2178 lines
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2178 lines
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<span class="a2a_kit a2a_kit_size_32 addtoany_list" data-a2a-url="https://www.nibib.nih.gov/espanol/temas-cientificos/inteligencia-artificial-ia" data-a2a-title="Inteligencia Artificial (IA)"><a class="a2a_button_email"><span class="a2a_svg a2a_s_a2a a2a_s_email a2a-box"><img src="/themes/custom/nibib/assets/img/email-a2a.svg" border="0" alt="Facebook" width="27" height="27"></span></a><a class="a2a_button_facebook"><span class="a2a_svg a2a_s_a2a a2a_s_facebook a2a-box"><img src="/themes/custom/nibib/assets/img/facebook-a2a.svg" border="0" alt="Facebook" width="27" height="27"></span></a><a class="a2a_button_linkedin"><span class="a2a_svg a2a_s_a2a a2a_s_linkedin a2a-box"><img src="/themes/custom/nibib/assets/img/linkedin-a2a.svg" border="0" alt="Facebook" width="27" height="27"></span></a><a class="a2a_button_twitter"><span class="a2a_svg a2a_s_a2a a2a_s_x a2a-box"><img src="/themes/custom/nibib/assets/img/x-a2a.svg" border="0" alt="Facebook" width="27" height="27"></span
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|
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|
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<ul id="science-topic-anchor-tag-list">
|
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|
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<a href="#pid-9376">¿Cuáles son los tipos de IA y en qué se diferencian?</a>
|
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|
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<a href="#pid-9381">¿Cómo se usa la IA para mejorar la atención médica y la investigación biomédica?</a>
|
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|
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|
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<a href="#pid-9386">¿Cómo utilizan la IA los investigadores financiados por el NIBIB en su investigación biomédica?</a>
|
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|
||
<div class="field--item"> <div id="pid-9376" class="paragraph paragraph--type--science-topic paragraph--view-mode--full">
|
||
<h2>
|
||
|
||
<div class="field field--name-field-science-topic-title field--type-string field--label-hidden field__item">¿Cuáles son los tipos de IA y en qué se diferencian?</div>
|
||
</h2>
|
||
|
||
|
||
<div class="field field--name-field-science-topic-description field--type-text-long field--label-hidden field__item"><p><strong>Inteligencia Artificial:</strong> Una función con la cual las máquinas aprenden a realizar tareas, en lugar de simplemente hacer cálculos que son ingresados por usuarios humanos.</p>
|
||
<ul>
|
||
<li>Las primeras aplicaciones de IA incluían máquinas que podían jugar juegos como damas y ajedrez, y programas que podían reproducir lenguaje.</li>
|
||
</ul>
|
||
<div>
|
||
<figure role="group" class="align-right">
|
||
<img alt="Imagen del concepto de IA en múltiples industrias." data-entity-type="file" data-entity-uuid="d38d36f5-960a-44ae-b667-bad532e468f2" height="194" src="/sites/default/files/inline-images/AI%20600%20x%20400_0.jpg" width="291" loading="lazy"></p>
|
||
<figcaption>La IA está integrada en numerosas tecnologías que las personas usan diariamente. <em> Crédito: iStock-metamorworks</em></figcaption>
|
||
</figure>
|
||
<div><strong>Aprendizaje Automático:</strong> Un enfoque de la IA en el que se desarrolla un algoritmo de computadora (una serie de reglas y procedimientos) para analizar y hacer predicciones a partir de datos que se introducen en el sistema.</div>
|
||
<ul>
|
||
<li>Las tecnologías basadas en el aprendizaje automático se utilizan de forma rutinaria todos los días, como las fuentes de noticias personalizadas y los mapas de predicción de tráfico.</li>
|
||
</ul>
|
||
<p><strong>Redes Neuronales:</strong> Un enfoque de aprendizaje automático modelado a partir del cerebro en el que los algoritmos procesan señales a través de nodos interconectados llamados neuronas artificiales</p>
|
||
</div>
|
||
<ul>
|
||
<li>Imitando los sistemas nerviosos biológicos, las redes neuronales artificiales se han utilizado con éxito para reconocer y predecir patrones de señales neuronales involucradas en la función cerebral.</li>
|
||
</ul>
|
||
<p><strong>Aprendizaje Profundo</strong>: Una forma de aprendizaje automático que utiliza muchas capas de computación para formar lo que se describe como una red neuronal profunda, capaz de aprender de grandes cantidades de datos complejos y no estructurados.</p>
|
||
<ul>
|
||
<li>Las redes neuronales profundas son responsables de los asistentes virtuales controlados por voz, así como de los vehículos autónomos que aprenden a reconocer las señales de tráfico.</li>
|
||
</ul>
|
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</div>
|
||
|
||
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</div>
|
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</div>
|
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<div class="field--item"> <div id="pid-9381" class="paragraph paragraph--type--science-topic paragraph--view-mode--full">
|
||
<h2>
|
||
|
||
<div class="field field--name-field-science-topic-title field--type-string field--label-hidden field__item">¿Cómo se usa la IA para mejorar la atención médica y la investigación biomédica?</div>
|
||
</h2>
|
||
|
||
|
||
<div class="field field--name-field-science-topic-description field--type-text-long field--label-hidden field__item"><ul>
|
||
<li><strong>Radiología:</strong> La capacidad de la IA para interpretar los resultados de las imágenes puede ayudar a detectar un cambio minúsculo en una imagen que un clínico podría omitir por accidente.</li>
|
||
<li><strong>Imágenes:</strong> Un ejemplo es el uso de IA para evaluar cómo se verá un individuo después de una cirugía facial y de paladar hendido.</li>
|
||
<li><strong>Telesalud:</strong> Los dispositivos portátiles permiten el monitoreo constante de un paciente y la detección de cambios fisiológicos que pueden proporcionar señales de alerta temprana de un evento como un ataque de asma. </li>
|
||
<li><strong>Cuidados clínicos:</strong> Un gran enfoque de la IA en el sector de salud es en los sistemas de apoyo a la decisión clínica, los cuales utilizan observaciones de la salud y conocimiento de casos para ayudar con las decisiones de tratamiento.</li>
|
||
</ul>
|
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</div>
|
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</div>
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</div>
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<div class="field--item"> <div id="pid-9386" class="paragraph paragraph--type--science-topic paragraph--view-mode--full">
|
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<h2>
|
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|
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<div class="field field--name-field-science-topic-title field--type-string field--label-hidden field__item">¿Cómo utilizan la IA los investigadores financiados por el NIBIB en su investigación biomédica?</div>
|
||
</h2>
|
||
|
||
|
||
<div class="field field--name-field-science-topic-description field--type-text-long field--label-hidden field__item"><h3>Diagnóstico temprano de la Enfermedad de Alzheimer (EA) mediante el análisis de redes cerebrales</h3>
|
||
<p>La degeneración neurológica relacionada con la EA comienza mucho antes de que aparezcan los síntomas clínicos. La información proporcionada por los datos de neuroimagen de una IRM funcional (fMRI, por sus siglas en inglés), los cuales pueden detectar cambios en el tejido cerebral durante las primeras etapas de la EA, tiene potencial para la detección y tratamiento tempranos. Los investigadores están combinando la capacidad de la fMRI para detectar cambios cerebrales sutiles con la capacidad del aprendizaje automático para analizar múltiples cambios cerebrales a través del tiempo. Este enfoque intenta mejorar la detección temprana de la EA, así como otros trastornos neurológicos, incluyendo la esquizofrenia, el autismo y la esclerosis múltiple.</p>
|
||
<h3>Predicción de los niveles de glucosa en la sangre utilizando sensores portátiles </h3>
|
||
<p>Los investigadores financiados por el NIBIB están construyendo modelos de aprendizaje automático para mejorar el manejo de los niveles de glucosa en la sangre mediante el uso de datos obtenidos de sensores portátiles. Las nuevas tecnologías de detección portátiles proporcionan mediciones continuas que incluyen el ritmo cardiaco, la conductancia de la piel, la temperatura, y los movimientos del cuerpo. Los datos se utilizarán para entrenar una red de inteligencia artificial que ayude a predecir cambios en los niveles de glucosa en la sangre antes de que ocurran. El poder anticipar y prevenir los problemas de control de glucosa en la sangre mejorará la seguridad del paciente y reducirá las costosas complicaciones.</p>
|
||
<div>
|
||
<figure role="group" class="align-left">
|
||
<img alt="Representación artística de una red neuronal profunda." data-entity-type="file" data-entity-uuid="f3cdbb2e-215f-4d17-8880-d13cb055bb3f" height="231" src="/sites/default/files/inline-images/una%20l%C3%A1mina%20de%20histopatolog%C3%ADa.jpg" width="281" loading="lazy"></p>
|
||
<figcaption>Representación artística de una red neuronal profunda reconstruyendo una lámina de histopatología para revelar características microscópicas del tejido humano. <em>Crédito: Laboratorio Ozcan, UCLA</em></figcaption>
|
||
</figure>
|
||
<h3>Análisis de imagen optimizado para una mejor detección del cáncer colorrectal</h3>
|
||
<p>Este proyecto intenta desarrollar un sistema avanzado de escaneo de imágenes con una alta sensibilidad de detección muy enfocada a los cánceres de colon. Los investigadores desarrollarán redes neuronales profundas que pueden analizar un campo más amplio en las imágenes radiográficas obtenidas durante una cirugía. Estos escaneos más amplios abarcarán las áreas lesionadas sospechosas y más tejido circundante. Las redes neuronales compararán las imágenes del paciente con imágenes de casos diagnosticados en el pasado. Se espera que este sistema supere a los sistemas actuales asistidos por computadora en el diagnóstico de lesiones colorrectales. La adopción generalizada podría mejorar la prevención y el diagnóstico temprano del cáncer<span lang="ES" style="font-size:11.0pt"><span style="line-height:107%"><span style="font-family:"Arial",sans-serif"><span style="color:black">.</span></span></span></span></p>
|
||
</div>
|
||
<h3>Ropa inteligente para reducir el dolor lumbar</h3>
|
||
<p>Se está desarrollando ropa inteligente de asistencia ciber-física (CPAC, por sus siglas en inglés) en un esfuerzo por reducir la alta prevalencia del dolor lumbar. Las fuerzas en los músculos de la espalda y los discos que ocurren durante las tareas diarias son los principales factores de riesgo para el dolor de espalda y las lesiones. Los investigadores están recopilando una serie de datos públicos de más de 500 movimientos, medidos de cada sujeto, para informar un algoritmo de aprendizaje automático. La información se utilizará para desarrollar ropa de asistencia que pueda detectar condiciones inseguras e intervenir para proteger la salud de la espalda baja. La visión a largo plazo es crear ropa inteligente que pueda monitorear la carga lumbar; entrenar patrones de movimiento seguros; ayudar directamente a los usuarios a reducir la incidencia del dolor lumbar; y reducir los costos relacionados con los gastos de atención médica y el trabajo perdido.</p>
|
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</div>
|
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<h2 class="block-title" >View and Download Science Topic Fact Sheets - Spanish</h2>
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