AI raadplegen voor een belangrijke persoonlijke beslissing vs. beslissen zonder
Als je handelt
AI raadplegen voor beslissing
22%
Als je niets doet
Beslissen zonder AI-input
28%
Percentage dat later spijt heeft van elke keuze. De balken en het volledige overzicht staan hieronder.
Levensstijl
Laatst beoordeeld 2026-05-11
Kwaliteit van bewijs 4.0/5
Beoordelingsscore op acht dimensies volgens de
kwaliteitsrubriek
. Elke dimensie krijgt een score van 1 tot 5.
D1 Bronverificatie
4/5
D2 Autoriteit en onafhankelijkheid van bronnen
5/5
D3 Nauwkeurigheid van spijtcijfer
2/5
D4 Vergelijkbaarheid van bronnen
2/5
D5 Gilovich-patroon
4/5
D6 Prozakwaliteit
5/5
D7 Volledigheid van voorbehouden
5/5
D8 Steekproefkwaliteit
5/5
Gemiddelde4.0/5
Proxygegevens — er bestaat geen directe spijtenquête voor deze beslissing. De percentages zijn afgeleid van tevredenheidsscores en toegangsdrempelgegevens in plaats van vragen die direct naar spijt vroegen. Zie opmerkingen hieronder.
Spijt van handelen
AI raadplegen voor beslissing
22%
~22% van degenen die AI raadpleegden voor een significante beslissing meldt een schadelijke of problematische uitkomst (proxy; 11% kreeg onveilige gezondheidsaanbevelingen; 21-49% van AI medische reacties worden in gecontroleerde studies als problematisch beoordeeld)
Amerikaanse volwassenen die AI-chatbots raadpleegden voor gezondheids-, financiële of levensbeslissingen
oktober-december 2025
Spijt van nalaten
Beslissen zonder AI-input
28%
~28% van degenen die AI-consultatie oversloegen, kan nuttige informatie hebben gemist die hun beslissing had kunnen verbeteren (proxy; 46-59% van AI-gezondheidsgebruikers meldt concrete voordelen; 66-90% van financiële AI-gebruikers vond het de moeite waard)
Amerikaanse volwassenen die significante beslissingen namen zonder AI te raadplegen
2025-2026
% betreurt deze keuze
AI raadplegen voor beslissingBeslissen zonder AI-input
22%28%
balanced — Ongeveer in balans — beide keuzes leveren vergelijkbare spijt op.
Gerelateerde keuzes
Semantisch vergelijkbare keuzes — zelfde terrein, andere afwegingen.
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.
Bronnen: handelen
Bronnenverantwoording
Elk getal hieronder is wat elke bron rapporteerde, met het letterlijke citaat waarop we ons baseerden en hoe we tot ons cijfer kwamen. Klik op een link om rechtstreeks te verifiëren.
2/3 bronnen onafhankelijk woordelijk geverifieerd tegenover de geciteerde bron
[1]West Health / Gallup — Millions of Americans Now Consult AI Before, After, and Sometimes Instead of Seeing a Doctor
Geverifieerd
Gerenommeerde referentie
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
Fragment
“"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."
”
Brongegevens van
2026-01-01
Geraadpleegd
2026-05-11
Verificatie
Fragment onafhankelijk opnieuw opgehaald en woord voor woord bevestigd tegenover de geciteerde bron tijdens onze onderbouwingsaudit.
Berekening
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
Geverifieerd
Gerenommeerde referentie
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)
Fragment
“"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."
”
Brongegevens van
2025-02-01
Geraadpleegd
2026-05-11
Verificatie
Fragment onafhankelijk opnieuw opgehaald en woord voor woord bevestigd tegenover de geciteerde bron tijdens onze onderbouwingsaudit.
Berekening
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
Vakgenoten-beoordeeld
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
Fragment
“"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."
”
Brongegevens van
2025-05-13
Geraadpleegd
2026-05-11
Berekening
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.
Bronnen: niet handelen
Bronnenverantwoording
Elk getal hieronder is wat elke bron rapporteerde, met het letterlijke citaat waarop we ons baseerden en hoe we tot ons cijfer kwamen. Klik op een link om rechtstreeks te verifiëren.
[1]West Health / Gallup — Millions of Americans Now Consult AI Before, After, and Sometimes Instead of Seeing a Doctor
Gerenommeerde referentie
46% of AI health users felt more confident asking providers questions afterward; 59% used AI to research before a doctor visit
Fragment
“"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."
”
Brongegevens van
2026-01-01
Geraadpleegd
2026-05-11
Berekening
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
Gerenommeerde referentie
~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)
Fragment
“"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.'"
”
Brongegevens van
2026-04-01
Geraadpleegd
2026-05-11
Berekening
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.
Kanttekeningen
PROXY-METINGEN OVERAL. Geen enquête heeft individuen direct gevraagd "heb je spijt AI te hebben geraadpleegd voor deze beslissing?" of "wens je AI te hebben geraadpleegd voordat je besloot?" Beide zijden zijn volledig opgebouwd uit aangrenzende gegevens: uitkomstkwaliteitsstudies (voor actie-risico), gerapporteerde voordelen (voor inactie-opportuniteitskosten) en gedragsmaten (provider-bezoek-overslag-percentages). De regret_delta van -0,06 is te klein om dit betrouwbaar te classificeren als inactie-domineert; het is geclassificeerd als 'gemengd' omdat het bewijs geen van beide patronen duidelijk ondersteunt. Het actie-risico varieert drastisch per domein en hoe de AI wordt gebruikt: een doktersbezoek aanvullen (laag risico, hoog voordeel) vs. het vervangen voor een ernstig symptoom (hoog risico). De academische nauwkeurigheidsstudies (21-49% problematische respons-percentages) weerspiegelen AI-prestaties specifiek op medische vragen — het hoogste-belangen-domein. Financiële en algemene levensbeslissingen hebben waarschijnlijk lagere problematische-respons-percentages, maar minder gepubliceerde nauwkeurigheid-benchmarks. De NEJM AI-overtrust-studie is een bijzonder belangrijke kanttekening: het mechanisme dat problematische reacties omzet in schade is het onvermogen van gebruikers om ze te detecteren, en zelfverzekerde framing met weinig caveats (gedocumenteerd in de UCLA/BMJ Open-studie) verbergt systematisch deze onzekerheid. "AI gebruiken" is geen enkele actie: een AI-samenvatting lezen ter voorbereiding op een doktersbezoek is categorisch anders dan handelen op een AI-diagnose in plaats van een dokter te zien. Deze vermelding aggregeert deze heterogene gebruiken, wat zijn precisie beperkt. Wells Fargo en TD Bank-zelfrapport-gegevens zijn ondoorzichtig over methodologie en moeten alleen als richtinggevend worden behandeld. AI-capaciteiten verbeteren snel; nauwkeurigheids- en betrouwbaarheidsbenchmarks van 2025-2026 zullen waarschijnlijk verschillen van die 12-24 maanden later van toepassing. Deze vermelding heeft een kortere houdbaarheid dan de meeste in de dataset.
