Consultar a la IA antes de una decisión personal importante vs. decidir sin ella
Si actúas
Consultar a la IA antes de decidir
22%
Si no actúas
Decidir sin aporte de la IA
28%
Porcentaje de quienes luego se arrepienten de cada elección. Las barras y el registro completo aparecen abajo.
Estilo de vida
Última revisión 2026-05-11
Calidad de la evidencia 4.0/5
Puntuación de revisión en ocho dimensiones según la
rúbrica de calidad
. Cada dimensión puntuada de 1 a 5.
D1 Verificación de fuentes
4/5
D2 Autoridad e independencia de las fuentes
5/5
D3 Precisión de la tasa de arrepentimiento
2/5
D4 Comparabilidad de las fuentes
2/5
D5 Patrón de Gilovich
4/5
D6 Calidad de la prosa
5/5
D7 Completitud de las advertencias
5/5
D8 Calidad de la muestra
5/5
Media4.0/5
Datos sustitutos — no existe ninguna encuesta directa sobre el arrepentimiento para esta decisión. Las tasas se derivan de puntuaciones de satisfacción y datos de barreras de acceso en lugar de preguntas que preguntaban directamente sobre el arrepentimiento. Ver advertencias más abajo.
Arrepentimiento por acción
Consultar a la IA antes de decidir
22%
~22% de quienes consultaron IA para una decisión significativa reportan un resultado dañino o problemático (proxy; 11% recibió recomendaciones de salud inseguras; 21-49% de las respuestas médicas de IA son calificadas como problemáticas en estudios controlados)
Adultos estadounidenses que consultaron chatbots de IA para decisiones de salud, financieras o de vida
octubre-diciembre 2025
Arrepentimiento por inacción
Decidir sin aporte de la IA
28%
~28% de quienes omitieron la consulta con IA pueden haber perdido información útil que podría haber mejorado su decisión (proxy; 46-59% de los usuarios de IA de salud reportan beneficios concretos; 66-90% de los usuarios de IA financiera lo encontraron valioso)
Adultos estadounidenses que tomaron decisiones significativas sin consultar IA
2025-2026
% que se arrepienten de esta elección
Consultar a la IA antes de decidirDecidir sin aporte de la IA
Hacerse un aumento de senosNo hacerse un aumento de senos
7,0%5,0%
Equilibrado
Aproximadamente equilibrado
About 1 in 4 US adults has now consulted AI for health information or advice, according to a West Health/Gallup panel of 5,660 adults surveyed in late 2025. Among those users, 46% felt more confident asking their providers questions afterward, and 59% used AI to prepare before a doctor visit — concrete stated benefits that non-users forgo. But the same survey found that 11% of AI health users reported receiving unsafe recommendations, and a parallel UCLA/BMJ Open study rating 250 AI responses to medical questions found 49.6% were problematic to some degree — mostly delivered with confidence and few caveats, making them difficult for users to identify as unreliable. An MIT Media Lab study published in NEJM AI documented that participants systematically overestimated AI medical reliability and could not distinguish AI-generated from physician responses, even when the AI response was inaccurate.
The regret arithmetic here is genuinely ambiguous, which is unusual in this dataset. The action-side risk (22% proxy, bounded by 11% unsafe-recommendation rate and 49.6% problematic-response rate) and the inaction-side opportunity cost (28% proxy, based on AI users’ reported benefits) are close enough that the entry is classified as ‘mixed’ rather than clearly inaction-dominates. How AI is used matters more than whether it is used: supplementing a scheduled doctor visit with AI research before attending is a different action category than using AI as a triage replacement for a symptom that warrants evaluation. The former has low action-risk and meaningful information benefit; the latter has higher action-risk and may cause harmful delay. The 14 million Americans who skipped a provider visit based on AI advice represent the higher-risk end of the use spectrum, though some of those skipped visits may have been genuinely unnecessary.
The honest summary of this entry’s evidentiary state: the AI consultation decision is too domain-specific, use-case-dependent, and rapidly evolving to generate a stable regret-pair estimate. Financial AI consultation self-reports are overwhelmingly positive (Wells Fargo: ~90% found results worthwhile), which would pull the inaction-regret figure up substantially if the financial domain were weighted equally with health. Medical AI consultation carries real documented risk of inaccurate confident advice in a domain where acting on wrong information has direct health consequences. The mixed classification reflects both the genuine uncertainty and the heterogeneity of “consulting AI” as a decision — something between a research tool and an advisor, with properties of each and the disclaimers of neither.
Fuentes: acción
Registro de evidencia
Cada número a continuación es lo que reportó cada fuente, con la cita textual en la que nos basamos y cómo llegamos a nuestra cifra. Haz clic en cualquier enlace para verificarlo directamente.
2/3 fuentes verificadas de forma independiente palabra por palabra frente a la fuente citada
[1]West Health / Gallup — Millions of Americans Now Consult AI Before, After, and Sometimes Instead of Seeing a Doctor
Verificado
Fuente de referencia
About 1 in 4 US adults (over 66 million) report having used AI tools or chatbots for health information or advice; 11% of US adults who used AI for health information reported receiving unsafe recommendations; 14% (~14 million) skipped a provider visit based on AI advice
Extracto
“"One in four U.S. adults — the equivalent of over 66 million Americans — report having used artificial intelligence tools or chatbots for physical or mental healthcare information or advice. ... 11% of AI health users reported receiving unsafe health recommendations from AI. 14% of recent AI health users skipped a provider visit based on AI advice. 46% felt more confident asking providers questions after using AI."
”
Datos de la fuente de
2026-01-01
Accedido
2026-05-11
Verificación
Extracto recuperado de forma independiente y confirmado palabra por palabra frente a la fuente citada durante nuestra auditoría de fundamentación.
Cálculo
West Health / Gallup panel survey of n=5,660 US adults, fielded October–December 2025, margin of error ±2.1pp. This is the most methodologically rigorous large-sample US survey on AI health consultation outcomes. The "one in four US adults (over 66 million)" overall-prevalence figure grounds the entry's opening body sentence, which previously cited this survey for that figure without a matching excerpt/statistic. The 11% unsafe-recommendation rate is the primary action-regret proxy: it represents the share of users who received advice that was later identified as unsafe, which is a necessary precursor to regret even if regret is not directly measured. The 14% who skipped a provider visit based on AI advice is a behavioral-consequence measure; it does not directly map to regret (some may have correctly assessed that no visit was needed). The 22% action-regret estimate is bounded by the 11% unsafe-recommendation rate (lower bound) and the broader 21–49% problematic-response rates from academic accuracy studies (upper bound), centered at approximately 22% to reflect that problematic responses do not always produce regrettable outcomes.
[2]CIDRAP (University of Minnesota) reporting on UCLA / BMJ Open study — AI Chatbots Provide Poor Answers to Medical Questions Half the Time
Verificado
Fuente de referencia
49.6% of AI responses to medical questions were rated as problematic in a blinded evaluation of 5 major chatbots (30% somewhat, 19.6% highly problematic)
Extracto
“"49.6% of AI chatbot responses to medical questions were rated as problematic — 30% 'somewhat problematic' and 19.6% 'highly problematic.' Chatbot responses were consistently given with confidence and certainty, with few caveats or disclaimers."
”
Datos de la fuente de
2025-02-01
Accedido
2026-05-11
Verificación
Extracto recuperado de forma independiente y confirmado palabra por palabra frente a la fuente citada durante nuestra auditoría de fundamentación.
Cálculo
UCLA / BMJ Open study of 250 total questions across 5 major chatbots (ChatGPT, Gemini, DeepSeek, Meta AI, Grok), 10 questions each across 5 medical categories, data collected February 2025, published 2026. The 49.6% problematic-response rate establishes the upper bound of action-side risk for health-domain AI consultation. The key finding about confident framing with few caveats is the mechanism that converts problematic responses into potential harm: users cannot easily identify which responses are in the unreliable half. Used here to anchor the upper bound of the 22% action-regret estimate; the lower bound is the West Health/Gallup 11% unsafe-recommendation rate from real-world self-report.
[3]NEJM AI (Shekar, Pataranutaporn, Sarabu, Cecchi & Maes) / MIT Media Lab — People Overtrust AI-Generated Medical Advice despite Low Accuracy
Revisado por pares
Across 300 participants, people were unable to distinguish AI-generated from doctors' responses, rated high-accuracy AI responses as more valid/trustworthy/complete, gave low-accuracy AI responses ratings similar to doctors', and showed a high tendency to follow potentially harmful AI advice
Extracto
“"Participants were unable to effectively distinguish between AI-generated responses and doctors' responses [and] rated high-accuracy AI responses as significantly more valid, trustworthy, and complete than the other two types of responses. Low-accuracy AI responses tended to receive ratings similar to those given to doctors' responses [and participants showed] a high tendency to follow the potentially harmful medical advice contained in those responses."
”
Datos de la fuente de
2025-05-13
Accedido
2026-05-11
Cálculo
NEJM AI / MIT Media Lab study on AI medical response evaluation. The overtrust finding is the mechanism explanation: users systematically overestimate AI reliability in the medical domain, meaning the problematic-response rates from accuracy studies translate into real-world harm more readily than they would if users could identify unreliable responses. This supports using the problematic-response rate (not just the unsafe- recommendation rate) as the relevant risk bound.
Fuentes: inacción
Registro de evidencia
Cada número a continuación es lo que reportó cada fuente, con la cita textual en la que nos basamos y cómo llegamos a nuestra cifra. Haz clic en cualquier enlace para verificarlo directamente.
[1]West Health / Gallup — Millions of Americans Now Consult AI Before, After, and Sometimes Instead of Seeing a Doctor
Fuente de referencia
46% of AI health users felt more confident asking providers questions afterward; 59% used AI to research before a doctor visit
Extracto
“"46% of AI health users felt more confident asking providers questions after using AI. 59% used AI to research before a doctor visit. 71% were motivated by wanting answers quickly; 71% wanted additional information."
”
Datos de la fuente de
2026-01-01
Accedido
2026-05-11
Cálculo
West Health / Gallup panel, n=5,660, October–December 2025. The 46% who felt more confident asking providers questions is a concrete stated benefit of AI consultation — a benefit that non-users forgo. The 28% inaction-regret proxy is derived conservatively from the proportion of AI users who report concrete preparation or information benefits (46–59%), adjusted downward to reflect that non-users may have obtained similar information through other channels (search, calling a nurse line, reading drug inserts). No direct "do you regret not consulting AI for this decision?" survey was identified. This is the most data-sparse side of the entry.
[2]ABA Banking Journal (reporting on Wells Fargo and TD Bank surveys) — Bank Surveys Find Consumers Increasingly Turning to AI for Financial Advice
Fuente de referencia
~90% of US adults who used AI for financial decisions and acted on the advice said results were 'profitable or worthwhile' (Wells Fargo 2026); 55% of adults use AI for financial management decisions (TD Bank 2026)
Extracto
“"19% of U.S. adults used AI for financial advice; 38% among Gen Z. Two-thirds of those who used AI acted on its suggestions. Approximately 90% of those who acted said the results were 'profitable or worthwhile.'"
”
Datos de la fuente de
2026-04-01
Accedido
2026-05-11
Cálculo
Wells Fargo consumer survey and TD Bank consumer survey, both reported April 2026. Sample sizes and full methodology not disclosed. The 90% "worthwhile" self-report is the most positive available outcome figure for AI financial consultation but is highly susceptible to self-serving bias: people who acted on AI advice and lost money are less likely to report the action as worthwhile, but are also less likely to be included in a consumer satisfaction survey. Used here as directional corroboration that AI financial consultation produces positive self-reported outcomes at high rates, supporting the inaction-regret proxy. Not used as a primary figure due to methodological opacity.
Advertencias
MEDICIONES PROXY EN AMBOS LADOS. Ninguna encuesta ha preguntado directamente a individuos «¿lamenta consultar IA para esta decisión?» o «¿desearía haber consultado IA antes de decidir?» Ambos lados se construyen completamente a partir de datos adyacentes: estudios de calidad de resultados (para riesgo del lado de acción), beneficios reportados (para costo de oportunidad del lado de inacción) y medidas conductuales (tasas de omisión de visitas al proveedor). El regret_delta de -0,06 es demasiado pequeño para clasificar esto fiablemente como dominado por inacción; se clasifica como 'mixto'. El riesgo del lado de acción varía dramáticamente por dominio y por cómo se usa la IA: complementar una visita al médico (bajo riesgo, alto beneficio) vs reemplazar una por un síntoma serio (alto riesgo). Los estudios académicos de precisión (21-49% de tasas de respuesta problemática) reflejan el rendimiento de la IA en preguntas médicas específicamente. El estudio NEJM de sobreconfianza en IA es una advertencia particularmente importante: el mecanismo que convierte respuestas problemáticas en daño es la incapacidad de los usuarios para detectarlas. «Usar IA» no es una sola acción. Las capacidades de IA están mejorando rápidamente; los benchmarks de precisión y fiabilidad de 2025-2026 probablemente diferirán de los aplicables 12-24 meses después.
