Descripción de las herramientas de inteligencia artificial, Deep Learning y Machine Learning empleadas en el aprendizaje del diagnóstico: Un nuevo enfoque en desarrollo de la valoración de signos y síntomas desde una revisión sistemática

Vista sencilla de ítem
dc.contributor.advisorIbañez Pinilla, Edgar Antonio
dc.contributor.authorLeon Chavez, Angel Fabian
dc.contributor.authorDuque Ortiz, Jesus David
dc.contributor.orcidLeon Chavez, Angel Fabian [0009-0003-1994-639X]
dc.date.accessioned2024-07-04T20:58:30Z
dc.date.available2024-07-04T20:58:30Z
dc.date.issued2024-05
dc.description.abstractIntroducción: La evolución tecnológica en el mundo ha planteado retos importantes para la humanidad. Con la llegada de la inteligencia artificial, se han generado diferentes dudas de cómo será la interacción que tengan los profesionales en las habilidades a desarrollar en las diferentes áreas del conocimiento, para de esta manera lograr capacidades profesionales enfocadas a la resolución de problemas. Objetivo: Describir los diferentes modelos de inteligencia artificial; Deep Learning y Machine Learning; empleados en el aprendizaje de la medicina del diagnóstico en medicina, enfocados en la evaluación de signos y síntomas para el diagnóstico de la enfermedad. Metodología: Se realizó una revisión sistemática acerca del uso de modelos de inteligencia artificial para la enseñanza del aprendizaje del diagnóstico a partir de la presencia de signos síntomas, aplicando la guía PRISMA, se aplicó la verificación de los parámetros propuestos en la guía JAMA, se analizado por dos evaluadores externos con la herramienta CASPe, se valoraron los sesgos con las herramientas ROBIS. Resultados: Se eligieron 19 artículos, 42,10% correspondieron a Metaanálisis, 52,63% a revisiones sistemáticas y 5,27% revisión de alcance. Se empleó el uso de AI para el diagnóstico de diferentes enfermedades en diferentes áreas del conocimiento médico, 13 artículos usaron modelos de ML y DL, el uso de regresión logística represento el 73,68%, la estadística Bayesiana represento el 36,84%, los modelos de AI tienen una precisión para el diagnóstico de las enfermedades en comparación con los métodos tradicionales de mayores al 80% alcanzando niveles de precisión hasta del 99%. La predicción de toxicidad en cáncer de cabeza y cuello por quimioterapia ROC área de 0,82 IC 95% (0,771-0,868) p(<0,001), competencias de trabajadores de la salud área ROC 0,96 IC 95% (0,954-0,976) p(<0,001), en cirugía vascular área ROC 0,795 (0,61-1.00) p(<0,001)(34), el diagnostico de Alzheimer área ROC de 0,96 IC 95% de 0,94- 0,97 p significativa(<0,029), la predicción de enfermedad cardiovascular ROC 0,843 IC95% (0,840-0,845) p(<0,001). Para el diagnóstico de Alzhéimer OR 1,42 IC 95% (1,15 – 1,76) p(<0,029), hipertensión arterial y enfermedad cerebrovascular OR de 10,85 IC 95% (4,74-24,83) p(<0,05) y OR 25,08 IC 95% (11,48-54,78) (<0,05). En contraste los OR de predicción para diabetes mellitus OR 0,09 IC 95% (0,048 – 0,167) p (<0,001). Conclusión: El análisis de datos y el manejo de la información ha permitido que los modelos de AI tengan la capacidad de desarrollar algoritmos para situaciones complejas como el diagnóstico de la enfermedad. Lejos de reemplazar la actividad humana facilitarán su desarrollo académico e investigativo. La docencia tiene la obligación de formar a los profesionales y en este contexto a los médicos en la manejo optimo y responsable de estas herramientas, integrándolos a la 9 didáctica y el currículo para consolidar habilidades clínicas como la detección de signos y síntomas, la integración con estudios bioquímicos y el incremento de su capacidad diagnostica al relacionar esta información con análisis imagenológico, radiológico y no radiológico.
dc.description.abstractenglishIntroduction: Technological evolution in the world has posed significant challenges for humanity. With the arrival of artificial intelligence, different doubts have arisen about how professionals will interact, and the skills needed to be developed in various fields of knowledge, to achieve professional skills focused on problem-solving. Objective: To describe the different models of artificial intelligence; Deep Learning and Machine Learning; used in teaching diagnostic medicine, focusing on evaluating signs and symptoms for disease diagnosis. Methodology: A systematic review was conducted on the use of artificial intelligence models for teaching diagnostic learning based on the presence of signs and symptoms, applying the PRISMA guideline, verifying the parameters proposed in the JAMA guideline, analyzed by two external evaluators with the CASPe tool, bias was assessed using the ROBIS tools. Results: Nineteen articles were selected, with 42.10% corresponding to Meta-analyses, 52.63% to systematic reviews, and 5.27% to scoping reviews. AI was used for diagnosing various diseases in different medical fields, with 13 articles using ML and DL models. Logistic regression was used in 73.68% of cases, Bayesian statistics in 36.84%. AI models have shown a precision for diagnosing diseases of over 80% compared to traditional methods, reaching precision levels of up to 99%. Prediction of toxicity in head and neck cancer due to chemotherapy had a ROC area of 0.82, workers' competencies in the health field had a ROC area of 0.96, and for vascular surgery, the ROC area was 0.795. Alzheimer's diagnosis had a ROC area of 0.96, cardiovascular disease prediction achieved a ROC area of 0.843. Odds ratio for Alzheimer's diagnosis was 1.42, while for hypertension and cerebrovascular disease were 10.85 and 25.08, respectively. Conversely, the odds ratio for predicting diabetes mellitus was 0.09. Conclusion: Data analysis and information management have allowed AI models to develop algorithms for complex situations such as disease diagnosis. Instead of replacing human activity, they will facilitate academic and research development. Education has the duty to train professionals, including medical professionals, in the optimal and responsible use of these tools, integrating them into teaching methods and curricula to enhance clinical skills such as symptom detection, integration with biochemical studies, and improved 10 diagnostic capacity through the correlation of information with imaging and non-imaging analysis.
dc.description.degreelevelEspecializaciónspa
dc.description.degreenameEspecialista en Docencia Universitariaspa
dc.format.mimetypeapplication/pdf
dc.identifier.instnameinstname:Universidad El Bosquespa
dc.identifier.reponamereponame:Repositorio Institucional Universidad El Bosquespa
dc.identifier.repourlrepourl:https://repositorio.unbosque.edu.co
dc.identifier.urihttps://hdl.handle.net/20.500.12495/12560
dc.language.isoes
dc.publisher.facultyFacultad de Educaciónspa
dc.publisher.grantorUniversidad El Bosquespa
dc.publisher.programEspecialización en Docencia Universitariaspa
dc.relation.references1. Randolph T. Artificial Intelligence: Work, Machines and Human Interaction [Internet]. New York: SNOVA; 2021. (Computer Science, Technology and Applications Ser). Available from: https://login.ezproxy.unbosque.edu.co/login?url=https://search.ebscohost.com/login.aspx?direct=tru e&db=nlebk&AN=3072406&lang=es&site=ehost-live
dc.relation.references2. Masters K. Artificial intelligence in medical education. Med Teach. 2019 Sep 2;41(9):976–80.
dc.relation.references3. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. Vol. 23, BMC Medical Education. BioMed Central Ltd; 2023.
dc.relation.references4. Azer SA, Guerrero APS. The challenges imposed by artificial intelligence: are we ready in medical education? BMC Med Educ. 2023 Sep 19;23(1).
dc.relation.references5. Komasawa N, Yokohira M. Simulation-Based Education in the Artificial Intelligence Era. Cureus. 2023 Jun 25;
dc.relation.references6. Rezazadeh H, Ahmadipour H, Salajegheh M. Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students. BMC Med Educ. 2023 Dec 1;23(1).
dc.relation.references7. Cohen B, DuBois S, Lynch PA, Swami N, Noftle K, Arensberg MB. Use of an Artificial Intelligence- Driven Digital Platform for Reflective Learning to Support Continuing Medical and Professional Education and Opportunities for Interprofessional Education and Equitable Access. Vol. 13, Education Sciences. Multidisciplinary Digital Publishing Institute (MDPI); 2023.
dc.relation.references8. Das D, Kumar N, Longjam LA, Sinha R, Deb Roy A, Mondal H, et al. Assessing the Capability of ChatGPT in Answering First- and Second-Order Knowledge Questions on Microbiology as per Competency-Based Medical Education Curriculum. Cureus. 2023 Mar 12;
dc.relation.references9. Ghosh A, Bir A. Evaluating ChatGPT’s Ability to Solve Higher-Order Questions on the Competency- Based Medical Education Curriculum in Medical Biochemistry. Cureus. 2023 Apr 2;
dc.relation.references10. Giannos P. Evaluating the limits of AI in medical specialisation: ChatGPT’s performance on the UK Neurology Specialty Certificate Examination. BMJ Neurol Open. 2023 Jun 15;5(1).
dc.relation.references11. Corsello A, Santangelo A. May Artificial Intelligence Influence Future Pediatric Research?—The Case of ChatGPT. Children. 2023 Apr 1;10(4).
dc.relation.references12. Jartarkar SR. Artificial intelligence: Its role in dermatopathology. Vol. 89, Indian Journal of Dermatology, Venereology and Leprology. Scientific Scholar; 2023. p. 549–52.
dc.relation.references13. Stachowicz-Stanusch A. Principles of Responsible Management Education (PRME) in the Age of Artificial Intelligence (AI): Opportunities, Threats, and the Way Forward [Internet]. Charlotte, NC: Information Age Publishing; 2021. (Research in Management Education and Development). Available from: https://login.ezproxy.unbosque.edu.co/login?url=https://search.ebscohost.com/login.aspx?direct=tru e&db=e000xww&AN=2953380&lang=es&site=eds-live&scope=site
dc.relation.references14. Shah P. AI and the Future of Education : Teaching in the Age of Artificial Intelligence [Internet]. Hoboken, New Jersey: Jossey-Bass; 2023. Available from: https://login.ezproxy.unbosque.edu.co/login?url=https://search.ebscohost.com/login.aspx?direct=tru e&db=edsebk&AN=3670862&lang=es&site=eds-live&scope=site
dc.relation.references15. Kaban A. Artificial Intelligence in Education: A Science Mapping Approach. International Journal of Education in Mathematics, Science and Technology. 2023;11(4):844–61.
dc.relation.references16. AI and the Future of Skills, Volume 1 [Internet]. OECD; 2021. (Educational Research and Innovation). Available from: https://www.oecd-ilibrary.org/education/ai-and-the-future-of-skills-volume- 1_5ee71f34-en
dc.relation.references17. Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross- sectional needs assessment. BMC Med Educ. 2022 Dec 1;22(1).
dc.relation.references18. Çalışkan SA, Demir K, Karaca O. Artificial intelligence in medical education curriculum: An e-Delphi study for competencies. PLoS One. 2022 Jul 1;17(7 July).
dc.relation.references19. Palencia-Díaz R, de Jesús Palencia-Vizcarra R, Rodolfo Palencia C. 419 www.medicinainterna.org.mx ANTECEDENTES El potencial de la inteligencia artificial para disminuir errores médicos y mejorar la educación médica continua The potential of artificial intelligence to reduce medical errors and improve continuous medical education. Available from: https://doi.org/10.24245/mim.v39i3.8934
dc.relation.references20. Zarei M, Eftekhari Mamaghani H, Abbasi A, Hosseini MS. Application of artificial intelligence in medical education: A review of benefits, challenges, and solutions. Medicina Clinica Practica. 2024 Apr 1;7(2).
dc.relation.references21. Bohler F, Aggarwal N, Peters G, Taranikanti V. Future Implications of Artificial Intelligence in Medical Education. Cureus. 2024 Jan 8;
dc.relation.references22. Gmj |, Vol X. Artificial Intelligence in Medical Education: Present Situation and Prospects for the Future.
dc.relation.references23. Grunhut J, Marques O, Wyatt ATM. Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum. JMIR Med Educ. 2022 Apr 1;8(2).
dc.relation.references24. Airaj M. Ethical artificial intelligence for teaching-learning in higher education. Educ Inf Technol (Dordr). 2024;
dc.relation.references25. Urrútia G, Bonfill1 X. EDITORIAL LA DECLARACIÓN PRISMA: UN PASO ADELANTE EN LA MEJORA DE LAS PUBLICACIONES DE LA REVISTA ESPAÑOLA DE SALUD PÚBLICA [Internet]. Available from: http://www.prisma
dc.relation.references26. Moher D, Liberati A, Tetzlaff J, Altman DG, Antes G, Atkins D, et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Vol. 6, PLoS Medicine. Public Library of Science; 2009.
dc.relation.references27. Guyatt G, Meade MO, Rennie D, Cook DJ. Users’ Guides to the Medical Literature.
dc.relation.references28. Rafael Santamaría Olmo Programa de Habilidades en Lectura Crítica Español (CASPe) - http://www.redcaspe.org/ Unidad de Gestión Clínica de Nefrología. Hospital Universitario Reina Sofía. Córdoba NefroPlus 2017;9(1):100-101
dc.relation.references29. Ciapponi, A. (2022). Herramientas ROBINS para evaluar el riesgo de sesgo de estudios no aleatorizados. Evidencia, Actualizacion En La práctica Ambulatoria, 25(3), e007024. https://doi.org/10.51987/evidencia.v25i4.7024
dc.relation.references30. Whiting P, Savović J, Higgins JP, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R; ROBIS group. ROBIS: A new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol. 2016 Jan;69:225-34. doi: 10.1016/j.jclinepi.2015.06.005. Epub 2015 Jun 16. PMID: 26092286; PMCID: PMC4687950.
dc.relation.references31. Araújo ALD, Moraes MC, Pérez-de-Oliveira ME, Silva VM da, Saldivia-Siracusa C, Pedroso CM, et al. Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis. Vol. 140, Oral Oncology. Elsevier Ltd; 2023.
dc.relation.references32. Azzopardi-Muscat N, Zapata T, Novillo-Ortiz D, Martinez Z, Zucoloto ML, Novillo-Ortiz D, et al. The global effect of digital health technologies on health workers’ competencies and health workplace: an umbrella review of systematic reviews and lexical-based and sentence-based meta-analysis [Internet]. Vol. 5, Review Lancet Digit Health. 2023. Available from: www.thelancet.com/
dc.relation.references33. Adebayo O, Bhuiyan ZA, Ahmed Z. Exploring the effectiveness of artificial intelligence, machine learning and deep learning in trauma triage: A systematic review and meta-analysis. Vol. 9, Digital Health. SAGE Publications Inc.; 2023.
dc.relation.references34. Li B, Feridooni T, Cuen-Ojeda C, Kishibe T, de Mestral C, Mamdani M, et al. Machine learning in vascular surgery: a systematic review and critical appraisal. Vol. 5, npj Digital Medicine. Nature Research; 2022.
dc.relation.references35. Baashar Y, Alkawsi G, Alhussian H, Capretz LF, Alwadain A, Alkahtani AA, et al. Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis. Comput Intell Neurosci. 2022;2022.
dc.relation.references36. Qu C, Zou Y, Ma Y, Chen Q, Luo J, Fan H, et al. Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta- Analysis. Vol. 14, Frontiers in Aging Neuroscience. Frontiers Media S.A.; 2022.
dc.relation.references37. Abd-Alrazaq A, AlSaad R, Harfouche M, Aziz S, Ahmed A, Damseh R, et al. Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis. Vol. 25, Journal of Medical Internet Research. JMIR Publications Inc.; 2023.
dc.relation.references38. Abdul NS, Shivakumar GC, Sangappa SB, Di Blasio M, Crimi S, Cicciù M, et al. Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta- analysis. BMC Oral Health. 2024 Dec 1;24(1).
dc.relation.references39. Chadaga K, Prabhu S, Sampathila N, Nireshwalya S, Katta SS, Tan RS, et al. Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review. Vol. 13, Diagnostics. Multidisciplinary Digital Publishing Institute (MDPI); 2023.
dc.relation.references40. Nabizadeh F, Masrouri S, Ramezannezhad E, Ghaderi A, Sharafi AM, Soraneh S, et al. Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review. Vol. 59, Multiple Sclerosis and Related Disorders. Elsevier B.V.; 2022.
dc.relation.references41. Altuhaifa FA, Win KT, Su G. Predicting lung cancer survival based on clinical data using machine learning: A review. Vol. 165, Computers in Biology and Medicine. Elsevier Ltd; 2023.
dc.relation.references42. Barrera FJ, Brown EDL, Rojo A, Obeso J, Plata H, Lincango EP, et al. Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review. Vol. 14, Frontiers in Endocrinology. Frontiers Media SA; 2023.
dc.relation.references43. Issaiy M, Zarei D, Saghazadeh A. Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models. Vol. 18, World Journal of Emergency Surgery. BioMed Central Ltd; 2023.
dc.relation.references44. Nadarajah R, Alsaeed E, Hurdus B, Aktaa S, Hogg D, Bates MGD, et al. Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis. Heart. 2022 Jul 1;108(13):1020–9.
dc.relation.references45. Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, et al. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol. 2022 Dec 1;22(1).
dc.relation.references46. Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, et al. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Vol. 14, Translational Psychiatry. Springer Nature; 2024.
dc.relation.references47. Rashid M, Ramakrishnan M, Chandran VP, Nandish S, Nair S, Shanbhag V, et al. Artificial intelligence in acute respiratory distress syndrome: A systematic review. Vol. 131, Artificial Intelligence in Medicine. Elsevier B.V.; 2022.
dc.relation.references48. Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Vol. 180, International Journal of Medical Informatics. Elsevier Ireland Ltd; 2023.
dc.relation.references49. Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Hosseinzadeh S, Sajadi Tabar SS. Smartwatches in healthcare medicine: assistance and monitoring; a scoping review. BMC Med Inform Decis Mak. 2023 Dec 1;23(1).
dc.relation.references50. Universitat Oberta de Catalunya [Internet]. Available from: http://oikonomics.uoc.edu
dc.relation.references51. Jesús Gonzalez-Hernandez I, Armas-Álvarez B, Coronel-Lazcano M, Vergara-Martínez O, Maldonado-López N, Granillo-Macías R. Ingenio y Conciencia Boletín Científico de la Escuela Superior Ciudad Sahagún El desarrollo tecnológico en las revoluciones industriales Technological development in industrial revolutions [Internet]. Vol. 8, Publicación semestral. 2021. Available from https://repository.uaeh.edu.mx/revistas/index.php/sahagun/issue/archive
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightshttps://purl.org/coar/access_right/c_abf2
dc.rights.localAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectEnseñanza
dc.subjectAprendizaje
dc.subjectSignos
dc.subjectSíntomas
dc.subject.ddc378.12
dc.subject.keywordsDeep learning
dc.subject.keywordsMachine learning
dc.subject.keywordsTeaching
dc.subject.keywordsLearning
dc.subject.keywordsSigns
dc.subject.keywordsSymptoms
dc.titleDescripción de las herramientas de inteligencia artificial, Deep Learning y Machine Learning empleadas en el aprendizaje del diagnóstico: Un nuevo enfoque en desarrollo de la valoración de signos y síntomas desde una revisión sistemática
dc.title.translatedDescription of artificial intelligence tools, Deep Learning and Machine Learning used in learning diagnosis: A new developing approach to assessment of signs and symptoms from a systematic review
dc.type.coarhttps://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttps://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.localTesis/Trabajo de grado - Monografía - Especializaciónspa

Archivos

Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
Trabajo de grado.pdf
Tamaño:
4.07 MB
Formato:
Adobe Portable Document Format
Descargar

Colecciones

Especialización en Docencia Universitaria