studies with Pratik's medical technologies have revealed "missing sick" patients, who otherwise remain undiagnosed in conventional healthcare settings. Dr. Shah's graduate and postdoctoral research contributed to the discovery of a vaccine component to prevent pneumococcal (Streptococcus pneumoniae) diseases; the identification of new pathways, technologies and metabolites as antimicrobials to target gastrointestinal infections; and a nonprofit to deploy a low-cost water quality test for the developing world.

Past recognition for Dr. Shah includes the American Society for Microbiology's Raymond W. Sarber national award, the Harvard Medical School and Massachusetts General Hospitals ECOR Fund for Medical Discovery postdoctoral fellowship, the AAAS-Lemelson Invention Ambassador Award and a TED Fellowship. Pratik has been an invited discussion leader at Gordon Research Seminars; a speaker at Cold Spring Harbor Laboratories, Gordon Research Conferences and IEEE bioengineering conferences; and a peer reviewer for leading scientific publications and funding agencies. Pratik has a BS, MS, and a PhD in Microbiology and completed fellowship training at The Broad Institute of MIT and Harvard, Massachusetts General Hospital and Harvard Medical School.

More profile about the speaker
Pratik Shah | Speaker | TED.com

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