The DOI matches its Crossref record; 46 of the 48 references it cites were found in a registry or an index; none of the 28 sources its claims cite is checked for support yet.
Understanding, explaining, and utilizing medical artificial intelligence
Open accessAbstract
Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective difficulty of understanding algorithms (the perception that they are a 'black box') and by an illusory subjective understanding of human medical decision-making. In five pre-registered experiments (1-3B: N = 2,699), we find that people exhibit an illusory understanding of human medical decision-making (study 1). This leads people to believe they better understand decisions made by human than algorithmic healthcare providers (studies 2A,B), which makes them more reluctant to utilize algorithmic than human providers (studies 3A,B). Fortunately, brief interventions that increase subjective understanding of algorithmic decision processes increase willingness to utilize algorithmic healthcare providers (studies 3A,B). A sixth study on Google Ads for an algorithmic skin cancer detection app finds that the effectiveness of such interventions generalizes to field settings (study 4: N = 14,013).
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
People report greater subjective understanding of medical decisions made by human than algorithmic providers, but their objective understanding is similarly poor; this illusory understanding contributes to algorithm aversion. Brief explanations of how medical AI works increase subjective understanding and willingness to use algorithmic providers, including in a Google Ads field study.
Key findings
- In study 1 (N = 297), making people explain provider decision processes reduced subjective understanding for human providers (Mpre = 5.08 vs Mpost = 4.52, ΔH = −0.56, P < 0.001) but not for algorithmic providers (Mpre = 3.99 vs Mpost = 3.83, ΔA = −0.16, P = 0.14); the interaction was significant (ΔH − ΔA = −0.40, P = 0.009).
- In studies 2A/B, participants claimed greater subjective understanding of human than algorithmic providers (2A: β = −0.82, P < 0.001; 2B: β = −0.46, P = 0.001), while objective understanding did not differ by provider (2A: β = 0.02, P = 0.91; 2B: β = 0.11, P = 0.43).
- In studies 3A/B, interventions explaining decision processes increased subjective understanding and reduced algorithm aversion; utilization intentions increased more for algorithmic providers (3A: β = 0.83, P < 0.001; 3B: β = 0.70, P < 0.001) than for human providers (3A: β = 0.34, P = 0.008; 3B: β = 0.14, P = 0.41).
- In study 4 (N = 14,013 impressions), the intervention advertisement had a higher click-through rate (6.36%) than the control advertisement (3.29%; β = 0.69, z = 8.17, P < 0.001, d = 0.38).
Methods
- Five pre-registered online experiments (studies 1, 2A/B, 3A/B; N = 2,699) plus a sixth pre-registered online field study on Google Ads (study 4; N = 14,013). Participants were US residents recruited from Lucid, Amazon Mechanical Turk, and Google Ads, in a skin cancer detection context.
- Designs included: study 1, 2 (provider: human vs algorithm) × 2 (rating order: pre- vs post-intervention) mixed design; studies 2A/B, 2 (provider) × 2 (understanding type: subjective vs objective) between-subjects; studies 3A/B, 2 (provider) × 2 (intervention: control vs intervention) between-subjects; study 4, intervention vs control advertisement.
- Analyses used regressions, moderated mediation with 5,000 bootstrap samples, and logistic regression for click-through rate. Pre-registrations, raw data, and Stata syntax are available at https://osf.io/taqp8/.
Limitations
- The paper states that mediation analysis has limitations and invites replication with alternative manipulations, designs, and measures; it also notes that the ancillary objective understanding measure may have been sensitive, and that wider-range questions might detect subtle changes. For study 4, the authors state that ad assignment was not random because Google Ads optimized for clicks, which may have generated unintended variance, though they believe replication in studies 3A/B makes it unlikely the results are solely due to ad optimization.
- Evident from the text: studies 1–3B measure subjective understanding and healthcare utilization intentions rather than actual clinical uptake, and study 4 measures ad click-through rather than real patient utilization; the samples are online panels and Google Ads users, not a clinical patient population.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
22 Own method · 20 Own result · 16 Not checked yet · 15 Own interpretation · 2 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A logistic regression revealed that the click-through rate was higher in the intervention advertisement (6.36%) compared with the control advertisement (3.29%, β = 0.69, z = 8.17, P < 0.001, d = 0.38).”
- Also stated in: § Discussion · plain
“Indeed, we find increased advertising efficacy for SkinVision, a skin cancer detection app, when advertising included language explaining how it works.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Discussion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but they exhibit similar objective understanding of decisions made by both providers (people actually know as little about both decision processes).”
- Also stated in: § Studies 2A,B. · plain
“whereas their objective understanding did not differ by provider (2A: β = 0.02, t = 0.11, P = 0.91; 2B: β = 0.11, t = 0.79, P = 0.43)”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Participants' self-reported subjective understanding accurately reflects their subjective understanding.
- Also stated in: § Studies 2A,B. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It increased subjective understanding of the decision made by algorithmic providers (3A: Δ = 1.42, t = 12.51, P < 0.001; 3B: Δ = 0.56, t = 6.21,P < 0.001) and human providers (3A: Δ = 1.04, t = 11.08,P < 0.001; 3B: Δ = 1.39, t = 11.85,P < 0.001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the indirect effect of healthcare provider on utilization intentions through subjective understanding was significantly stronger (3A: Δ = 0.20, 95% CI [0.05, 0.37]; 3B: Δ = 0.60, 95% CI [0.35, 0.89]) in the control condition”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, we find that people claim greater subjective understanding for medical decisions made by human than AI providers (people feel that they know more), but they exhibit similar objective understanding of decisions made by both providers”
- Also stated in: § Greater reductions in pre-intervention to post-intervention ratings indicate a larger illusion of understanding. · plain
“Participants claimed greater subjective understanding of a human than an algorithmic decision processes (pre-intervention: β = 1.08, z = 5.02, P < 0.001; post-intervention: β = 0.69, z = 3.19, P = 0.001).”
- Also stated in: § Studies 2A,B. · plain
“Participants claimed greater subjective understanding of medical decisions made by human than algorithmic providers (2A: β = -0.82,t = -6.01,P < 0.001; 2B: β = -0.46,t = -3.33,P = 0.001)”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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- Rests on an assumption: Participants' self-reported subjective understanding accurately reflects their subjective understanding.
- Also stated in: § Greater reductions in pre-intervention to post-intervention ratings indicate a larger illusion of understanding. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the intervention increased utilization intentions to a greater extent for algorithmic providers (3A: β = 0.83, t = 6.46,P < 0.001; 3B: β = 0.70, t = 4.09, P < 0.001) than for human providers (3A: β = 0.34, t = 2.66, P = 0.008; 3B: β = 0.14, t = 0.82, P = 0.41).”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A simple one-page visual or sentence that explains the criteria or process used to make medical decisions increased acceptance of an algorithm-based skin cancer diagnostic tool”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference in subjective understanding of the decision made by the human and algorithmic provider was smaller in the intervention condition (3A: β = -0.70,t = -5.41,P < 0.001; 3B: β = -1.10,t = -6.97,P < 0.001) than in the control condition (3A: β = -0.22,t = -1.74,P = 0.08; 3B: β = -0.08,t = -0.52,P = 0.60).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithm aversion in healthcare utilization (greater reluctance to utilize algorithmic than human providers) was smaller in the intervention condition (3A: β = -0.58,t = -4.50,P < 0.001; 3B: β = -0.01,t = -0.05,P = 0.96) than in the control condition (3A: β = -1.08,t = -8.32,P < 0.001; 3B: β = -0.57,t = -3.31,P = 0.001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Explanations reduce the difference in subjective understanding between algorithmic and human decision-making in study 3A (a) and 3B (c)”
plaindirectn = 798- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 22
- Probably not a substantial claim. 24
- The quote only partly states this claim. 3
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the intervention significantly reduced reported subjective understanding of decisions made by human providers (M pre = 5.08 versus M post = 4.52, Δ H = -0.56,z = -5.27,P < 0.001), but not of decisions made by algorithmic providers (M pre = 3.99 versus M post = 3.83, Δ A = -0.16,z = -1.47,P = 0.14).”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The advertisements generated 14,013 impressions (that is, how many times the ad was viewed across the following demographics: age: 18-24 years: 6.9%, 25-35 years: 5.7%, 35-44 years: 7.3%, 45-54 years: 15.1%, 55-64 years: 35.6%, 65+ years: 29.4%; gender: 78.8% female) and generated 698 clicks.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Click-through rate on Google Ads is a valid measure of healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“creating the predicted provider × understanding interaction (2A: β = -0.80,t = -4.19,P < 0.001; 2B: β = -0.35,t = -1.78,P = 0.08).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, in an ancillary study examining the marketing of skin cancer smartphone applications (Supplementary Appendix 8), we find that performance-related keywords were used to describe 57-64% of the applications, whereas process-related keywords were used to describe 21% of the applications.”
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Results from this experiment show that, relative to a control condition, the interventions used in studies 3A and 3B significantly increased subjective understanding (respectively, β = 0.67, t = 4.90, P < 0.001; β = 0.82, t = 6.05,P < 0.001) but did not influence objective understanding (respectively, P = 0.78 and P = 0.89; all interactions, ts > 3.60, ps < 0.001).”
plaindirectn = 601 ·- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The analyses revealed the predicted significant interaction (Δ H -Δ A = -0.40,z = -2.61,P = 0.009; Fig. 1).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Making people explain provider decision processes reduces their subjective understanding of decisions made by human providers, but not decisions made by algorithmic providers (study 1; N = 297).”
plaindirectn = 297- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Decision processes used by algorithms are perceived to be opaque by patients, because they are often actually opaque to medical professionals and even their creators, who cannot explain them 15,16 .”
What the sources say
- Not checked yetcites [15] Castelvecchi, D. Can we open the black box of AI? Nature 538, 20–23 (2016). source
- Not checked yetcites [16] Kroll, J. A. et al. Accountable algorithms. Univ. Pa. Law Rev. 165, 633 (2016).
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Algorithmic-based healthcare services (medical 'artificial intelligence' (AI)) are cost-effective and scalable and provide expert-level accuracy in applications ranging from the detection of skin cancer 5 and emergency department triage 6,7 to diagnoses of COVID-19 from chest X-rays 8 .”
- Also stated in: § Understanding, explaining, and utilizing medical artificial intelligence · plain
“Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it.”
What the sources say
- Not checked yetcites [5] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). source
- Not checked yetcites [6] Laranjo, L. et al. Conversational agents in healthcare: a systematic review. J. Am. Med. Inform. Assoc. 25, 1248–1258 (2018). source
- Not checked yetcites [7] Goto, T., Camargo, C. A. Jr, Faridi, M. K., Freishtat, R. J. & Hasegawa, K. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Netw. Open 2, e186937–e186937 (2019). source
plain- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Understanding, explaining, and utilizing medical artificial intelligence · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we developed a quiz in consultation with experts in the relevant medical domain (preliminary visual inspection of moles): a team of dermatologists at a medical school in the Netherlands, and a team of developers of a popular skin cancer detection application in Europe.”
plain- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Condition assignments were random in all our studies, with randomization administered by Qualtrics software.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we ran an additional three (intervention: control, study 3A, study 3B) × 2 (understanding: subjective, objective) between-subjects pre-registered experiment with 601 participants on Amazon Mechanical Turk.”
plainn = 601- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All conditions, measures and exclusions are reported; data and pre-registrations are available at https://osf.io/taqp8/.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Amazon Mechanical Turk is an online sample recruiting platform that, although not nationally representative, yields high data quality through the use of attention checks 44 and worker reputation scores 45 .”
What the sources say
- Not checked yetcites [44] Paolacci, G., Chandler, J. & Ipeirotis, P. G. Running experiments on Amazon Mechanical Turk. Judgm. Decis. Mak. 5, 411–419 (2010). source
- Not checked yetcites [45] Peer, E., Vosgerau, J. & Acquisti, A. Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behav. Res. Methods 46, 1023–1031 (2014). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Following prior research, we selected participants with an approval rating above 95% on Amazon Mechanical Turk 45 .”
What the sources say
- Not checked yetcites [45] Peer, E., Vosgerau, J. & Acquisti, A. Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behav. Res. Methods 46, 1023–1031 (2014). source
plain- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Studies 1-3B were approved for use with human participants by the Institutional Review Board on the Charles River Campus at Boston University (protocol 3632E); informed consent was obtained for all participants.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“All manipulations and measures are reported.”
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results suggest an egocentric bias in this illusion of understanding, that the illusion may loom largest in assessments of people-the causal systems most similar to ourselves.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We suggest that these coupled effects make people aware of their limited understanding of medical decisions made by algorithmic providers, but also lead them to overestimate their understanding of (similarly opaque) decisions made by human providers.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Pre-registrations, raw data and Stata syntax files are available on the Open Science Framework at https://osf.io/ taqp8/.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We tested our predictions in a set of five pre-registered online experiments with nationally representative and convenience samples (studies 1, 2A,B and 3A,B: N = 2,699), and a sixth pre-registered online field study on Google Ads (study 4: N = 14,013).”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which could be easily adapted to other domains and procedures.”
hedged- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants were randomly assigned to one of the two provider conditions between subjects, and ratings of subjective understanding were repeated within subjects.”
- Also stated in: § Studies 2A,B. · plain
“Participants were randomly assigned to one of four conditions in a 2 (provider: human, algorithm) × 2 (understanding type: subjective, objective) between-subject design.”
- Also stated in: § Studies 2A,B. · plain
“In both studies, participants were randomly assigned to one of four conditions in a 2 (provider: human, algorithm) × 2 (intervention: control, intervention) between-subject design.”
plain- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Studies 2A,B. · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Improving subjective understanding of how medical AI works may then not only provide beneficent insights for increasing consumer adoption but also for firms seeking to improve their positioning.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Providers tend to present AI provider decisions as a 'black box' because the technologies used in algorithmic-based medical applications are complex.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All experiments were approved for use with human subjects by institutional review boards.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This study was approved by the Erasmus University Erasmus Research Institute of Management Internal Review Board.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“It is impossible to identify, interact with and obtain consent from individual participants.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cronbach's α = 0.65 in the human condition and α = 0.67 in the algorithm condition”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Study 4: Allocation from Google platform”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The present research involved no more than minimal risks, and all study participants were 18 years of age or older.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We screened out N = 211 participants in study 1, N = 255 participants in study 2A, N = 260 participants in study 3A and N = 378 participants in study 3B.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No exclusions in data analyses”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No participant declined participation”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Pre-registrations, raw data and syntax files are available on the Open Science Framework at https://osf.io/taqp8/”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In studies 1, 2A and 3A,B, as pre-registered, we programmed our surveys to automatically exclude participants who failed an attention check at the very beginning of the survey and prior to any manipulation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Finally, people do exhibit an illusion of explanatory depth of causal systems [21][22][23]42 in many domains, claiming to better understand a variety of ideas, technologies and biological systems than they objectively do.”
What the sources say
- Not checked yetcites [21][22][23]42
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Randomization Studies 1, 2A, 2B, 3A, 3B: Qualtrics randomizer”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The literature on algorithm aversion finds that people are generally averse to using algorithms for tasks that are usually done by humans 34,35 , on the grounds that algorithms are perceived as unable to learn and improve from their mistakes 36 , unsuitable for subjective or experiential tasks 37,38 , unable to adapt to unique or mutable circumstances 10,39 and unable to carry responsibility for”
What the sources say
- Not checked yetcites [34] Dawes, R., Faust, D. & Meehl, P. Clinical versus actuarial judgment. Science 243, 1668–1674 (1989). source
- Not checked yetcites [35] Yeomans, M., Shah, A., Mullainathan, S. & Kleinberg, J. Making sense of recommendations. J. Behav. Decis. Mak. 32, 403–414 (2019). source
- Not checked yetcites [36] Dietvorst, B. J., Simmons, J. P. & Massey, C. Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144, 114 (2015). source
- Not checked yetcites [37] Castelo, N., Bos, M. W. & Lehmann, D. R. Task-dependent algorithm aversion. J. Mark. Res. 56, 809–825 (2019). source
- Not checked yetcites [38] Longoni, C. & Cian, L. Artificial intelligence in utilitarian vs. hedonic contexts: the “word-of-machine” effect. J. Market. https://doi.org/10.1177/0022242920957347 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
“Healthcare utilization intentions were measured in study 3A with the item”
“they reported their healthcare utilization intentions.”
The paper relies on this without support.
The skin cancer detection context is representative of medical AI decision-making generally.
“In the same skin cancer detection context as study 1”
“medical AI”
The paper relies on this without support.
Human and algorithmic providers in the studies are comparable in quality or accuracy.
“comparable human providers”
The paper relies on this without support.
Participants' self-reported subjective understanding accurately reflects their subjective understanding.
“participants reported their subjective understanding”
“Subjective understanding was measured in study 3A with the item used in study 2A.”
The paper relies on this without support.
Click-through rate on Google Ads is a valid measure of healthcare utilization.
“tested the impact of that intervention on ad click-through rates.”
The paper relies on this without support.
The Google Ads platform serves ads according to the campaign goal (maximizing clicks), so the frequency of appearance of different ads was not random.
“The frequency of appearance of the different ads was not random, because the Google Ads platform serves ads according to the goal of the campaign (maximizing clicks, in our case).”
The paper relies on this without support.
Providers tend to present AI provider decisions as a 'black box' because the technologies used in algorithmic-based medical applications are complex.
“Because the technologies used in algorithmic-based medical applications are complex, providers tend to present AI provider decisions as a 'black box' .”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Decisions made by human physicians or AI are probably equally unobservable to a patient—but because patients feel that they can understand the decision-making as explained by human providers, they ultimately penalize and resist the clinical use of AI (122).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [122]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“First, it may prevent the formation of reliable case-by-case expectations of AI accuracy [65 , 66] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [65]
Experimental — accuracy not yet measuredIn the citing paper
“Algorithmic aversion refers to a phenomenon where clinicians or healthcare professionals exhibit reluctance or skepticism toward relying on AI algorithms for making diagnostic or treatment decisions in medical imaging.46”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [46]
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentz = -2.61reported p = .009 · recomputed p = .009
“,z = -2.61,p = .009”
- Consistentz = 5.02reported p < .001 · recomputed p < .001
“z = 5.02, p < .001”
- Consistentz = 3.19reported p = .001 · recomputed p = .001
“z = 3.19, p = .001”
- Consistentz = -5.27reported p < .001 · recomputed p < .001
“,z = -5.27,p < .001”
- Consistentz = -1.47reported p = .14 · recomputed p = .142
“,z = -1.47,p = .14”
- Consistentz = 8.17reported p < .001 · recomputed p < .001
“z = 8.17, p < .001”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Studies 2A,B.
“The difference in subjective understanding of the decision made by the human and algorithmic provider was smaller in the intervention condition”
§ Studies 2A,B.
“(3A: β = -0.70,t = -5.41,P < 0.001; 3B: β = -1.10,t = -6.97,P < 0.001) than in the control condition (3A: β = -0.22,t = -1.74,P = 0.08; 3B: β = -0.08,t = -0.52,P = 0.60).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Studies 2A,B.
“Participants claimed greater subjective understanding of medical decisions made by human than algorithmic providers (2A: β = -0.82,t = -6.01,P < 0.001; 2B: β = -0.46,t = -3.33,P = 0.001)”
§ Studies 2A,B.
“We then compared them by regressing understandings on provider (0 for algorithm, 1 for human), understanding type (0 for subjective, 1 for objective) and their interaction.”
Citations
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Cited by
Sorted by citations
Items 201–300 of 338
Li, X., Liao, J.-P., Chen, M.-K., Gao, K., Wang, Y., Yan, S., Huang, Q. Y., Wang, X., Shi, Y., Hu, W., et al. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-02007-1 · Cited by 3
“Additionally, as a main field of computer technology, AI has been applied in medical domains rapidly and widely in recent years, especially in diagnosis generation, therapy selection and risk prediction [10, 11].”
Security and privacy aspects in intelligence systems through blockchain and explainable AI
Thiruthuvanathan, M. M., Kumar, K. P., Nasar, N., Swathikiran, K. K., & Joseph, P. M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95315-3.00011-5 · Cited by 4
On the (Non-)Reliance on Algorithms - A Decision-Theoretic Account
Sinclair‐Désgagné, B. (2021) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2024.102844 · Cited by 2
When and why consumers prefer human-free behavior tracking products
Raveendhran, R., & Fast, N. J. (2024) · Marketing Letters · doi:10.1007/s11002-024-09726-6 · Cited by 2
“Furthermore, people are likely to overestimate their understanding of how humans operate relative to algorithms that can seem opaque (Cadario et al., 2021).”
BackgroundAlgorithm Aversion in Joint Decision‐Making: The Role of Preference for Neutrality
Ulqinaku, A., Çakanlar, A., & Sarial‐Abi, G. (2025) · Psychology and Marketing · doi:10.1002/mar.70041 · Cited by 2
Shiriaev, E. M., Nazarov, A., Kucherov, N. N., & Babenko, M. G. (2024) · Programming and Computer Software · doi:10.1134/s0361768824700117 · Cited by 4
“It could be medical information [39–44], banking [45–48], government [49, 50] and all kinds of personal data.”
Meyers, E. A., Gretton, J. D., Budge, J. R. C., et al. (2022) · doi:10.31234/osf.io/3krx8 · Cited by 2
Understanding Algorithm Aversion: When Do People Abandon AI After Seeing It Err?Preprint
Zhang, Y., & Gosline, R. R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4299576 · Cited by 1
AI and the Evolution of the Patient–Physician Relationship
Barkal, J. L., Stockert, J. W., Ehrenfeld, J. M., & Cohen, L. K. (2023) · AI in Clinical Medicine · doi:10.1002/9781119790686.ch44 · Cited by 3
Psychological ownership influences perceived legal ownership of patient medical dataPreprint
Cadario, R., Shachar, C., Cohen, I. G., & Morewedge, C. K. (2023) · medRxiv · doi:10.1101/2023.04.03.23288075 · Cited by 2
Self-citationBulut, O., Wongvorachan, T., He, S., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3871667/v1 · Cited by 2
Voice Chatbot Design: Leveraging the Preemptive Prediction AlgorithmPreprint
Hao, S., & Xu, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5216183 · Cited by 2
The Behavioural Impact of Artificial Intelligence
Balasooriya, B. A. D. N., Sedera, D., & Sorwar, G. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-4453-8.ch016 · Cited by 2
Intelligent Medicine Box Monitoring System Based on ZigBee Technology
Diao, J., Sun, P., Chen, J. B., Zhang, Z., & Chen, Z. (2024) · International Conference on Power Electronics and Artificial Intelligence (PEAI) · doi:10.1145/3674225.3674391 · Cited by 1
“The application of intelligent medicine box monitoring system can realize the continuous automatic monitoring of temperature and humidity of medicines, respond to environmental changes in time, and ensure that the medicines are in the best condition during the whole transportation process [5].”
Kim, Y., Melzer, A., Kim, S., & Barach, P. R. (2024) · Springer eBooks · doi:10.1007/978-3-031-68574-3_8 · Cited by 1
Liu, T., Wang, H., Feng, F., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107385 · Cited by 1
Ochmann, J., Zilker, S., Michels, L., Tiefenbeck, V., Matzner, M., & Laumer, S. (2025) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/3715966.3715970 · Cited by 1
“…proves that employees tend to perceive algorithms as threatening their jobs, leading to job insecurity and turnover intentions, and to ignore algorithmic outputs when they consider themselves domain experts and are overconfident in their judgments (Cadario et al., 2021; Longoni et al., 2019).”
Tovmasyan, A., Weinstein, N., & Mittelstadt, B. (2025) · Social Influence · doi:10.1080/15534510.2025.2478940 · Cited by 3
“Further, some patients and healthcare providers report resistance to using AI algorithms because of a lack of understanding how the systems may be making decisions (Cadario et al., 2021; Richardson et al., 2021; Shinners et al., 2020).”
Guan, J., Chen, X.-P., Yu, S., & Qin, X. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321173 · Cited by 2
“Robots and many other types of artificial intelligence (AI) are revolutionizing the world in numerous areas, including politics (e.g., political audience diversity) [1], the economy (e.g., labor markets) [2], society (e.g., religion) [3], and technology (e.g., medicine) [4].”
The Role of Artificial Intelligence Algorithms in Personalized Design of Visual Communication
Sun, H. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10968996 · Cited by 1
“GAN learns from a large number of image data to generate new image samples, which are perceptually from real images [15].”
The Role of Empathy in Critical Reasoning and the Limitations of Medical AI Systems
Favaretto, M., & Stroh, K. (2025) · The Journal of Medicine and Philosophy A Forum for Bioethics and Philosophy of Medicine · doi:10.1093/jmp/jhaf022 · Cited by 1
Chen, K. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/5581926 · Cited by 1
Trust is all you Need: Reinforcing the Patient-physician Bond in Times of AI
Reis, F., Reis, M., Drzeniek, N. M., & Balzer, F. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02296-8 · Cited by 2
“Trust can erode when patients are unaware of how AI is used or, worse, imagine it to be a cold replacement for human insight [12].”
Du, G., Zhou, C., & Han, Z. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2586812 · Cited by 2
“Through six experiments, Cadario et al. (2021) found that patient’s resistance to the use of medical AI primarily stems from a lack of understanding of the algorithms involved and a misconception of human medical decision-making.”
Lai, K., Na, Y., & Wang, H. (2026) · Current Psychology · doi:10.1007/s12144-025-08583-4 · Cited by 1
“We used the Cadario et al. ( 2021) three-item scale to measure the participants’ subjective knowledge of medical AI.”
Bai, J., Yu, F., & He, X. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27031165 · Cited by 1
The persuasive effects of AI-generated digital human avatar videos in public legal education
Xu, L., Su, S., Hu, C., & Wang, X. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2621779 · Cited by 1
“This finding is consistent with prior research, which suggests that in domains emphasising objective reasoning, people may have more positive attitudes toward and greater trust in AI (Cadario, Longoni, and Morewedge 2021; Longoni and Cian 2022).”
InfluentialLiao, G., Ren, X., Zheng, X., & Zhang, Y. (2026) · Behavioral Sciences · doi:10.3390/bs16020248 · Cited by 2
“Permissibility was measured using the scale developed by Bigman and Gray (2018); liking for algorithms was measured using Jago’s (2019) scale; and utilization intention was measured with items from Cadario et al. (2021).”
Zhang, C., Luo, J., Abuliezi, Z., & Wang, W. (2026) · Current Psychology · doi:10.1007/s12144-026-09210-6 · Cited by 1
“Despite growing interest in AI’s broader social consequences, most research remains focused on perceptions and attitudes toward AI (e.g., Cadario et al., 2021; De Freitas et al., 2023 ) and its instrumental benefits (Kim & Cho, 2023; Crawford et al., 2024).”
Hampton, L., & Poquiz, J. L. S. (2026) · Journal of Economic Surveys · doi:10.1111/joes.70081 · Cited by 5
Zhao, Q., & Li, H. (2026) · Personality and Individual Differences · doi:10.1016/j.paid.2026.113786 · Cited by 1
AI-Enabled Digital Health Promotion and Prevention: Computational Literature Review
Carrilho, M., Pinto, D. C., Wagner, R., Rohden, S. F., de Arriaga, M. T., & Pinto, L. Q. (2026) · JMIR AI · doi:10.2196/84492 · Cited by 1
Understanding Artificial General Intelligence: Defining Characteristics and Benchmarks
Muhsen, D. K., & Sadiq, A. T. (2025) · Journal of Artificial Intelligence & Control Systems · doi:10.66967/jaics.2025.v1i103 · Cited by 1
The More You Know, the Less You Trust
Lee, J., Keil, M., & Lee, J. S. (2026) · Information Resources Management Journal · doi:10.4018/irmj.414355 · Cited by 1
AI Assistant in Online Pharmacy
Shen, T., Liang, C., Peng, J., Guan, M., & Li, J. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.461 · Cited by 2
Yamim, A. P., Mai, R., & Jörling, M. (2024) · Recherche et Applications en Marketing (English Edition) · doi:10.1177/20515707241283230 · Cited by 1
“In healthcare, individuals are hesitant to use technology due to uniqueness neglect (Longoni et al., 2019) and the opaque characteristics of technology decision processes (Cadario et al., 2021).”
Enhancing visual and semantic alignment of multimodal large models in medical images
Liu, Q., Fang, Y., & Liu, X. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07859-7 · Cited by 1
“With the rapid advancement of artificial intelligence (AI) [7], deep learning models [8], particularly visual-language model [9], have demonstrated exceptional performance in extracting meaningful features from medical images.”
General Framework for Using AI in Clinical Practice
Barkal, J. L., Stockert, J. W., Ehrenfeld, J. M., Aunger, C. E., & Cohen, L. K. (2023) · AI in Clinical Medicine · doi:10.1002/9781119790686.ch2 · Cited by 1
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Marmolejo‐Ramos, F. (2023) · doi:10.31234/osf.io/9wh2f · Cited by 1
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On Preferring People to AlgorithmsPreprint
Kaiser, M., Sunstein, C. R., & Reisch, L. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5157745 · Cited by 1
Students Choose Human Counselors Over Algorithms in College Applications, but Not AlwaysPreprint
Das, H., Goulas, S., & Monachou, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5282793 · Cited by 1
Nizette, F., & Hammedi, W. (2025) · Springer eBooks · doi:10.1007/978-3-031-75316-9_25-1 · Cited by 1
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From Expectation to Evaluation: Expectation Cues Systematically Bias LLM and Human Judgment
Sun, Y., Dillion, D., Gray, K. J., Lyu, M., Zhang, Z., & Li, F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790492 · Cited by 1
“Prior beliefs [12], stereotypes [9, 19], and contextual cues [63, 77] shape how information is encoded and integrated, often producing systematic distortions in domains such as impression formation [6], educational assessment [79], medical decision-making [61], and consumer behavior [47].”
Trust Formation in AI Delegation: The Interplay of Explainability and Anthropomorphism
Li, C., Deng, Z., Ling, H., & ZHANG, X. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3793215
“This shift is particularly salient in high-stakes consumer contexts , where AI agents are increasingly entrusted with complex and personalized tasks such as financial planning [9], product delegation (e.g., voice-assisted commerce like Amazon Echo) [30, 53], or medical diagnosis [6, 46, 57].”
Ausserhofer, D., Colletti, N., Lombardo, S., Gärtner, T., Rina, P., Barbieri, V., von Strobele-Prainsack, D. H., Piccoliori, G., & Wiedermann, C. J. (2026) · doi:10.2196/preprints.109238 · Cited by 1
A single latent channel is sufficient for biomedical image segmentationPreprint
Kist, A. M., Schützenberger, A., Dürr, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.10.472122
“However, these deep neural networks have commonly a black box character, lowering their acceptance in a clinical environment [11].”
BackgroundOn the (Non-)Reliance on Algorithms - A Decision-Theoretic AccountPreprint
Sinclair‐Désgagné, B. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3952166
Technoism: A new form of prejudice and discriminationPreprint
Jörling, M., Heuvinck, N., de Kerviler, G. C., & Rucker, D. D. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/jtbc3
Technoism: A new form of prejudice and discriminationPreprint
Jörling, M., Heuvinck, N., de Kerviler, G. C., & Rucker, D. D. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/6rxhs
Parental Trust in Automated Detection of Cyberpredators
Brandimarte, L. (2023) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro57284.2023.10159713
“In another set of experiments in the context of medical decision making [26], researchers have shown that algorithm aversion is, at least in part, caused by the fact that algorithms are perceived as inscrutable black boxes, more so than recommendations made by human doctors.”
BackgroundSupplemental Material for Exposure to Robot Preachers Undermines Religious Commitment
(2023) · Journal of Experimental Psychology General · doi:10.1037/xge0001443.supp
Chung, S., Lee, C. H., & Zheng, E. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4548973
The Narrow-Taste Effect: When Consumers Display Narrow Tastes to Algorithmic RecommendersPreprint
Gai, P. J., Leung, E., & Klesse, A.-K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4585195
New Artificial Intelligence System Based on Information Processing Technology
Tian, Z. (2023) · International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI) · doi:10.1109/icdacai59742.2023.00111
“The new AI system based on information processing technology can be studied from five directions: reinforcement learning, NLP, image recognition and processing, data mining and knowledge discovery, and human-computer interaction, as shown in Figure 1 [17].”
Mani, Z., & Chouk, I. (2024) · Décisions Marketing · doi:10.3917/dm.112.0007
Liu, Y., & Mu, Y. (2024) · Journal of Information Analysis · doi:10.53964/jia.2024007
“(5) The main path analysis of the medical AI research shows some important studies, including Cadario et al. [41] , Yokoi et al. [42] , and Yun et al. [43] These studies commonly refer to the study of Longoni et al [35] .”
BackgroundAdoption of Medical AI and its Effect on Speciesism
Abangbila, L., Li, X., Agordzo, G. K., & Fomude, A. H. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575005
“Our findings demonstrate that resistance to the adoption of medical artificial intelligence is influenced by two factors: the perceived complexity of algorithms (viewed as a 'black box') and a false sense of understanding regarding human medical decision-making [15].”
BackgroundThe patient perspective on big data and its use in clinical care
Tai, K., Babu, C., Kim, Y. E., Subramanian, T., & Rameau, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-10520-3.00004-6
Role of pandemic in driving adoption of artificial intelligence in healthcare industry
Oumaihka, K. (2024) · Computer Science & IT Research Journal · doi:10.51594/csitrj.v5i7.1358
McCarley, J. S., Knight, M., & Fletcher, K. I. (2024) · Ergonomics · doi:10.1080/00140139.2024.2386562
“…(Reiner et al. 2017), and might be moderated by aid design and information about aid function disclosed to the user (Barg-Walkow and Rogers 2016; cadario, longoni, and Morewedge 2021; e.g. chen et al. 2018; Neyedli, hollands, and Jamieson 2011; Wang, Jamieson, and hollands 2009). in any case,…”
Wang, Y., Banerjee, C., Chucri, S., & Chen, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4930677
The GP’s guide to Artificial Intelligence (AI) in medicine
Outhoff, K., & Trathen, K. (2024) · South African General Practitioner · doi:10.36303/sagp.0954
Yamim, A. P., Mai, R., & Jörling, M. (2024) · Recherche et Applications en Marketing (French Edition) · doi:10.1177/07673701241285335
“Dans le secteur des services médicaux, les individus hésitent à utiliser la technologie en raison de la perte de singularité (Longoni et al., 2019) et des caractéristiques opaques des processus décisionnels technologiques (Cadario et al., 2021).”
Nishida, S. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100104
Adaptive Statistical Iterative Reconstruction Algorithm Based on Artificial Intelligence
Li, C., & Zhang, Y. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.10.026
Algorithm Adoption and Explanations: An Experimental Study on Self and Other PerspectivesPreprint
Bravo, F., He, Z., Shaposhnik, Y., & Valdés, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5017247
Shiriaev, E. M., Nazarov, A., Kucherov, N. N., & Babenko, M. G. (2024) · Программирование · doi:10.31857/s0132347424040036
Intelligent Connected Vehicle Information Security Detection System Based on Artificial Intelligence
Liu, D., Cheng, X., Zhang, Z., Liu, Z., & Ma, Y. (2025) · Sustainable civil infrastructures · doi:10.1007/978-3-031-72509-8_28
Application of Personal Information Privacy Protection Based on Artificial Intelligence Algorithms
Mu, X. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2794-3_37
Sayres, R., Devon-Sand, A., Schaekermann, M., Smith, M. A., Strachan, P., Hong, G., Nguyen, T., Wong, D., Hammel, N., Siegel, D. H., et al. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720531 · Cited by 1
“Previous work has indicated potential benefits and risks with consumer-oriented AI health tools: They may extend access to high-quality care, but also lead to systematic over-or under-diagnosis [1, 5, 29].”
Discussions on Neuroscience of Decision-Making
Hou, Y. (2025) · Psychomachina · doi:10.59388/pm00357
The Negative Effects of Positive Real-Time Feedback from Artificial IntelligencePreprint
Kinch, M. A. (2025) · Social Science Research Network · doi:10.2139/ssrn.5249994
Li, X., Xiang, H., & Zhou, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92712-6_26 · Cited by 1
Explaining Model Behavior Across Space and Time: Differential and Intertemporal ExplanationsPreprint
He, Z., & Shaposhnik, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5277675
“Subjective Biased Perception on AI Knowledge and AI Aversion”
Oezguer, K., Jan, S., & Thomas, L. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00070
“Interestingly, recent research on AI aversion [5, 12] suggest that rather than their objective AI knowledge it is individuals’ confidence in their AI knowledge (i.e., how they [in]correctly assess their knowledge of AI systems) that can lead to AI aversion.”
Reframing the AI Doctor debate: the comparison should not be with Doctors but Doctorlessness
Tunçdoğan, A., Acar, O. A., & Car, J. (2025) · Discover Health Systems · doi:10.1007/s44250-025-00264-0
Zhang, Y., Fast, N. J., & Schroeder, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5332391 · Cited by 1
Majouni, S., Tennankore, K., & Abidi, S. S. R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5355115
Tackling AI Transparency Concerns in Biomedical Research
Arzuaga, L. S. (2025) · Routledge eBooks · doi:10.4324/9781032634487-12
Qin, Z., Zhang, P., Fu, H., Yan, S., & Li, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5350604
Jia, F., Gao, X., & Zeng, S. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3590647
“Cadario et al. found that resistance to healthcare AI stems from perceived difficulty understanding algorithms (viewing them as “black boxes”) and overestimation of human medical decision-making [50].”
Credibility Assessment of Human–Generative AI Interaction
Zhao, Y., Song, S., & Jing, Y. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009587877.005
Oliveira, E. D. S., & Mantovani, D. (2025) · ReMark - Revista Brasileira de Marketing · doi:10.5585/2025.26110
Carrilho, M., Pinto, D. C., Wagner, R., Rohden, S. F., de Arriaga, M. T., & Pinto, L. Q. (2025) · doi:10.2196/preprints.84492
Chalyy, K. A., & Kryvenko, I. (2025) · Medical Informatics and Engineering · doi:10.11603/mie.1996-1960.2024.3-4.15460
From Aversion to Adoption: The Role of Promotion Design in Mitigating Algorithm AversionPreprint
Zheng, Z., Hao, S., Xu, Y., & Ghose, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5634710
McCarthy, M. P. (2025) · HEC Forum · doi:10.1007/s10730-025-09567-4
“While supervised ML utilizes identifiable neural networks, DL uses more neural networks in which the outputs are not always explainable (Aung et al., 2021 ; Cadario et al., 2021 ).”
Strategic (Non-)Investment in AI Pricing: How Does Price Monitoring Deter AI Adoption?Preprint
Gu, D., Lu, T., & Zhou, W. H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5823562
Harnessing AI wisely for a more equitable world
Liu, Q., Guo, F. S., Guo, Z., Dong, Y., & Bryan, B. A. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.01.023
hong, Z., qu, S., Zhang, L., Zhou, Y., gui, W., & Zhang, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6100674
Cui, S., Wang, S., Deng, J., Jia, R., & Jiang, Y. (2026) · Healthcare · doi:10.3390/healthcare14020268 · Cited by 2
“This is consistent with the findings of Cadario R et al. [38].”
Rethinking Psychological Ownership Over AI Agents
Cheng, J. M., Xu, Y.-X., & Kuzminykh, A. (2025) · Conference of the Centre for Advanced Studies on Collaborative Research · doi:10.1109/cascon66301.2025.00119
“Furthermore, regarding self-efficacy, research consistently shows that users report limited understanding of AI functionality, view their AI agents as opaque systems, and express concern that they are “not using the agents to the fullest” [32]–[35].”
Nizette, F., & Hammedi, W. (2026) · Springer eBooks · doi:10.1007/978-3-031-75316-9_25-3
Zeng, R., Zhu, D., & Evans, R. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06573-w · Cited by 4
Public perceptions of AI-assisted cancer care in Abu Dhabi, UAE: A cross-sectional survey
Ghemrawi, R., Sakkal, M., Kayed, K., Mousa, W., & Khair, M. (2026) · Supportive Care in Cancer · doi:10.1007/s00520-026-10389-4
Niu, W., Fang, J. Y., & Zhang, W. (2026) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2026.104794 · Cited by 2
AI appreciation effect: how contagious diseases influence medical AI adoption
Hu, Y., & Ding, Y. (2026) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2026.03.001
Moral Punishment in Generative AI Discrimination
张, 玥. (2026) · Advances in Psychology · doi:10.12677/ap.2026.163154
Metadata
- Authors
- Cadario, R., Longoni, C., & Morewedge, C. K.
- Affiliations
- Erasmus University Rotterdam (NL)
- Boston University (US)
- Corresponding author
- Cadario, Romain
- Venue
- Nature Human Behaviour
- Published
- 2021-06-28
- Volume
- 5
- Issue
- 12
- Pages
- 1636-1642
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3095202475
- Semantic Scholar
- 2dbd832cce2b900341e0634995e5a093c23ccb0b
- Cited by
- 342
- References
- 48
- Citations by year
- 2026: 74 · 2025: 93 · 2024: 87 · 2023: 54 · 2022: 23 · 2021: 11
- FWCI
- 15.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion, public-domain)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (acceptedVersion, public-domain)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- Erasmus Research Institute of Management
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- artificial intelligence in healthcare, algorithmic decision-making, trust in AI, resistance to AI adoption, algorithmic transparency, brief intervention, skin cancer detection, Google AdWords
- MeSH terms
- Clinical Decision-Making, Adult, Algorithms, Artificial Intelligence, Delivery of Health Care, Female, Humans, Male, Middle Aged
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