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survey · BMC pediatrics · la publicación, 13 may 2026 · gratis

La mitad de los padres no sabía qué pensar sobre la IA en el consultorio del niño

Una encuesta en Arabia Saudita preguntó a 254 padres. La mayoría no rechazó la idea, pero tampoco la respaldó, y casi seis de cada diez temían un consejo equivocado.

Versión breve · la versión detallada sigue, unos 7 min

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El estudio, de un vistazo
Quiénes
Padres que llevaban a sus hijos a la consulta pediátrica
Cuántos
254 padres
Dónde
Arabia Saudita
Cuándo
Del 7 al 13 de julio de 2024
Tipo de estudio
survey
Quién lo hizo
Johns Hopkins Aramco Healthcare, Arabia Saudita
El límite que importa
Un solo centro, dos clínicas, una semana; comunidad de ingresos medios y altos.
Qué esperaban los padres de la inteligencia artificial en pediatría
Ayudar a llegar al diagnóstico57%
Reducir las visitas a la clínica43%
Bajar los costos22%

Porcentaje de padres que mencionó cada expectativa; son expectativas declaradas, no resultados medidos, y cada padre podía mencionar más de una.

Si en la clínica donde atienden a su hijo empiezan a usar un programa así para ayudarlo a diagnosticar, ¿qué querría saber antes de decir que sí?
Lectura de weeklyAI
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Investigadores del Johns Hopkins Aramco Healthcare, en Arabia Saudita, hicieron una encuesta a 254 padres que llevaban a sus hijos a la consulta pediátrica. Fue durante una semana, del 7 al 13 de julio de 2024, en dos clínicas: una en Abqaiq y otra en Alahsa.

La inteligencia artificial, aquí, quiere decir programas de computadora que usan muchos datos para ayudar al médico a hacer un diagnóstico y a tomar decisiones sobre el tratamiento.

La mitad de los padres, el 50%, dijo que no estaba segura sobre si apoyar o no el uso de la inteligencia artificial en pediatría. El 44% apoyaba su uso en pediatría, y un 6% se oponía.

El mayor miedo era que la inteligencia artificial diera un consejo equivocado por culpa de datos malos. Un 58% de los padres lo mencionó. Un 18% mencionó que no se sabe por qué la máquina decide lo que decide. Un 17% habló de la privacidad.

Sobre el permiso, el 47% de los padres daría su consentimiento para que se usara. Un 18% solo lo permitiría en una emergencia. Un 12% se negó. Para el 87% era importante o muy importante saber quién responde por un error. Para el 87% era importante o muy importante saber quién responde por un error.

Los padres también tenían expectativas sobre lo que la inteligencia artificial podría hacer. El 57% creía que la inteligencia artificial ayudaría a llegar al diagnóstico. El 43% pensaba que reduciría las visitas a la clínica. El 22% esperaba que bajaría los costos. Son expectativas, no resultados medidos. El estudio no comprobó que nada de eso ocurriera.

Estas cifras tienen límites. Fue un solo centro de salud, con dos clínicas, durante una semana. La comunidad tiene ingresos medios y altos. El cuestionario no pasó por una validación completa después de las pruebas iniciales y la revisión de expertos. Así que esto no dice qué piensan los padres de su país ni qué dice su ley. Tampoco dice qué puede hacer una empresa con su cara, su voz o sus datos donde usted vive.

Si en la clínica donde atienden a su hijo empiezan a usar un programa así para ayudarlo a diagnosticar, ¿qué querría saber antes de decir que sí?

Qué significa para usted

Cuando en la clínica de su hijo le hablen de un programa así, pregunte para qué se usará y quién responde si se equivoca. Sepa que este estudio solo recogió lo que esperaban 254 padres en Arabia Saudita, no lo que después ocurrió, y que allí la mitad dudaba. La ley de su país es otra cosa y es lo que conviene revisar.

Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8

Quién pagó: El artículo declara que no se recibió financiación de ninguna fuente para este estudio, y los autores declaran no tener intereses en conflicto.

No tome esto como consejo médico profesional.

Versión detalladaLos pasajes copiados del artículo, las ilustraciones y cada fuente con cuánto leímos de ella · 1389 palabras · unos 7 minLeerla →Cerrar

Los padres saudíes y la IA en pediatría: apoyo tibio, dudas firmes

Una encuesta de 254 padres en una clínica de Arabia Saudita encontró que la mitad no estaba segura, y que la mayoría temía un consejo equivocado por datos incorrectos.

Así podría verse · ilustración generada por weeklyAI.watch, no es una fotografía

Un equipo de investigadores del Johns Hopkins Aramco Healthcare, en Arabia Saudita, preguntó a 254 padres que llevaban a sus hijos a la consulta pediátrica qué sabían y qué pensaban sobre el uso de inteligencia artificial en la medicina infantil. El estudio se hizo en dos clínicas, en Ahsa y Abqaiq, durante una sola semana, del 7 al 13 de julio de 2024. A los padres se les explicó que por inteligencia artificial se entendían aplicaciones informáticas avanzadas que usan grandes cantidades de datos para ayudar a los médicos a hacer diagnósticos y a tomar decisiones de salud. No se les preguntó por ninguna herramienta concreta. El trabajo no recibió financiamiento de ninguna fuente, y los autores declaran no tener conflictos de interés.

Los resultados dibujan una foto de cautela, no de entusiasmo. El 44 por ciento apoyaba el uso de la IA en pediatría, y solo el 14 por ciento la apoyaba con firmeza; la mitad, el 50 por ciento, no estaba segura, y el 6 por ciento se oponía1. Sobre el consentimiento, el 47 por ciento dijo que lo daría2. Las expectativas eran altas: el 57 por ciento esperaba que la IA ayudara a llegar al diagnóstico, el 43 por ciento que redujera las visitas a la clínica y el 22 por ciento que bajara los costos3. Es importante subrayar que esas cifras son lo que los padres esperaban, no lo que ocurrió: el estudio no midió si la IA mejoró un diagnóstico, redujo una visita o abarató nada.

Los temores fueron más nítidos que el apoyo. Al 58 por ciento le preocupaba que la IA diera un consejo equivocado por datos incorrectos; al 18 por ciento, que no se supiera por qué se tomaba una decisión; y al 17 por ciento, la privacidad4. El consentimiento informado, la autonomía y la rendición de cuentas fueron calificados como muy importantes por la mayoría de los participantes5. El estudio también encontró una asociación entre ser un padre joven y estar dispuesto a dar el consentimiento6. En cambio, no encontró relación entre el nivel educativo y el apoyo a la IA7. Tampoco encontró relación entre la educación y el consentimiento. Una mayor confianza en el propio conocimiento sobre IA se asoció, de forma débil, con mayor apoyo8.

Conviene leer estas cifras con cuidado. Los participantes venían de una comunidad de ingresos medios y altos, lo que puede sesgar la muestra9. Todo ocurrió en una sola institución, en dos clínicas, durante una semana10. La selección de participantes fue por conveniencia, no al azar11. El cuestionario no fue validado del todo12. Y como muchos lo llenaron a mano, pudo haber errores de autorreporte. Todo esto significa que estos porcentajes son una fotografía de un momento y un lugar, no una medida en la que usted pueda basar una decisión.

Para ponerlo en perspectiva, otros trabajos que leímos, y de los que solo pudimos leer el resumen porque el texto completo está detrás de una suscripción, muestran que esta desconfianza no es exclusiva de los padres. En América Latina, una encuesta a 123 profesionales de urgencias de 16 países encontró que la familiaridad con la IA generativa era de moderada a alta, y que su uso profesional era común13. Sus principales preocupaciones eran la desinformación y el deterioro del juicio clínico14. Es decir, ni siquiera quienes la usan a diario dejan de temerle.

En Europa, una encuesta de la Organización Mundial de la Salud a 50 países encontró que la gobernanza de la IA en salud está poco desarrollada: solo cuatro países tenían una estrategia sanitaria específica para IA, y menos de diez por ciento había desarrollado normas sobre responsabilidad en caso de error1516. El propio informe concluía que se necesitan mecanismos legales y de política adaptables17. Esto importa porque, si ni los sistemas de salud más formalizados tienen claro quién responde cuando una herramienta automática se equivoca, es razonable que una familia tampoco lo sepa.

Así podría verse · ilustración generada por weeklyAI.watch, no es una fotografía

También leímos, solo en su resumen, una evaluación de un sistema de IA conversacional sobre Parkinson desplegado en Alemania durante sus primeros 129 días, que analizó más de dos mil conversaciones18. La clasificación automática marcó como buenas casi nueve de cada diez, pero una revisión humana de casos marcados encontró cinco eventos críticos1920. Y al revisar al azar conversaciones que la máquina había calificado como buenas, los expertos hallaron errores clínicos graves que la clasificación automática no había detectado21. Su conclusión fue que un sistema bien delimitado puede lucir bien en las métricas automáticas y aun así esconder fallas críticas invisibles para ellas22.

El mismo trabajo propone un marco de vigilancia llamado CARE-LLM, que combina la clasificación automática de cada conversación con la revisión experta de los casos marcados y una retroalimentación de las fallas detectadas23. Es decir, la respuesta no es confiar más en la máquina, sino vigilarla con personas. Vale decir que ese sistema alemán fue financiado por agencias de investigación alemanas y su desarrollo fue encargado y pagado por una fundación sobre Parkinson24.

Así lo leemos nosotros. Cuando una decisión importante se toma con ayuda de sistemas automáticos, la gente suele desconfiar si no puede entender cómo se llegó a esa decisión ni quién responde si algo sale mal. La desconfianza crece cuando el sistema es opaco y nadie explica sus criterios. Por eso esperaríamos que, en una clínica que adopta IA para diagnosticar niños, los padres que no reciben una explicación clara sobre cómo funciona la herramienta y quién es responsable de sus errores se muestren más reacios a dar su consentimiento, incluso si confían en el médico. Si los padres dieran su consentimiento sin preguntar nada sobre el funcionamiento del sistema o sobre la responsabilidad en caso de error, esta expectativa sería falsa.

También notamos otra cosa. Las instituciones tienden a presentar las decisiones tomadas por máquinas como más neutrales y confiables que las humanas, y a usarlas para no cargar con la responsabilidad de decisiones difíciles. Eso puede hacer que la gente acepte sin discutir lo que la máquina dice. Esperaríamos que, en el consultorio, la IA se presente como una ayuda objetiva que reduce errores, y que los padres, al no entender su funcionamiento, la acepten con resignación o sin exigir explicaciones, aunque en realidad tengan dudas. Si los padres cuestionaran activamente esa supuesta neutralidad y exigieran saber quién diseñó la herramienta y con qué intereses, esta expectativa no se cumpliría.

Así podría verse · ilustración generada por weeklyAI.watch, no es una fotografía

Lo que un padre o una madre puede hacer con esto es concreto. Antes de aceptar que se use una herramienta automática en la atención de un familiar, pregunte quién revisa el resultado, qué pasa si la máquina se equivoca y quién asume la responsabilidad. Pida que le expliquen en palabras sencillas cómo se llegó a esa recomendación. Cuando le digan que un sistema automático es "objetivo" o "neutral", pregunte quién lo programó, con qué datos se entrenó y qué intereses hay detrás. No dé por sentado que la máquina acierta más que una persona. Y antes de firmar cualquier consentimiento, pida que le expliquen qué datos se usarán, para qué y quién los verá. Si no le dan respuestas claras, puede pedirlas por escrito o consultar con otra persona de confianza.

El estudio mismo apunta en esa dirección. Sus autores concluyen que construir la confianza de los padres exigirá que los sistemas de salud los escuchen como partes interesadas, atiendan sus preocupaciones de frente y garanticen que la presencia humana en la atención siga siendo central en la era de la IA25. En otras palabras, la tecnología no basta: hace falta que alguien del otro lado del mostrador le explique a usted lo que está pasando.

Y aquí está lo que usted puede llevarse. La próxima vez que en una clínica le mencionen una herramienta automática, no necesita entender su funcionamiento técnico para hacer las preguntas correctas: quién revisa, quién responde, qué datos se usan y cómo se llegó a esa conclusión. Esas cuatro preguntas no requieren título ni abogado. Y si le responden con evasivas, eso ya es una respuesta.

¿Quién revisa lo que la máquina recomienda para su hijo, y quién responde si se equivoca?

De dónde sale cada dato de contexto, y cuánto leímos de cada documento

  1. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "While 44% supported its use in paediatrics (14% strongly), 50% were unsure."
  2. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Regarding consent, 47% would agree to its use."
  3. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Parents expected AI to assist in diagnosis (57%), reduce clinic visits (43%), and lower costs (22%)."
  4. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Concerns included errors due to incorrect data (58%), lack of transparency (18%), and privacy concerns (17%)."
  5. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Informed consent, autonomy, and accountability were rated as highly important by most participants."
  6. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "We found a statistically significant association between young parental age group and willingness to consent to the use of artificial intelligence in pediatric clinics (χ² = 25,864, p = 0.002)."
  7. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "No statistically significant association between education level and parental support for the use of artificial intelligence was found (χ² = 7.24, p = 0.842)."
  8. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Spearman correlation analysis demonstrated a weak but statistically significant positive association between self-reported knowledge of artificial intelligence and parental support for its use in pediatric care (ρ = 0.203, p = 0.001)."
  9. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "This study is limited by potential selection bias, as participants were members of a community with moderate-to-high income."
  10. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The study was conducted at one institution and two clinics over one week, which could also have affected participation."
  11. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Another limitation is convenience sampling."
  12. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Additionally, lack of validation of the questionnaire could not be ignored."
  13. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Familiarity with generative AI was moderate to high (76.9%), and professional use was common (78.3%), with ChatGPT the dominant platform (81.0%)."
  14. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Major concerns included misinformation, erosion of clinical judgment (56.2% each), algorithmic bias (53.7%), and legal liability (46.3%)."
  15. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Of the 50 Member States responding to the survey, 8% (4/50) have a health-specific AI strategy and 14% (7) are developing one."
  16. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Less than 10% (4) of Member States have developed liability standards for AI or guidance on the application of existing liability standards."
  17. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "In the WHO European Region, governance of AI in health care is underdeveloped. Adaptive legal and policy mechanisms are needed to respond effectively to the complex and evolving challenges of AI integration in health systems."
  18. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "We conducted a prospective, observational, conversation-level evaluation of jAImes, a retrieval-augmented AI information system for Parkinson's disease deployed publicly in Germany, across its first 129 days of operation (Nov 11, 2025-Mar 20, 2026), analysing all 2035 conversations (6146 messages)."
  19. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "AI-assisted triage classified 1803/2035 conversations (88·6%) as good, 224/2035 (11·0%) as partially adequate, and 8/2035 (0·4%) as inadequate."
  20. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Expert review of 45 flagged conversations confirmed five critical events."
  21. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Independent re-review of 100 randomly sampled 'good'-rated conversations identified four (4/100, 4%; 95% CI 1·6-9·8) with clinically critical errors missed by the triage-a conditional false-negative rate within the 'good' stratum, not an overall critical-event rate."
  22. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Tightly scoped, retrieval-augmented, patient-facing AI achieved favourable automated triage classifications while still harbouring clinically critical failures invisible to automated quality metrics alone; detecting them required conversation-level evaluation with clinical expert adjudication."
  23. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "CARE-LLM (Conversation-level AI Real-world Evaluation) combines automated triage of every conversation, structured expert review of flagged cases, sampling-based sensitivity validation against the adequate class, and a failure-class feedback loop."
  24. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — solo el resumen - no se pudo obtener el texto completo — el pasaje: "Funding Deutsche Forschungsgemeinschaft, Interdisciplinary Center for Clinical Research Würzburg, and German Ministry of Education and Research (BMBF); technical development of jAImes was commissioned and financed by Parkinson Stiftung Deutschland."
  25. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Building parental trust will require healthcare systems to listen to parents as stakeholders, address their concerns directly, and ensure that the human involvement in care remains central in the era of AI."

Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8

Quién pagó: El artículo declara que no se recibió financiación de ninguna fuente para este estudio, y los autores declaran no tener intereses en conflicto.

No tome esto como consejo médico profesional.

survey · BMC pediatrics · the paper, 13 May 2026 · free

Half of Parents Told Researchers They Were Unsure About AI in Their Child's Care

In one week at two Saudi clinics, 254 parents were asked about AI in children's medicine. The survey did not test any AI tool, and it cannot say what parents or laws where you live require.

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The study at a glance
Who
Parents of children at pediatric outpatient clinics
How many
254 parents
Where
Saudi Arabia
When
July 7 to 13, 2024
Kind of study
survey
Who did it
Johns Hopkins Aramco Healthcare, Saudi Arabia
The limit that matters
One institution, two clinics, one week, moderate-to-high income community; questionnaire not fully validated.
What parents said about using AI in children's medicine
Supported its use44%
Were unsure50%

Shares of 254 parents surveyed at two Saudi pediatric clinics in July 2024; the study asked about AI in general, not any specific tool, and the authors warn the sample was one institution, one week, in a moderate-to-high income community.

The parents in this study did not ask for AI. They were asked about it, and most of them said: maybe, if someone is answerable.
weeklyAI's reading
Watch · 0:55

Fifty percent. That is how many of 254 parents, asked at pediatric clinics in Saudi Arabia, said they were unsure about using artificial intelligence in children's medicine. Another 44 percent supported it, and 6 percent were against it.

Researchers at Johns Hopkins Aramco Healthcare in Ahsa and Abqaiq, Saudi Arabia, handed a 12-item questionnaire to parents of children at outpatient clinics from July 7 to 13, 2024. A nurse explained the form and got consent. The results were published in 2026.

The largest worry, named by 58 percent, was that AI could give wrong advice because of bad data. Eighteen percent named a lack of transparency, and 17 percent named privacy.

Fewer than half, 47 percent, said they would agree to AI use. Another 18 percent would agree only in emergencies, and 12 percent refused. When asked about accountability for errors, 87 percent called it important or very important.

Parents also had hopes. Fifty-seven percent expected AI to help reach a diagnosis, 43 percent expected fewer clinic visits, and 22 percent expected lower costs. The study recorded what parents expected. It did not measure whether any of that happened.

This was one institution, two clinics, one week, in a community the authors describe as moderate-to-high income. The questionnaire was not fully validated after a pilot with 10 patients. So the percentages are a snapshot of these 254 parents. They do not tell you what parents in your country or your clinic think.

The study asked about AI in general, not about any specific tool or law. It says nothing about what a company may do with your face, your voice or your child's data where you live, or what you can demand and from whom. That depends on the rules of your own country.

The authors say the uncertainty they found points to a need for clear communication and for keeping a physician involved. If a clinic near you starts using AI to help diagnose your child, what would you want to know before you agree?

What this means for you

You are reading a survey of 254 parents at two clinics in Saudi Arabia, not a law or a ruling, and it says nothing about what any company may do with your family's data where you live. If a clinic near you starts using AI to help with your child, ask who is responsible when it is wrong and whether a doctor reviews the answer. That is what 87 percent of these parents called important.

Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8

Who paid: The article states that no funding whatsoever was received from any source towards this study, and the authors declare no competing interests.

Do not take this as professional medical advice.

The longer versionThe passages copied from the paper, the pictures, and every source with how much of it we read · 1555 words · about 8 minRead it →Close

Parents Were Asked About AI in Their Children's Care. Half Weren't Sure.

A survey of 254 parents in Saudi Arabia found cautious interest, a strong wish for a doctor in charge, and one worry that outranked all others.

How it could look · illustration generated by weeklyAI.watch, not a photograph

The study took place at Johns Hopkins Aramco Healthcare, at clinics in Ahsa and Abqaiq in Saudi Arabia, over a single week in July 2024. Two hundred and fifty-four parents of children attending paediatric outpatient clinics filled in a twelve-item questionnaire, one parent per child, once, even if the family came back during the week1. The authors are Mohammed W, Deyab K, Alslman F, and colleagues. No funding was received from any source, and the authors declare no competing interests.

Most of the parents were mothers, and most were between 21 and 40 years old. Their education level was high: more than half had a university degree and about a third had finished high secondary school. More than half said they had some knowledge of AI, and 57 per cent rated their knowledge as moderate.

On the central question, the parents split three ways. Forty-four per cent supported the use of AI in paediatrics, including 14 per cent who supported it strongly; 50 per cent were unsure; 6 per cent were opposed2. Asked what they thought AI would actually do, 57 per cent expected it to help reach a diagnosis, 43 per cent expected it to reduce clinic visits, and 22 per cent expected it to lower costs3. Those are expectations the parents reported, not results anyone measured — the study did not follow a single child, and it cannot say whether any of those things happen.

Asked whether they would consent to AI being used, 47 per cent said yes, 12 per cent said no, 23 per cent were not sure, and 18 per cent would agree only in an emergency4. The worry that dominated everything else was error: 58 per cent said they feared AI giving wrong advice because of bad data, 18 per cent named a lack of transparency, and 17 per cent named privacy5. When the questionnaire turned to ethics, informed consent, autonomy and accountability were rated as highly important by most participants6.

Two comparisons inside the survey are worth separating. Younger parents were more willing to consent than older ones — a link the authors call statistically significant7. Education, by contrast, made no measurable difference: the study found no statistically significant link between education level and support for AI, and none between education and consent8. Parents who rated their own knowledge of AI higher were somewhat more supportive, but the authors describe that link as weak9. They also warn that some of these groups were small and the results should be read with caution.

The limits are the authors' own, and they are substantial. Participants came from a community with moderate-to-high income, which raises the risk that the sample is not representative10. The whole study ran at one institution, in two clinics, over one week11. Parents were recruited by convenience rather than at random12. The questionnaire was adapted from validated ones but not fully validated itself13. And because the answers were self-reported, they may not perfectly reflect what the parents actually think or do. Above all, the study asked about AI in general — an idea explained to parents as advanced computer applications that use large amounts of data to help clinicians diagnose and decide — not about any particular tool, product or rule.

How it could look · illustration generated by weeklyAI.watch, not a photograph

It helps to say what this kind of system actually is, because the word "AI" covers a wide range. In the form the researchers described to parents, it is software that reads large amounts of data and offers a suggestion to a clinician — a pattern it has seen before, applied to this child's case. The clinician is meant to be the one who decides. That is the arrangement parents in this study appear to have had in mind, and it is very different from a chatbot that answers a patient or a parent directly. One deployment of that second kind was studied in Germany: a system answering Parkinson's disease questions, live for its first 129 days, with 2,035 conversations examined — according to the summary of that study, and we could read only the summary, the full paper is behind a subscription14.

Automated screening sorted 1,803 of the 2,035 conversations as good, 224 as partially adequate, and 8 as inadequate15. But when clinicians reviewed a random sample of 100 conversations that the automated screening had rated good, they found four containing clinically critical errors the screening had missed16. Across the flagged cases and the sample, the confirmed critical events included serious errors17. The authors' conclusion is the part worth carrying: a tightly scoped system can look fine to automated quality measures while still holding serious failures that only a clinician reading the conversations can catch18. That work was funded by German research bodies, and the technical development of the system was commissioned and paid for by a German Parkinson's foundation19.

A survey of World Health Organization European Region member states, run between June 2024 and March 2025, found that 4 of the 50 responding countries had a health-specific AI strategy and 7 more were developing one20. Ten had passed new health-specific AI laws21, 14 had issued guidelines on the ethical implications of using AI in health or across sectors22, and fewer than 10 per cent — four countries — had developed liability standards for AI or guidance on applying existing ones23. The authors' own verdict was that governance of AI in health care in that region is underdeveloped24.

A voluntary online survey of emergency medicine professionals across the region, run from April to October 2025, drew 123 respondents from 16 countries — again, only the summary was available to us25. Familiarity with generative AI was moderate to high and professional use was common, with one platform dominant26. Patterns were similar across countries27. The main uses were research and literature review, medical education, and clinical documentation28. The concerns those clinicians named were misinformation and erosion of clinical judgment, followed by algorithmic bias and legal liability29. And institutional governance was nearly absent: only 7.4 per cent reported that their institution had any AI policy at all30. Nearly nine in ten said they wanted formal training31.

How it could look · illustration generated by weeklyAI.watch, not a photograph

Here is how we read it. Put the Saudi parents and the German deployment side by side and the same gap appears from two directions. Parents say the thing they fear most is wrong advice, and the only study we have of a live patient-facing system found exactly that — critical errors that the automated checks did not catch. The parents' instinct and the measured failure line up. What we expect in homes like yours, if AI quietly enters a clinic near you, is not a dramatic refusal. It is the fifty per cent: people who go along with it, do not ask, and only later wonder who was actually responsible for the suggestion that shaped their child's care. You would know we were wrong if clinics adopting these tools published, in advance and in plain language, the name of the clinician responsible for every AI-assisted decision, and if that person took responsibility when something went wrong. Before you agree to AI-assisted care for your child, ask the clinic one question and ask for the answer in writing: who is responsible if the machine's advice turns out to be wrong? Keep the copy.

We also read, in the German summary, something that changes what "the doctor is in the loop" should mean to you. Automated quality checks rated almost everything good, and a clinician reading the actual conversations still found serious errors in the ones rated good. So the promise that a doctor oversees the system is not the same as a doctor reading what the system said to you or your child. If a clinic tells you a physician supervises the AI, that is the moment to ask what the supervision consists of — does a person read the conversation, or does a person read a dashboard? In the Latin American clinicians' survey, almost none reported that their institution had any AI policy at all, which suggests that in many places there is no written answer to that question yet. That is not a reason to avoid care. It is a reason to ask, and to notice whether you get a specific answer or a general reassurance.

What would have to happen next, in the authors' own words, is that building parental trust requires health systems to listen to parents as stakeholders, address their concerns directly, and keep human involvement central in the era of AI32. The German team's answer to the same problem is procedural: screen every conversation automatically, have experts review the flagged ones, sample the ones that passed, and feed the failures back into the system33. Both are proposals about process, not products. Neither has been adopted as a rule anywhere we can point you to.

The parents in this study did not ask for AI. They were asked about it, and most of them said: maybe, if someone is answerable. That is a position you can hold too — and the question above is the whole of it. Who signs their name to the machine's suggestion about your child?

Where each piece of context comes from, and how much of it we read

  1. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Parents of children attending the pediatric outpatient clinics were eligible for participation. One parent per child was invited to complete the survey only once, even if multiple visits were made during the study period."
  2. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "While 44% supported its use in paediatrics (14% strongly), 50% were unsure."
  3. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Parents expected AI to assist in diagnosis (57%), reduce clinic visits (43%), and lower costs (22%)."
  4. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Regarding consent, 47% would agree to its use."
  5. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Concerns included errors due to incorrect data (58%), lack of transparency (18%), and privacy concerns (17%)."
  6. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Informed consent, autonomy, and accountability were rated as highly important by most participants."
  7. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "We found a statistically significant association between young parental age group and willingness to consent to the use of artificial intelligence in pediatric clinics (χ² = 25.864, p = 0.002)."
  8. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "No statistically significant association between education level and parental support for the use of artificial intelligence was found (χ² = 7.24, p = 0.842)."
  9. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Spearman correlation analysis demonstrated a weak but statistically significant positive association between self-reported knowledge of artificial intelligence and parental support for its use in pediatric care (ρ = 0.203, p = 0.001)."
  10. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "This study is limited by potential selection bias, as participants were members of a community with moderate-to-high income."
  11. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "The study was conducted at one institution and two clinics over one week, which could also have affected participation."
  12. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Another limitation is convenience sampling."
  13. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Additionally, lack of validation of the questionnaire could not be ignored."
  14. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — only the abstract - the full text could not be fetched — the passage: "We conducted a prospective, observational, conversation-level evaluation of jAImes, a retrieval-augmented AI information system for Parkinson's disease deployed publicly in Germany, across its first 129 days of operation (Nov 11, 2025-Mar 20, 2026), analysing all 2035 conversations (6146 messages)."
  15. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — only the abstract - the full text could not be fetched — the passage: "AI-assisted triage classified 1803/2035 conversations (88·6%) as good, 224/2035 (11·0%) as partially adequate, and 8/2035 (0·4%) as inadequate."
  16. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — only the abstract - the full text could not be fetched — the passage: "Independent re-review of 100 randomly sampled 'good'-rated conversations identified four (4/100, 4%; 95% CI 1·6-9·8) with clinically critical errors missed by the triage-a conditional false-negative rate within the 'good' stratum, not an overall critical-event rate."
  17. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — only the abstract - the full text could not be fetched — the passage: "Confirmed critical events-five flagged, four sampled-spanned three failure classes (knowledge boundary, robustness, and escalation failures) and included an inappropriate memantine recommendation in Parkinson's disease dementia and explicit suicidal ideation that did not trigger the intended emergency response."
  18. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — only the abstract - the full text could not be fetched — the passage: "Tightly scoped, retrieval-augmented, patient-facing AI achieved favourable automated triage classifications while still harbouring clinically critical failures invisible to automated quality metrics alone; detecting them required conversation-level evaluation with clinical expert adjudication."
  19. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — only the abstract - the full text could not be fetched — the passage: "Funding Deutsche Forschungsgemeinschaft, Interdisciplinary Center for Clinical Research Würzburg, and German Ministry of Education and Research (BMBF); technical development of jAImes was commissioned and financed by Parkinson Stiftung Deutschland."
  20. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — only the abstract - the full text could not be fetched — the passage: "Of the 50 Member States responding to the survey, 8% (4/50) have a health-specific AI strategy and 14% (7) are developing one."
  21. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — only the abstract - the full text could not be fetched — the passage: "Nearly half the Member States (23) reported ongoing assessments of laws and policies on AI systems and a fifth (10) have developed new health-specific AI laws."
  22. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — only the abstract - the full text could not be fetched — the passage: "Only 14 Member States have issued guidelines to address the ethical implications of using AI in health or across sectors."
  23. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — only the abstract - the full text could not be fetched — the passage: "Less than 10% (4) of Member States have developed liability standards for AI or guidance on the application of existing liability standards."
  24. Adib K, Letchford N, Dunning HE, Salama N, Tolias Y, De Barros J, et al. (2026). Governance of artificial intelligence for health systems, WHO European Region. Bulletin of the World Health Organization. 10.2471/blt.25.294978 — only the abstract - the full text could not be fetched — the passage: "In the WHO European Region, governance of AI in health care is underdeveloped. Adaptive legal and policy mechanisms are needed to respond effectively to the complex and evolving challenges of AI integration in health systems."
  25. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — only the abstract - the full text could not be fetched — the passage: "A total of 123 respondents from 16 countries were analyzed."
  26. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — only the abstract - the full text could not be fetched — the passage: "Familiarity with generative AI was moderate to high (76.9%), and professional use was common (78.3%), with ChatGPT the dominant platform (81.0%)."
  27. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — only the abstract - the full text could not be fetched — the passage: "Adoption patterns were homogeneous across countries, with no significant associations between country of origin and familiarity, frequency of use, or platform preference."
  28. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — only the abstract - the full text could not be fetched — the passage: "Primary applications included research and literature review (66.1%), medical education (59.1%), and clinical documentation (40.9%)."
  29. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — only the abstract - the full text could not be fetched — the passage: "Major concerns included misinformation, erosion of clinical judgment (56.2% each), algorithmic bias (53.7%), and legal liability (46.3%)."
  30. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — only the abstract - the full text could not be fetched — the passage: "Institutional AI governance was nearly absent, with only 7.4% reporting existing policies."
  31. Baez AA, Acra C, Difo B. (2026). Adoption, perceived value, and barriers to generative artificial intelligence in emergency medicine: A cross-sectional survey of Latin American clinicians. International Journal of Critical Illness and Injury Science. 10.4103/ijciis.ijciis_43_26 — only the abstract - the full text could not be fetched — the passage: "Despite this, 88.4% expressed interest in formal AI training and 65.6% supported integration into clinical guidelines."
  32. Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8 - the article this story is about — the whole article — the passage: "Building parental trust will require healthcare systems to listen to parents as stakeholders, address their concerns directly, and ensure that the human involvement in care remains central in the era of AI."
  33. Lange F, Mardi S, Binder T, Reich MM, Odorfer T, Volkmann J. (2026). Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study. The Lancet Regional Health - Europe. 10.1016/j.lanepe.2026.101866 — only the abstract - the full text could not be fetched — the passage: "CARE-LLM (Conversation-level AI Real-world Evaluation) combines automated triage of every conversation, structured expert review of flagged cases, sampling-based sensitivity validation against the adequate class, and a failure-class feedback loop."

Mohammed, W., Deyab, K., Alslman, F. et al. (2026). Parents’ knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-06939-8

Who paid: The article states that no funding whatsoever was received from any source towards this study, and the authors declare no competing interests.

Do not take this as professional medical advice.