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Inteligencia artificial como recurso didáctico para la enseñanza de Imagenología dentomaxilofacial

Artificial intelligence as a didactic resource for the teaching of Dentomaxillofacial Imaging=

 

1

Verónica Ivanova Verdugo Tinitana                     https://orcid.org/0000-0002-8235-056X<= o:p>

Universidad Católica de Cuenca, Az= ogues, Ecuador

vverdugo@ucacue.edu.ec

2

Sandra Elizabeth Mena Clerque                          

Universidad Católica de Cuenca, Azogues, Ecuador

sandramena@ucacue.edu.ec

 

 

=  

 

 

 

 

 <= /p>

 <= /p>

 

 

Artículo de Investigación Científica y Tecnológica<= o:p>

Enviado: 07/06/2022

Revisado: 27/07/2022

Aceptado: 08/08/2022

Publicado:26/08/2022

DOI: https://doi.org/10.33262/exploradordigital.v6i3.1.2276   

 

 

 

Cítese:

 

&nbs= p;

Verdugo Tinitana, V. I., & Mena Clerque, S. E. (2022). Inteligencia artificial como recurso didáctico para la enseñanza de Imagenología dentomaxilofacia= l . Explorador Digital, 6(3.1), 91-108. https://doi.org/10.33262/exploradordigital.v6i3.1.2= 276

 

 

 

=  

= EXPLORADOR DIGITAL, = es una Revista electrónica, Trimestral, que se publica= rá en soporte electrónico tiene como misión contribuir a la &nbs= p; formación de profesionales competentes con visión humanística y crít= ica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://exploradordigital.org La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registra= da en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec<= /span>

 <= /o:p>

 

 

 

= Esta revista está protegida bajo una licencia Creative Commo= ns AttributionNonCommercialNoDerivatives 4.0 International. Copia de la licencia: .=

 

Palabras claves:

Entornos virtuales, aprendizaje activo, sistema educativo,

enseñanza,

imagenología<= /span>

 

Resumen

El objetivo de la investigación fue determinar las expectativas de los estudiantes frente al uso de la inteligencia artificial de imagenología dentomaxilofacial Materiales y métodos La presente estudio es una investigación descriptiva, cuantitativa actual, la población de esta investigación tuvo la  participac= ión de  283 personas, 183 estudiantes de Odontología la Universidad Católica de Cuenca de cuarto ciclo y octavo ci= clo, específicamente quienes cursaron la asignatura imagenología en proporcion= es prácticamente iguales. Por otra parte, participaron radiólogos, de ellos quienes únicamente ejercían la radiología y radiólogos docentes. La información recolectada de radiólogos y radiólogos docentes. Resultados escaso conocimiento por parte de estudiantes y docentes el tema, porcenta= jes elevados en lo referente a estrategia de enseñanza aprendizaje. Bajo nive= l de que el radiólogo será remplazado por la inteligencia artificial. Poca familiaridad con el tema, acotación de la inteligencia artificial como estrategia de enseñanza y aprendizaje de forma positiva tanto para el profesional como para los estudiantes en el ámbito anatómico como patológ= ico y el que el radiólogo no será remplazado por la Inteligencia Artificial.<= o:p>

 

Keywords: Virtual environments, active learning, educational system,

teaching,

imaging

 

Abstract

The objective of the research was to determine t= he expectations of the students regarding the use of artificial intelligence= for dentomaxillofacial imaging Materials and methods This study is a descript= ive, current quantitative research, the population of this research had the participation of 283 people, 183 students of Dentistry at the Catholic University of Cuenca in fourth cycle and eighth cycle, specifically those= who took the imaging subject in practically equal proportions. On the other h= and, radiologists participated, including those who only practiced radiology a= nd teaching radiologists. Information collected from radiologists and teaching radiologists. Results Little knowledge by students and teachers of the subject, high percentages in relation to teaching-learning strategy. Low level that the radiologist will be replaced by artificial intelligence. Little familiarity with the subject, limiting artificial intelligence as a teaching and learning strategy in a positive way for both the professional and the students for the anatomical and pathological field and that the r= adiologist will not be replaced by Artificial Intelligence.

 

 

Introducción.

El termino de inteligencia artificial data desde el año de 1956   por J. McCarthy en donde formo parte d= e la informática la cual se encargaba de diseñar maquinas que tenían capacidades= de simular actividades que los seres humanos pueden hacer, y que son descritas como inteligentes (Gross, 1992). Herrera y Muñoz (1992) definen a la inteligencia artificial como la construcción de modelos computacionales que desempeñan u= na labor que requieren de un atributo, que sea capaz de comunicarse en la mism= a lengua y con igualdad de competencias que los interlocutores humanos.

En = el campo de la educación Gross (1992) manifiesta que las técnicas de inteligen= cia artificial varían según los objetivos que se desea, lo cual se puede distin= guir dos grandes aristas, el primero destinado al aprendizaje del estudiante mediante micromundos y el uso de tutores inteligentes y el segundo destinad= o al desempeño de tareas del docente en las cuales menciona la planificación, el= diseño y organización.

La inteligencia artificial en la actualidad aún no es muy conocida es por esta= razón que entidades como la Nemitz (2021); UNESCO (2018) e ISO/IEC (2019) estable= cen terminología relacionada con la misma y con ciencias afines   tales como:  aprendizaje automático (Machine learning), aprendizaje profundo (Deep learning), inteligencia artificial fuerte, inteligencia artificial débil, macro datos = o inteligencia de los datos [Big Data] y red neuronal (García Peña et al., 2020).

El desarrollo de varios recursos formativos digitales, han contribuido con var= ias soluciones para los procesos de enseñanza, la complejidad en su uso o el desconocimiento de los mismos al momento de emplearlos en la didáctica, ha expuesto los esquemas de metadatos, que aplicables a la odontología se vuel= ven herramientas necesarias que van a permitir condesar la información de las historias clínicas de los pacientes, y poder llevar un control adecuado

Builes (2008= ), manifiesta que la clasificación de l= as herramientas educativas, que se han venido utilizando en los procesos del mejoramiento que se vienen usando para el mejoramiento del razonamiento que= ha tomado, como base, casos propios de cada ciencia.  Los procesos que se han venido bajo tér= minos de uso de tecnología y programación a través de la eficiencia y eficacia que algunas herramientas tecnológicas a través del uso de computadoras.  contribuirá a la descripción de los rec= ursos educativos y didácticos para la enseñanza de Imagenología dentomaxilofacial.

La Inteligencia Artificial es un tema vigente que tiene muchos aspectos coadyuvantes a los procesos tecnológicos; considerando que la media de la comunidad educativa comprende lo mínimo de este. Al respecto, Miailhe y Lannquist (2018) manifiestan que el conglomerado ciudadano de la llamada "aldea mundo" esta situado de una forma no adecuada en relación al uso de las tecnologías que se utilizan en la Inteligencia Artificial y omit= en los efectos y riesgos que podrían darse con cada adelanto que se vaya dando= en relación a esta. Expresado con propiedad se podría entender que el riesgo s= ocio- económico, o posibles desastres por la autonomía que podrían desarrollar las maquinas, como se elucubra acerca de lo que sucederá en el futuro, es decir= los puntos de vista distópicos o mundos imaginarios sobre la inteligencia  artificial; de acuerdo a los avances tecnológicos  que se presentan alre= dedor de la IA no necesitarán de un futuro muy lejano si no que la  contrario cada día las nuevas herramientas  que se consiguen y se diseñan están empezando a suplir desde la mano de obra en una fábrica hasta  empezando hacer ensayos sobr= e su propia programación.

Algo característico y que preocupa en parte es que la interacción en tiempo real, las secuelas de alteraciones posibles que surjan de la aplicabilidad de la = IA será, ciertamente un desafío en el trascurrir del tiempo y su normalidad por deci= r la cotidianidad de las actividades que surjan de la IA, (Diéguez, 2017); todo = esto hará  minimizar el impacto de la  revolución industrial, en relación a lo que  estamos empezando a vivir con = la IA. (Miailhe y Lannquist, 2018).

La utilidad de la IA es amplia, actualmente es utilizada por áreas, como: informática y robótica y esta aplicada a la medicina humana en casi todos s= us especialidades, dando connotaciones importantes avances que podemos decir q= hay ocasiones que  parecerá que cada nu= evo avance es como si quisiéramos “jugar a ser dioses” (Vázquez, Jara, Riofrio,= y Teruel, 2018); otra área interesante es la sociales y los   aportes a las ciencias administrativas y manufactureras, donde la interacción en ti= empo real de transacciones requiere procesar enorme bases de datos que se están implementando en base a la IA.

(Mi= ailhe, 2018). En lo concerniente al segmento económico y sus consecuencias, se tie= ne la firme intención de las empresas líderes que desarrollan la IA, ya que su tendencia es establecerse en el mercado mundial; pero bajo un esquema de ac= ceso de datos organizados en el orbe digital, sobre el tiempo real al que se gen= eran datos en todas las áreas a través , de un personal apto y capacitado para  tener mayores beneficios; es decir el d= e los programadores, técnicos y conocedores en ciertos casos hasta empíricos han podido diseñar e implementar algunos algoritmos de aprendizaje impensado y = todo el conjunto de técnicas que se derivan de estas aplicaciones. Para algunos autores estos cambios tecnológicos y sus usos les han llevado a denominar c= omo "cuarta revolución industrial" (Corvalán, 2017) o el "quinto dominio"= ; (Saavedra, 2016).

Seg= ún Saavedra (2016), lo planteado desde el conocimiento de la inteligencia estratégica, las permutas son evidentes a partir de un método que amalgama = la tecnología robótica, computacional y digital basada en IA, que se convertir= á en un catalizador de cambios más prósperos para la humanidad. En todo este asp= ecto tratado hay un aspecto crucial que viene a ser el mecanismo de regulación, = evitando que se vulnerabilice a la población que estará inmiscuida en los procedimie= ntos que se deriven del uso de IA.

Seg= ún Ocaña-Fernández et al. (2019)   manifiesta que los educandos forman parte de los nativos digitales y= que están familiarizados  con la tecnol= ogía, formatos de transmisión de data  y plataformas interactivas y que el docente se encuentra dentro del grupo de migrantes digitales, el cual ha tenido que adaptarse, al advenimiento de nu= evas tecnologías, por lo descrito se plantea que el currículo debe diseñarse,  en un mundo de  interconexión digital en donde la Inteligencia Artificial  (IA) se de= be de establecer cómo desarrollarla  de a= cuerdo a los entornos multivariados  como = es países en desarrollo y en subdesarrollo y de acuerdo al tipo de institución superior educativa sea pública o privada.

Tuo= mi (2018) referente al impacto de la IA  en el aprendizaje opina que será tanto positivo como negativo, debido al conti= nuo cambio en donde los tecnólogos transfieren sus experiencias  acerca de los aprendizajes sus diseños = , y que los sistemas actuales  transfie= ren de aprendizaje en IA transmiten conocimientos a la mente del alumno es por esto que es necesario que los procesos de aprendizaje se incorporen de diferente manera ya que se considerada como el desarrollo de habilidades y competenci= as, lo cual está vinculado  con la ense= ñanza ya que se orienta hacia la   prospe= ctiva sobre el futuro del aprendizaje.

Sil= cox (2020) menciona que la IA, es un software que está destinada a realizas tareas que hacían los humanos en el campo de la salud a través de softwares especialme= nte para el monitoreo de la salud especialmente en tiempos de pandemia para detección de COVID-19 a través de imágenes radiográficas y ver si su desemp= eño a largo plazo y determinar si cumple sus funciones optimas en el desarrollo= de cada una de las actividades destinadas de acuerdo para lo que fue programa = da la IA. <= /o:p>

Gor= ospe-Sarasúa et al. (2022) tribuye que la inteligencia artificial es un reto dentro del = área de formación radiológica ya que se proporciona una retroalimentación inmedi= ato en el estudiante haciendo que la materia se vulva más atractiva  ya que esto vendría  hacer un aprendizaje basado en problema= s, donde el estudiante juega un papel de manera más interactiva y de la misma manera se evalúa  de forma continua= ,  de acuerdo a las competencias que desar= rolle el estudiante pero teniendo en cuenta que los docentes deben de estar capac= itado para el uso de dicha tecnología y poder desarrollar  de manera  mejorar la atención del paciente  y la formación de los estudiantes es por eso que en esta investigaci= ón se plantea, determinar las expectativas de profesionales de radiología y docentes radiólogos frente al uso de la inteligencia artificial como herramienta para uso profesional y como herramienta de enseñanza de imagenología dentomaxilofacial.

Valdés, (202= 1) considerara que la Inteligencia Artificial como “un instrumento para mejorar la Radiología”, que la utilidad de l= a IA en la rama de la Radiología tiene aspectos fundamentales, que radican en automatizar procesos que llevan mucho y que yendo a la eficacia del uso de = una maquina estas pueden dar buenos resultados, así por ejemplo puede ser el ca= so de una segmentación de lesiones o estructuras que sean necesarias ser analizadas para una intervención médica. Otro aspecto, es la posibilidad de obtener información imperceptible al ojo humano, lo que podrá evitar una intervención errónea.  E= s decir, se podría tener un diagnóstico más amplio, que podría detectar tempranamente anomalías, o mejorar el diagnóstico en una patología odontológica.

Sie= ndo una especialidad la radiología con un grado de complejidad considerable que determina un buen diagnóstico clínico, en la que el profesional de la imagen logia realiza diferentes actividades complejas con un grado de competencias= en varias áreas. Para la prescripción de una patología el disponer de herramient= as modernas que mejoren su desplazamiento es un coadyuvante al mejoramiento de los pacientes. Es por eso que en el área de la salud humana siempre vamos a referirnos a una inteligencia asistida, como parte de un cuadro en el que l= os profesionales pondremos como herramientas que permitirán agilitar las tareas y eviten rep= etirlas, con posibilidad de error, y que nos permitan hacer lo que debemos hacer: pe= nsar en el paciente y ver cada caso de forma global", ha argumentado.<= /o:p>

Res= pecto a las complicaciones que tienen los radiólogos con el establecimi= ento de la Inteligencia Artificial, Valdés, (2021), ha explicado que así exista multidisciplinaridad, en el desarrollo de estas herramientas el aplicar en la práctica clínica, conlleva un desafío enorme = y la incertidumbre de los profesionales. Implementar un sistema de IA es un trab= ajo de mucha importancia en la validación y no todos quienes brindan servicios de Radiología disponen de conocimientos o los medios suficientes, para sacar el mejor provecho a estas herramientas.

La consideración acerca del diseño de estos sistemas y la especificidad de la información que utilizan son demasiado específicos, y por ende su programac= ión debe ser idónea, y cualificada con la información necesaria y suficiente pa= ra dar diagnósticos precisos.

Valdés, (202= 1)  manifiesta que la aplicación de la "están mejorando año tras año y se es= tán superando muchos problemas técnicos (como problemas en la fase de aprendiza= je, los sesgos, las 'cajas negras')". A pesar de esto los radiólogos ven "la dificultad" que se les puede presentar, con lo complejo de los sistemas de información y el ingreso de datos para obtener resultados de calidad. Es de ahí que es necesario que se realice una formación específica para contar con personal capacitado diferentes discipl= inas en donde confluyan los servicios clínicos que se integren en los servi= cios clínicos.

Valdés, (202= 1), ha manifestado que el error en la utilización de la IA es "no razonar sobre la diferencia existente entre un prototipo y un sistema ya listo y probado para ser utiliz= ado en la atención médica.

La existencia en el uso de la IA en Radiología, y los cuestionamientos éticos = de algunos profesionales conservadores, hace que se mantenga reservas sobre el= uso de estas herramientas, Valdés, (202= 1) ha explicado que los principales problemas= a los que se enfrentan en este campo son la pertenencia de los datos y l= a independencia del paciente, el compromiso de responsabilidad y los conflictos de equidad.=

En lo referente a la utilización de la IA, las radiografías (panorámica o periapicales) ensayaron con diferentes tipos de IA entre ellas: deep CNN para identificar los impla= ntes dentales los que tuvieron una exactitud de 93,8% Hadj Saïd M, (2020), el Yolov-3, Tensor Flow and Keras deeplearning mostró precisión de 0,51-0,85 d= ependiendo del sistema de implante utilizado Takahashi T, (2020).  Dentro del establecimiento los factores= que intervienen en el pronóstico del implante estableciéndose que la posición mesiodistal del mismo es determinante.

Exi= sten varios estudios  en donde se habla = acerca de las percepciones por parte de los estudiantes y los profesionales acerca= de la inteligencia artificial  y su aplicación en el campo de la radiología  y cómo influye en el proceso de aprendizaje del mismo ya que ha dado buenas expectativas el uso de la misma creando en el estudiante interactividad  realizando una retro alimentación y así su conocimiento aun no este enmarcado en el estudiantado tiene buenas expectativas por parte de los mismos, y por qué no decir  por parte de los profesionales de la radiología teniendo en cuenta que este sería  el advenimiento de una nueva tecnología  y la cual debemos de aptarnos para que el proceso de aprendizaje pue= da mejorar  como lo manifiesta Caparrós-Galán y Sendra-Portero (2021)  por todo lo descrito anteriormente el objetivo de la presente investigación es Determinar las expectativas de los estudiantes frente al u= so de la inteligencia artificial de imagenología dentomaxilofacial.=

Metodologia= .

El presente estudio es una investigación descriptiva, cuantitativa actual, la población de esta investigación tuvo la participación de 283 personas, 183 estudiantes de Odontología la Universidad Católica de Cuenca de cuarto ciclo y octavo ciclo, específicamente quienes = cursaron la asignatura imagenología en proporciones prácticamente iguales. Por otra parte, participaron 100 radiólogos, 51 de ellos quienes únicamente ejercían= la radiología y 49 era radiólogos docentes. La información recolectada de radiólogos y radiólogos docentes.

Referente al instrumento   utilizamos dos instrumentos unos para estudiantes y el otro para radiólogos los cuales estuvieron formados la pregunta número 1 se refería a la familiaridad del tema y las demás pregunt= as eran relacionadas a la expectativa, la valoración de las preguntas 1 fue mediante frecuencias y las demás preguntas determinando los promedios de las mismas. En relación al alfa de Cronbach nos dio un resultado en estudiantes= de: 0.879 y en el de radiólogos del 0.823 siendo un valor positivo encontrado en nuestros instrumentos

Los datos estadísticos se expresan mediante frecuencias porcentuales, además cada una de las percepciones mediante medi= das de tendencia central y dispersión, el procesamiento de datos fue realizado = en el paquete estadístico SPSS versión 20 y la edición de tabla y gráficos en Excel 2019. El comportamiento de los datos según la prueba Kolmogorov Smirn= ov (K-S) resulto no normal (p<0.05) por lo que se tomó la decisión de aplic= ar una prueba no paramétrica (U- Mann Whitney) para la comparación de percepci= ones y expectativas entre radiólogos y radiólogos docentes. Las decisiones fueron tomadas con una significancia del 5% (p<0.05) (Guamán-Duy & Ochoa-En= calada, 2021).

Resultados.=

En la investigación participaron 283 personas, 183 estudiantes de Odontología la Universidad Católica de Cuenca de cuarto ciclo y octavo cicl= o, específicamente quienes cursaron la asignatura imagenología en proporciones prácticamente iguales. Por otra parte, participaron 100 radiólogos, 51 de e= llos quienes únicamente ejercían la radiología y 49 era radiólogos docentes. La información recolectada de radiólogos y radiólogos docentes se concentró en diferentes países de Latinoamérica principalmente Ecuador, Chile, México y = Perú los detalles se pueden observar en la tabla 1.

Tabla 1.

Característica de los participantes.

 

n

%

Ciclo cursado por los estudiantes<= /span>

Cua= rto ciclo

90

49,2

Oct= avo ciclo

93

50,8

Rol profesional

Rad= iólogo

51

51,0

Rad= iólogo - Docente

49

49,0

País de residencia de los radiólogos

Arg= entina

2

2,0

Bol= ivia

2

2,0

Chi= le

19

19,0

Col= ombia

9

8,0

Cos= ta Rica

7

7,0

Ecu= ador

10

10,0

Esp= aña

5

5,0

Gua= temala

1

1,0

Méx= ico

12

12,0

Pan= amá

2

2,0

Tabla 1.

Característica de los participantes (continuaci= ón)

 

 

n

%

Paí= s de residencia de los radiólogos

Per= ú

23

23,0

Rep= ública dominicana

2

2,0

Ven= ezuela

6

6,0

Fuente: Elaboración propia

En la figura 1 se observa que el 27% de los estu= diantes han desconocen del término inteligencia artificial, indicando una posible necesidad de familiarizarse con este concepto y se lo pueda aplicar con fin= es educativos.

Figura 1.

Familiarización con el término de “Inteligencia artificial”

Fuente: Elaboración propia

En cuanto a las percepciones de los estudiantes en torno a la inteligen= cia artificial se pueden observar en la tabla 2. que los estudiantes consideraban que la inteligencia artificial sería de gran utilidad en el ámbito de la anatomía radiográfica y el radiodiagnóstico patológico.

Por otra parte, se determinó que los estudiantes, tienen una percepción= de mediana satisfacción con respecto al aporte del aprendizaje de la radiología desde la enseñanza tradicional como lo referencia la tabla 2.

 

Tab= la 2.

Percepciones estudiantes (Escala 1 -5 )

 

Media

DE

Percepción del aporte al aprendizaje desde la enseñanza tradicional.

3,4

1,1

Pertinencia de la incorporación de la inteligencia artificial como estrategia de aprendizaje en Imagenología 

4,1

1,1

Expectativa de mejora de aprendizaje con el uso de la inteligencia artificial.

4,1

1,0

Utilidad de la inteligencia artificial en el ámbito de anatomía radiográfica.=

4,3

1,0

Utilidad de la inteligencia artificial en el ámbito del radiodiagnóstico patológico

4,3

1,0

Expectativa de amplitud de visión con la implementación de la inteligencia artificial. 

4,25

1,04

Fuente: Elaboración propia

Para profundiz= ar lo más relevante de los resultados iniciales, en la figura 2 se puede obser= var la frecuencia porcentual de los estudiantes según respuestas específicas. Se determinó que más del 80% se encontraban muy de acuerdo con que la intelige= ncia artificial sería un gran aporte y prestaría grandes ventajas en su aprendiz= aje.

Figura 2.=

Utilidad de la inteligencia artificial en el aprendizaje de la anatomía radiológica y en e= l radiodiagnóstico patológico

Fuente: Elaboración propia

En la misma línea, la evaluación realizada a los radiólogos y docentes determi= nó que el 83% tenían un conocimiento muy bajo sobre la inteligencia artificial, pues conocían nada, casi nada, muy poco y poco del tema; dando entrever nuevamente la necesidad de plantear alternativas que potencien su aplicació= n en el campo educativo.

Figura 3.

Autopercepc= ión de conocimiento sobre la inteligencia artificial

Fuente: Elaboración propia

Finalmente, al comparar cada una de las percepciones y expectativas sob= re la inteligencia artificial se determina diferencias significativas (p < 0.05) destacándose que tanto radiólogos como los docentes determinaban como= muy elevada la utilidad de la implementación de la inteligencia artificial en el ámbito de la anatomía radiográfica, así como en el papel de innovación del aprendizaje a través de la inteligencia artificial en el campo de la imagenología.

Se determinó percepciones muy positivas frente al uso de la inteligenci= a artificial, pues en una media baja, los radiólogos y docentes consideran que la inteligencia artificial podría reemplazaría a los radiólogos en algún momen= to de tiempo.

En conjunto las respuestas terminaron una expectativa y actitud positiva frente a la implementación de la inteligencia artificial tanto dentro del c= ampo de aprendizaje como dentro de la aplicación práctica ver tabla 3.

 

 

 

 

Tabla 3.

Perce= pción de radiólogos y radiólogos docentes=

 

Radiólogo

Radiólogo - Docente

U

p

Media

DE

Media

DE

Se debería de utilizar la inteligencia artificial para la enseñanza de radiología dentomaxilofacial.

4,0

0,9

4,1

1,2

1043,0

0,128

La inteligencia artificial va revolucionará el aprendizaje en el campo de la radiología.

4,1

0,9

4,1

1,2

1095,5

0,255

Uti= lidad de la implementación de la inteligencia artificial en el ámbito de anatom= ía radiográfica.

4,3

0,8

4,1

1,4

1248,5

0,994

La inteligencia artificial mejora la capacidad de carga de trabajo y la eficiencia de los radiólogos.

4,0

1,1

4,0

1,1

1239,5

0,942

La implementación de la inteligencia artificial en el aprendizaje dentomaxilofacial en radiodiagnóstico patológico es de utilidad.

4,1

0,9

4,0

1,2

1213,5

0,791

La inteligencia artificial remplazara a los radiólogos.

2,0

1,2

1,9

1,2

1145,0

0,430

Fuente: Elaboración propia

Para finalizar y al ser más específicos la figura 4 muestra claramente = las expectativas referentes a la IA, donde se determina que aproximadamente el = 50% de participantes consideraron estar muy de acuerdo con que el uso de la inteligencia artificial sería una estrategia de utilidad y además innovaría= el aprendizaje de imagenología dentomaxilofacial, principalmente en la anatomía radiográfica.

Figura 4.

Enseñanza de la anatomía radiográfica y expecta= tiva de aprendizaje revolucionario

Fuente: Elaboración propia

Discusión

En la actualidad la aplicación de la IA en la radiología ha demostrado = que es de gran utilidad para el proceso de enseñanza aprendizaje y además para detectar ciertas lesiones que optimiza el tiempo a los profesionales y a la= vez crean en el estudiante una retroalimentación.

De acuerdo a la familiaridad de los estudiantes con el termino  de inteligencia artificial tenemos un porcentaje del 63% datos que concuerdan con el estudio  de Caparrós-Galán y Sendra-Portero (202= 1) esto puede deberse que al momento aún no se está utilizando la IA y que exi= ste poco reporte además lo cual hace hincapié con lo  que manifiestan los profesionales la po= ca familiaridad con el tema es decir que si no se  ha escuchado no se puede opinar por lo que se plantea la capacitació= n al profesional para poder estar a la vanguardia de la tecnología y así que el estudiante pueda ser más interactivo de la misma forma lo manifiesta Santos= et al. (2019).

De acuerdo a la expectativa de los estudiantes  en relación  al aprendizaje son alta en relación a la utilidad de anatomía radiográfica  y en ámbito del radiodiagnóstico patológico datos que concuerdan con estudios realizados por Aguirre et al. (2021) y guarda  alta similitud con los datos encontrados en el estudio de Santos et al. (2019) y Caparrós-Galán y Sendra-Portero (2021) además en el campo del diagnóstico patológico en el área radiológica se ha determinado varios estudios que han obtenido excelentes resultados tales como Chan y Zary (2019); Nolting y Gib= lin (2020); Machacado-Rojas y Aparicio-Pico (2021)de la misma forma esto atribu= ye que los profesionales  en el área d= e la radiología guarden similitud entre la expectativa del uso de la inteligencia artificial en las áreas antes mencionadas.

En relación a que si los profesionales serán remplazados Pues la presen= te investigación no concuerda con Caparrós-Galán y Sendra-Portero (2021) ya que en este grupo manifiestan que les preocupa que puedan ser remplazados Santos et al. (2019) manifiesta que se puede deberse al temor de usar la tecnología debido a que puede que el grupo esté formado por mayor cantidad de migrantes digitales.<= o:p>

Propuesta. =

Con base en los resultados obtenidos es de suma importancia establecer procedimientos que permitan la aplicación de la IA como estrategia metodoló= gica para le enseñanza de Imagenología dentomaxilofacial, a respecto se establec= e el siguiente procedimiento, mismo que se explica en la figura 5.

 

 

Figura 5.

IA como recurso didáctico de enseñanza de Imagenología dentomaxilofacial

Fuente: Elaboración propia

Análisis: Conocer el entorno social, educativo y tecnológico del estudiante es fundamental para establecer correctamente los posibles recursos de IA a diseñar e implementar en el pro= ceso educativo, puesto que en varias ocasiones se crean recursos educativos que = no son aplicados óptimamente, debido al desconocimiento o poco desarrollo de competencias tecnológicas que poseen los estudiantes.

Diseño: Una vez que se conoce el entorno donde se ejecutará los recursos de IA educativos, se deben crear los materiales de una forma pedagógica pensando en que el aprendizaje se centre= en el estudiante más no en el docente, en este sentido se pueden aplicar metod= ología como la PACIE, ADDIE, entre otras y recursos tecnológicos como Python, Java= que permitas generar objetos virtuales educativos a través de reconocimiento de imágenes o visión artificial.

Implementación: Aplicar= en el contexto educativo los recursos IA es el centro de la metodología propue= sta, donde el uso de estrategias educativas como el Aprendizaje Colaborativo, Ap= rendizaje Basado en Problemas y el Aprendizaje Basado en la Investigación toman un pa= pel preponderante para la ejecución efectiva de este cometido.

Evaluación: Los procesos evaluativos constantes permitirán realizar mejoras continuas a los diferentes aspectos propuestos, la evaluación formativa es decir en cada actividad realizada má= s la evaluación final o sumativa nos darán luces de los aprendizajes generados y= las posibles mejoras, es importante considerar evaluaciones como la autoevaluac= ión, coevaluación y heteroevaluación mismas que minimizarán el sesgo implícito en todo proceso evaluativo.

Conclusiones.

·         Los estudiantes de la carrer= a de Odontología no están muy familiarizados con el término Inteligencia Artific= ial, a pesar que varios de los procesos aplicados en su formación y profesión gi= ran en torno a este término, existe una confusión importante con este termino c= on respecto a la robótica médica.

·         Las expectativas de los estudiantes frente al aprendizaje con IA son altas, principalmente por la posible utilidad que se le podría dar en la asignatura de Anatomía radiográfica, concretamente en el ámbito de radiodiagnóstico patológico, pu= esto que la visión artificial que permite propiciar la IA posibilita establecer parámetros de aprendizaje que el ojo humano los percibe con mucha dificulta= d.

·         La mayoría de los radiólogos= y estudiantes están de acuerdo con que la IA es de utilidad para el aprendiza= je y diagnóstico patológico, más aún si se lo orienta desde el ámbito formativo, donde el estudiante desde su quehacer diario lo adopta como implícito en su formación y aplicación como futuro profesional.

·         La aplicación de la IA como = recurso didáctico de enseñanza de la asignatura de Imagenología dentomaxilofacial, permitirá que el estudiante tenga un acercamiento real con su futura profes= ión como Odontólogo y a la vez le proporcionará la posibilidad de ejecutar aprendizajes en nuevos e innovadores ambientes de enseñanza.

 =

Referencias bibliográficas.

Aguirre, F., Carballo, L., Gonzál= ez, X., & Gigirey, V. (2021). Inteligencia artificial aplicada a la imagen médica. Rev. Imagenol., XXIV(2), 48–57.

Caparrós Galán, G., & Sendra Portero, F. (2021). Medical students’ perceptions of the impact of artifici= al intelligence in radiology. Radiologia. https://doi.org/10.1016/j.rx.2021.03.006

Chan, K. S., & Zary, N. (2019= ). Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Revi= ew. JMIR Medical Education= , 5(1), e13930. https://doi.org/10.2196/13930

García-Peña, V. R., Mora Marcillo= , A. B., & ÁvilaRamírez, J. A. (2020). La inteligencia artificial en la educación. Brazilian Journal of Development, 6(7), 648–666. h= ttps://n9.cl/iwk4z

Gorospe-Sarasúa, L., Muñoz-Olmedo= , J. M., Sendra-Portero, F., & de Luis-García, R. (2022). Challenges of Radiology education= in the era of artificial intelligence. Radiologia, 64(1), 54–59. https://doi.org/10.1016/j.rx.2020.10.003

Gross, B. (1992). La inteligencia artificial y su aplicación en la enseñanza. Comunicación, Lenguaje y Educación, 4(13), 73–80. https://doi.org/10.1080/02147033.1992.10821001

Guamán-Duy, J., & Ochoa-Encalada, S. (2021). Educació= n, COVID-19 y familia en el proceso de aprendizaje en Educación General Básica. Revista Arbitrada Interdisciplinaria Koinonía, = 6(3), 259-280. https://n9.cl/efwuf

Herrera, L., & Muñoz, D. (199= 2). Inteligencia artificial y lenguaje natural. Lenguas Modernas, 19<= /i>, 157–165.

Machacado-Rojas, A. M., & Aparicio-Pico, L. E. (2021). Técnicas de inteligencia artificial aplicadas = al análisis de imágenes diagnóstico. Eco Matemático, 12(2), 100–= 111. https://doi.org/10.22463/17948231.3237

Nemitz, P. (2021). ISO/IEC. 2019.= La era de la Inteligencia Artificial. ISO Focus. 137 (1). Noviembre-Diciembre = 2019. ISSN 2310-7987. Nueva Sociedad, 294, 130–140. https://n9.cl/s= tndf

Nolting, T., & Giblin, T. (20= 20). Totalmente integrado mediante avances en la tecnología de escaneado. In = Aprendizaje Dental.

Ocaña-Fernández, Y., Valenzuela-Fernández, L. A., & Garro-Aburto, L. L. (2019). Inteligencia artificial y sus implicaciones en la educación superior. Propósitos y Representaciones, 7(2), 536–568. https://doi.org/10.20511/pyr2019.v7n2.274

Santos, D. P., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (20= 19). Medical students ’ attitude towards artificial intelligence : a multicentre survey. 19, 1640–1646.

Silcox-Christina. (2020). La inteligencia artificial en el sector salud Promesas y desafíos apalancando tecnología. https://n9.cl/fyn2g

Tuomi, I. (2018). Informe Resumen= : el impacto de la Inteligencia Artificial en el aprendizaje, la enseñanza y la educación. Intef, November, 47. https://doi.org/10.2760/12297

Valdés, P. = L. (2021). "La inteligencia artificial está cambiando todo, también la Radiología". Redaccion Mèdica. Obtenido de https://n9.cl/ak1ez

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

El artículo que se publica es de exclu= siva responsabilidad de los autores y no necesariamente reflejan el pensamiento = de la Revista Explorador Digital.

 


 

 

 

El artículo queda en propiedad d= e la revista y, por tanto, su publicación parcial y/o total en otro medio tiene = que ser autorizado por el director de la Revista Explorador Digital.

 

 


 

 

 

 

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ISSN: 2661-6831

Vol. 6 No. 3.1, pp. 91 – 108, agosto 2022
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                                      Entornos de Aprendizaje         Página 91 | 1

 

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