From Oracles to Productive Friction: Designing for Meaningful Human Oversight in Clinical AI
Open accessAbstract
We propose productive friction as a design principle for clinical AI: the deliberate, graded introduction of interaction cost when accepting machine recommendations, calibrated to case uncertainty so that analytical reasoning is activated without disrupting workflow. We ground this principle in a mixed-method study with eleven practising radiologists interacting with two contrasting prototypes, an oracular system delivering a single diagnosis and an evaluative system presenting multiple differentials with reference exemplars. Results reveal a consistent preference for evaluative support in complex cases, where it enhances diagnostic reasoning, confidence, and reflective engagement, while oracular outputs were favoured for routine tasks. Radiologists described evaluative support as "accompanying" their thinking, preserving professional agency and offering pedagogical value. We argue that productive friction carries direct ethical significance: when agreeing with an AI recommendation requires less effort than disagreeing, the clinician's role risks becoming procedural rather than a genuine locus of judgement. We operationalise this principle along four dimensions, temporal delay, option cardinality, evidence depth, and commitment structure, and translate it into implementable design mechanisms for adaptive clinical AI.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes “productive friction” as a design principle for clinical AI, operationalised through temporal delay, option cardinality, evidence depth, and commitment structure to preserve meaningful human oversight. In a mixed-method design-probe study with 11 practising radiologists, evaluative differential-first AI was preferred for complex or uncertain cases, while oracular single-answer AI was preferred for routine and time-critical tasks.
Key findings
- The evaluative prototype showed stronger perceived fit: Facilitating Conditions median 5.0 vs. 3.4, r = .85; Job Relevance r = .80; Anxiety r = .67; Result Demonstrability r = .63; Performance Expectancy r = .62; Attitude toward Use r = .55; Behavioural Intention r = .42; Effort Expectancy r = .34.
- Oracular support was preferred for simple, binary decisions, routine straightforward cases, time-critical ER triage, and high-volume repetitive tasks; evaluative support was favoured for complex diagnostic cases, ambiguous or atypical findings, and learning contexts.
- Qualitatively, evaluative support was described as “accompanying” thinking and “making me reason,” while oracular support risked making clinicians feel “totally passive” or “blocked”; participants reported that spending “one extra minute” considering alternatives increased confidence.
- Baseline technology anxiety was low (mean 1.37, SD = 0.38), and 10 of 11 participants had prior professional AI experience.
Methods
- Mixed-methods, within-subject proof-of-concept design-probe study with 11 practising radiologists in a Swiss public regional hospital; each participant completed 10 diagnostic tasks with each of two simulated prototypes—oracular (single top diagnosis) and evaluative (3–4 differentials with evidence and exemplars)—in randomised order, using think-aloud sessions, an adapted UTAUT/TAM survey, and semi-structured interviews.
- Quantitative analysis used 5-point Likert UTAUT constructs, with within-participant contrasts summarised by medians, interquartile ranges, mean paired differences, and rank-biserial effect sizes; due to N = 11, quantitative results were descriptive and triangulating.
- Qualitative analysis used hybrid inductive-deductive thematic analysis of interview and think-aloud transcripts, with a final codebook of 19 codes and 4 high-level themes.
Limitations
- Stated: single-speciality, single-country sample likely shaped by self-selection; design-probe captures preferences and reasoning rather than behavioural outcomes; ecological validity is limited for this proof-of-concept; quantitative contrasts are hypothesis-generating and imprecise at this scale; fixed case-to-prototype assignment introduces a potential confound that complexity matching mitigates but does not eliminate.
- Evident: the sample is small (N = 11), and the prototypes used static single-slice presentation with minimal clinical context, which participants described as incompatible with normal practice; the study did not test live PACS use or real time pressure.
- Evident: the authors state they did not pursue conventional inter-coder reliability, instead using a collaborative analytic approach.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Claims the paper makes
4 of 24 cited sources checked · 2 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Radiological diagnosis is a distributed, collaborative process embedded in a professional infrastructure of routines, norms, and tacit expectations that shape both vision work and how expertise is recognised.
“Radiological diagnosis is a distributed, collaborative process embedded in a professional infrastructure of routines, norms, and tacit expectations that shape both vision work (Anichini, Natali, and Cabitza 2024) and how expertise is recognised.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Anichini, Natali, and Cabitza 2024 source
- Premisecitation
Recent evidence shows that deliberation interventions reduce overreliance only when tasks are sufficiently complex that engaging with the explanation is worth the cognitive cost.
“Recent evidence shows that deliberation interventions reduce overreliance only when tasks are sufficiently complex that engaging with the explanation is worth the cognitive cost(Vasconcelos et al. 2023), suggesting uniform friction imposes avoidable cost on routine cases.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Vasconcelos et al. 2023 source
The Study 1 results (section labeled “49 H1E no”; page unavailable) report that explanations did not observably reduce overreliance on easy or medium maze tasks, but did on hard tasks where reviewing the explanation offered a substantial cognitive-effort benefit. The claim broadens that finding from AI explanations in these tasks to deliberation interventions generally; the paper does not establish an “only when” rule for all such interventions. (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Premisecitation
AI-after conditions reduce overreliance on hard tasks at a modest time cost.
“consistent with experimental evidence that 'AI-after' conditions reduce overreliance on hard tasks at a modest time cost (Swaroop et al. 2024).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Swaroop et al. 2024 source
Section 5.2 reports that, under time pressure, AI-after improved overreliers’ accuracy on hard questions from 55% to 70% by slowing them down. The paper’s results tables show lower overreliance with AI-after overall, but the cited passages do not establish a reduction in overreliance specifically on hard questions or characterize the additional time as modest. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Findingown data
Participants saw potential in AI for coauthoring, not auto-authoring, the clinical report.
“They also saw potential in AI for coauthoring, not auto-authoring, the clinical report.”
Quote in the paperSubstantial claim - Findingnone given
Clear distinctions emerged between the two prototypes, mapped consistently onto task complexity, time pressure, and user expertise.
“Clear distinctions emerged between the two prototypes, mapped consistently onto task complexity, time pressure, and user expertise.”
Quote in the paperNot a substantial claim
- Findingown data
Participants saw clear value in prioritising urgent findings and emergency cases.
“Participants saw clear value in prioritising urgent findings and emergency cases (P2, P6).”
Quote in the paperNot a substantial claim - Findingown data
Interaction design plays as critical a role as model accuracy in shaping whether AI supports or supplants clinical reasoning.
“Through a design-probe study with practising radiologists, we contrasted oracular and evaluative paradigms of AI decision support, finding that interaction design plays as critical role as model accuracy in shaping whether AI supports or supplants clinical reasoning.”
Quote in the paperSubstantial claim - Findingown data
Several participants described a productive 'extra minute' that improved confidence when the system prompted consideration of alternatives.
“Several participants described a productive 'extra minute' that improved confidence when the system prompted consideration of alternatives (P3, P5, P7), suggesting that even modest increases along these dimensions yield disproportionate gains in reflective engagement.”
Quote in the paperSubstantial claim - Findingown data
Radiologists valued oracular speed, particularly for routine cases and under time pressure.
“Radiologists simultaneously valued oracular speed, particularly for routine cases and under time pressure, and resisted its prescriptive framing when uncertainty was present.”
Quote in the paperSubstantial claim - Findingown data
Participants did not experience the four productive friction dimensions as abstract interface properties but as felt differences in agency.
“Crucially, participants did not experience these dimensions as abstract interface properties but as felt differences in agency.”
Quote in the paperSubstantial claim - Findingown data
Radiologists resisted oracular prescriptive framing when uncertainty was present.
“Radiologists simultaneously valued oracular speed, particularly for routine cases and under time pressure, and resisted its prescriptive framing when uncertainty was present.”
Quote in the paperSubstantial claim - Findingown data
Temporal delay surfaced when P3 and P8 described forming an impression before AI output and gaining reflective distance from the suggestion.
“Temporal delay surfaced when P3 and P8 described forming an impression before AI output and gaining reflective distance from the suggestion.”
Quote in the paperSubstantial claim - Findingown data
The evaluative prototype was seen as a learning aid that encouraged comparison and deeper engagement with cases, particularly through reference images and the breadth of differential hypotheses offered.
“The evaluative prototype was seen as a learning aid that encouraged comparison and deeper engagement with cases, particularly through reference images and the breadth of differential hypotheses offered.”
Quote in the paperSubstantial claim - Findingreasoning
Participants explicitly rejected outputs that hide the basis of a suggestion.
“Since definitive claims without evidence clash with how radiologists reason and report (P7, P1), participants explicitly rejected outputs that hide the basis of a suggestion (P4, P8, P10).”
Quote in the paperSubstantial claim - Interpretationreasoning
The responsibility gap identified by Matthias (2004) for learning automata transposes to the human-AI decision dyad.
“This transposes to the human-AI decision dyad the 'responsibility gap' that Matthias (2004) identified for learning automata.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Matthias (2004) source
- Interpretationcitation
The tool shapes the decision space even if responsibility remains formally human.
“Re- sponsibility may remain formally human, yet the tool still shapes the decision space”
Quote in the paperSubstantial claim - Study design and conductown data
Complexity ratings obtained during baseline assessment ensured that both prototypes presented comparable task difficulty.
“Complexity ratings obtained during baseline assessment ensured that both prototypes presented comparable task difficulty.”
Quote in the paperSubstantial claim - Interpretationreasoning
When accepting AI output is easier than rejecting it, the clinician retains legal liability without having performed the cognitive work that liability presupposes.
“Where accepting AI output is easier than rejecting it, the clinician retains legal liability without having performed the cognitive work that liability presupposes.”
Quote in the paperSubstantial claim - Interpretationreasoning
Productive friction, operationalised through temporal delay, option cardinality, evidence depth, and commitment structure, preserves the epistemic conditions under which a signed report represents a judgement rather than a default acceptance.
“Productive friction, operationalised through temporal delay, option cardinality, evidence depth, and commitment structure, preserves the epistemic conditions under which a signed report represents a judgement rather than a default acceptance.”
Quote in the paperSubstantial claim - Interpretationreasoning
The ease of accepting an AI recommendation influences the clinician's exercise of judgement, on which moral and legal accountability depends.
“The ease of accepting an AI recommendation influences the clinician's exercise o their the judgement, on which moral and legal accountability depends.”
Quote in the paperSubstantial claim - Study design and conductreasoning
The single-speciality, single-country sample, likely shaped by self-selection, may limit generalisability to broader radiology populations or different institutional settings.
“The single-speciality, single-country sample, likely shaped by self-selection of research-engaged clinicians, may limit generalisability to broader radiology populations or different institutional settings.”
Quote in the paperNot a substantial claim - Study design and conductnone given
Both prototypes used the same visual layout, patient metadata, and typology of TC slices to ensure parity of visual input.
“Both prototypes used the same visual layout, patient metadata, and typology of TC slices to ensure parity of visual input.”
Quote in the paperSubstantial claim - Premisecitation
The easier it is to accept a recommendation, the more likely automation bias becomes.
“since the easier it is to accept a recommendation, the more likely automation bias becomes (Skitka, Mosier, and Burdick 1999)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Skitka, Mosier, and Burdick 1999 source
- Interpretationreasoning
The efficiency-agency tension is a persistent feature of the design space.
“We therefore approach this tension as a persistent feature of the design space, one that adaptive, context-sensitive support must address by calibrating deliberation cost to diagnostic uncertainty.”
Quote in the paperSubstantial claim - Interpretationcitation
Frictionless support over months and years may cumulatively erode the very expertise on which meaningful oversight depends.
“Third, frictionless support over months and years may cumulatively erode the very expertise on which meaningful oversight depends (Natali et al. 2025a).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Natali et al. 2025a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Bucinca, Malaya, and Gajos (2021) identified a satisfaction cost for uniform cognitive forcing.
“directly addressing the satisfaction cost that Buc ¸inca, Malaya, and Gajos (2021) identified for uniform cognitive forcing.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Buc ¸inca, Malaya, and Gajos (2021)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The efficiency-agency tension maps directly onto the four productive friction dimensions.
“The efficiency-agency tension formalised in our design conjecture maps directly onto the four productive friction dimensions introduced in Section 2 and summarised in Table 1.”
Quote in the paperSubstantial claim - Premisecitation
Swaroop et al. (2025) showed that adapting the type of AI assistance based on each user's observed overreliance rate improves human-AI team performance compared to uniform assistance.
“Swaroop et al. (2025) showed that adapting the type of AI assistance based on each user's observed overreliance rate (providing higher-friction support to users who tend to accept AI suggestions uncritically, and lower-friction support to well-calibrated users) improves human-AI team performance compared to uniform assistance.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Swaroop et al. (2025) source
- Premisereasoning
These traditions have addressed aesthetic, temporal, and infrastructural friction without being applied to the specific problem of preserving the epistemic conditions for accountability in AI-assisted clinical decisions.
“However, these traditions have addressed aesthetic, temporal, and infrastructural friction without being applied to the specific problem of preserving the epistemic conditions for accountability in AI-assisted clinical decisions, a gap the next subsection addresses.”
Quote in the paperSubstantial claim - Interpretationreasoning
The two paradigms stage a productive tension between efficiency and professional agency.
“Beyond differences in preference rankings, the two paradigms stage a productive tension between efficiency and professional agency.”
Quote in the paperSubstantial claim - Interpretationreasoning
In such cases, the clinician's signature functions as formal sign-off without the deliberation it is meant to certify.
“In such cases, the clinician's signature continues to function as formal sign-off, even though the deliberation it is meant to certify has not occurred.”
Quote in the paperSubstantial claim - Interpretationreasoning
The oracular model is especially likely to be organisationally disruptive.
“This is especially true of the oracular model.”
Quote in the paperSubstantial claim - Premisecitation
A design-probe approach lets researchers observe how interface choices, not only model accuracy, shape collaborative performance and user agency.
“This design-probe approach, having each participant evaluate and comment on contrasting interaction paradigms instead of a single model, lets us observe how interface choices, not only model accuracy, shape collaborative performance and user agency (Hutchinson et al. 2003).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hutchinson et al. 2003 source
- Interpretationcitation
A system efficient at the point of interaction may still be organisationally disruptive if it displaces interpretive labour, obscures uncertainty, or shifts accountability to the wrong actors.
“A system efficient at the point of interaction may still be organisationally disruptive if it displaces interpretive labour, obscures uncertainty, or shifts accountability to the wrong actors (cfr.articulation work by Schmidt and Bannon 1992).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Schmidt and Bannon 1992 source
- Interpretationreasoning
Each of the four productive friction dimensions constitutes a designable lever through which deliberation cost can be calibrated to diagnostic uncertainty.
“Each dimension constitutes a designable lever through which deliberation cost can be calibrated to diagnostic uncertainty, such that it remains high enough to activate analytical reasoning in complex cases, and low enough to avoid disrupting routine workflow.”
Quote in the paperSubstantial claim - Interpretationown data
Efficiency and professional agency form a persistent design condition irreducible to a choice between them.
“They reveal a structural feature of clinical AI design, one in which efficiency and professional agency form a persistent design condition irreducible to a choice between them.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The friction mechanisms generate auditable interaction traces that could serve as accountability infrastructure.
“First, the friction mechanisms we propose generate auditable interaction traces, pre-AI impressions, evidence inspection patterns, justification entries, that could serve as accountability infrastructure, providing regulators with designable evidence of meaningful oversight.”
Quote in the paperSubstantial claim - Premisecitation
Clinical AI uptake often stalls when systems behave like oracles, issuing definitive answers that neither fit clinicians' workflows nor respect the inherent uncertainty of diagnosis.
“Clinical AI has made rapid technical strides, yet its uptake in practice often stalls when systems behave like oracles, issuing definitive answers that neither fit clinicians' workflows nor respect the inherent uncertainty of diagnosis (Parasuraman and Manzey 2010;Cabitza, Rasoini, and Gensini 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
Frictionless compliance creates a gap between formal responsibility and substantive responsibility.
“Frictionless compliance creates a gap between formal responsibility, where the clinician signs the report, and substantive responsibility, where the clinician has exercised judgement.”
Quote in the paperSubstantial claim - Interpretationreasoning
Introducing productive friction functions as an ethical safeguard by preserving the epistemic conditions under which responsibility can be meaningfully attributed to a clinician.
“Introducing productive friction in interaction can therefore be seen as more than a design principle; by preserving the epistemic conditions under which responsibility can be meaningfully attributed to a clinician, it functions as an ethical safeguard.”
Quote in the paperNot a substantial claim
- May combine two claims. 38
- Classified as probably not a substantial claim. 32
May combine two claims.
PremisecitationWithholding AI assistance until after the user forms an independent judgment reduces overreliance compared to presenting it upfront, though at the cost of decision time.
“Withholding AI assistance until after the user forms an independent judgment reduces overreliance compared to presenting it upfront, though at the cost of decision time(Swaroop et al. 2024).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Swaroop et al. 2024 source
May combine two claims.
FindingreasoningThe Oracular prototype retains merit for streamlining routine confirmations and quick verification in straightforward cases, but its prescriptive framing was consistently resisted under uncertainty.
“The Oracular prototype retains merit for streamlining routine confirmations and quick verification in straightforward cases, but its prescriptive framing was consistently resisted under uncertainty.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataRadiologists emphasised that outputs remain editable and accountable.
“Although auto-generated text can save time, radiologists emphasised that outputs remain editable and accountable (P8, P2).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBehavioural Intention (r = .42) and Effort Expectancy (r = .34) showed smaller effects, indicating less differentiation between prototypes in adoption intent and perceived ease of use.
“Behavioural Intention (r = .42)and Effort Expectancy (r = .34)showed smaller effects, indicating less differentiation between prototypes in adoption intent and perceived ease of use.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataExperts preferred fast confirmation, while trainees stood to benefit from exploratory systems that supported learning.
“Differences across expertise levels reinforced the pattern.Experts preferred fast confirmation (P2), while trainees stood to benefit from exploratory systems that supported learning (P8).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOracular outputs were valued for speed in routine cases but resisted when they foreclosed deliberation under uncertainty.
“Evaluative, differential-first interfaces activated reflection, preserved professional agency, and aligned with the probabilistic logic of radiological diagnosis, while oracular outputs were valued for speed in routine cases but resisted when they foreclosed deliberation under uncertainty.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe largest effects appeared for Facilitating Conditions (Mdn = 5.0 vs. 3.4, r = .85) and Job Relevance (r = .80).
“The largest effects appeared for Facilitating Conditions (Mdn = 5.0 vs. 3.4, r = .85)and Job Relevance (r = .80),indicating that radiologists perceived the evaluative paradigm as substantially better aligned with their work context and professional needs.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataParticipants reported generally low levels of technology-related anxiety (mean 1.37, SD 0.38).
“The mean score was 1.37 (SD = 0.38), indicating that respondents overall did not feel anxious about engaging with new technology, including AI systems.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataContext-specific preferences resolved into a stance of selective reliance, with AI as a guide in unfamiliar domains but secondary to one's own judgement in routine cases.
“These contextspecific preferences resolved into a stance of selective reliance, with AI as a guide in unfamiliar domains, but secondary to one's own judgement in routine cases (P6).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTrainees benefited from the exploratory structure, while experienced radiologists sometimes found multiple options redundant for routine tasks.
“Trainees in particular benefited from the exploratory structure in ways that experienced practitioners did not need; experienced radiologists, by contrast, sometimes found multiple options redundant for routine tasks (P2), reinforcing the case for adaptivity rather than uniform depth.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe Evaluative prototype was favoured for complex diagnostic cases involving uncertainty, ambiguous or atypical findings, and learning contexts.
“Instead, the Evaluative prototype was favoured for complex di-agnostic cases involving uncertainty, ambiguous or atypical findings, and learning contexts.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMedium-to-large effects were found for Anxiety (r = .67), Result Demonstrability (r = .63), Performance Expectancy (r = .62), and Attitude toward Use (r = .55).
“Medium-to-large effects were also found for Anxiety (r = .67),Result Demonstrability (r = .63),Performance Expectancy (r = .62),and Attitude toward Use (r = .55).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe Oracular prototype was preferred for simple, binary decisions such as fracture detection, routine straightforward cases, time-critical ER triage, and high-volume repetitive tasks.
“The Oracular prototype was preferred for simple, binary decisions such as fracture detection, routine straightforward cases, time-critical ER triage, and high-volume repetitive tasks.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataParticipants proposed the inclusion of an AI triage lane to the worklist.
“To further support their workflow, participants proposed the inclusion of an AI triage lane to the worklist.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataParticipants asked for tools that fit into their existing environment, expose their reasoning, preserve agency, and support both fast confirmation and deeper learning.
“Participants asked for tools that fit into their existing environment, expose their reasoning, preserve agency, and support both fast confirmation and deeper learning.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe Evaluative prototype demonstrated superior overall alignment with radiological practice, supported by consistent positive quantitative contrasts across Performance Expectancy, Result Demonstrability, and Facilitating Conditions.
“The Evaluative prototype demonstrated superior overall alignment with radiological practice, supported by consistent positive quantitative contrasts across Performance Expectancy (r = .62),Result Demonstrability (r = .63),and Facilitating Conditions (r = .85).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataEvaluative, differential-first interfaces activated reflection, preserved professional agency, and aligned with the probabilistic logic of radiological diagnosis.
“Evaluative, differential-first interfaces activated reflection, preserved professional agency, and aligned with the probabilistic logic of radiological diagnosis, while oracular outputs were valued for speed in routine cases but resisted when they foreclosed deliberation under uncertainty.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataQuantitative measures revealed a conditional but favourable disposition toward the Evaluative prototype, with effect sizes consistently favouring the Evaluative paradigm.
“Quantitative measures revealed a conditional but favourable disposition toward the Evaluative prototype.9 Effect sizes consistently favoured the Evaluative paradigm.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe four productive friction dimensions can be read as designable operationalisations of what meaningful oversight demands in practice.
“The four productive friction dimensions we propose (temporal delay, option cardinality, evidence depth, and commitment structure) can be read as designable operationalisations of what meaningful oversight demands in practice, translating a normative aspiration into concrete interface proper-ties that determine whether a clinician's engagement with an AI recommendation is substantive or ceremonial.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataEthical approval was granted and informed consent obtained from all participants.
“Ethical approval was granted by the university's ethics committee, and informed consent, including permission for audio and video recording, was obtained from all participants before the study.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe evaluative prototype fit the multi-step reasoning sequence radiologists already follow, whereas the oracular prototype's single output was experienced as a shortcut that bypassed established verification steps.
“The large Facilitating Conditions effect (r = .85)is consistent with this distribution: the evaluative prototype fit the multi-step reasoning sequence radiologists already follow, whereas the oracular prototype's single output was experienced as a shortcut that bypassed, rather than supported, established verification steps.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataCases were assigned to the two prototypes using a fixed, balanced design, in which each prototype was tested on the same 10 cases across all participants, with both sets matched for baseline complexity.
“Cases were assigned to the two prototypes using a fixed, balanced design, in which each prototype was tested on the same 10 cases across all participants, with both sets matched for baseline complexity.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenParticipants gave their consent to participate voluntarily in the case study that was approved by the SUPSI ethical committee.
“Participants gave their consent to participate voluntarily in the case study that was approved by the SUPSI ethical committee.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningProductive friction is an ethical intervention that operates through usability.
“Productive friction, on this account, is thus an ethical intervention that operates through usability.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningOnly the explanation and interaction structure of the cases differed between prototypes.
“Only the explanation and interaction structure of the cases differed.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningThis fixed assignment may introduce a confound between case content and interaction type; although matching cases by complexity reduces this risk, it does not fully remove it.
“This fixed assignment may introduce a confound between case content and interaction type; although matching cases by complexity reduces this risk, it does not fully remove it.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningA differential-first interface presenting a small set of likely diagnoses with discriminative cues better mirrors how clinicians reason and report, while encouraging breadth before commitment.
“A differential-first interface presenting a small set of likely diagnoses with discriminative cues better mirrors how clinicians reason and report, while encouraging breadth before commitment.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataParticipants' rejection of opaque outputs, together with the observation that no participant asked for all evidence simultaneously, supports progressive disclosure.
“Participants' rejection of opaque outputs (P4, P8, P10), together with the observation that no participant asked for all evidence simultaneously, supports progressive disclosure, with a concise default view showing the hypothesis and its confidence, that can be expanded on demand to salient image regions, discriminative cues, and case-linked exemplars.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationnone givenFrictionless compliance optimises throughput at the cost of reflective engagement, while deliberative friction preserves judgement at the cost of speed.
“where frictionless compliance optimises throughput at the cost of reflective engagement, while deliberative friction preserves judgement at the cost of speed.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenA balanced dataset of 23 TC slices representing varying diagnostic complexity was curated.
“A balanced dataset of 23 TC slices representing varying diagnostic complexity was curated.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenThe design-probe approach captures preferences and reasoning patterns rather than behavioural outcomes, and ecological validity is necessarily limited for this proof-of-concept.
“The design-probe approach captures preferences and reasoning patterns rather than behavioural outcomes, and ecological validity is necessarily limited for this proof-of-concept.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported preferences and think-aloud accounts reflect their actual clinical reasoning and would generalise to real clinical settings.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe order in which participants used the prototypes was counterbalanced.
“The order in which participants used the prototypes was counterbalanced.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationProductive friction reveals epistemic limits of what the AI and the clinician can know about a case, unlike seamful design.
“And unlike seamful design (Chalmers 2003;Inman and Ribes 2019), which reveals infrastructural boundaries, productive friction reveals epistemic ones, the limits of what the AI (and the clinician) can know about a given case.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Chalmers 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Inman and Ribes 2019 source
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe question of who controls friction intensity is ethically charged.
“Second, the question of who controls friction intensity is itself ethically charged.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataThe study used a mixed-methods, within-subject proof-of-concept design with practising radiologists.
“To address our research questions, we conducted a mixedmethods, within-subject proof-of-concept study with practising radiologists.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningIn high-stakes clinical AI, the ease of accepting a recommendation determines whether the human decision-maker has exercised the judgement on which moral and legal accountability depends.
“In high-stakes clinical AI, the ease of accepting a recommendation determines whether the human decision-maker has exercised the judgement on which moral and legal accountability depends.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Premisenone givenNeither the philosophical account nor the regulation specifies how meaningfulness should be evidenced at the interface level.
“However, neither the philosophical account nor the regulation specifies how meaningfulness should be evidenced at the interface level.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationA single 'answer' risks recasting the AI as the primary source of truth, diminishing the radiologist's visible contribution and eroding agency, skill display, and professional recognition.
“A single 'answer' risks recasting the AI as the primary source of truth, diminishing the radiologist's visible contribution and eroding agency, skill display, and professional recognition (Natali et al. 2025a).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Natali et al. 2025a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationown dataThe combination of a large Facilitating Conditions effect and a more modest Behavioural Intention effect reflects that the evaluative paradigm was seen as a strong structural fit for clinical reasoning, but adoption remained contingent on conditions neither prototype fully satisfied.
“The combination of a large Facilitating Conditions effect (r = .85)with a more modest Behavioural Intention effect (r = .42)thus reflects a coherent pattern: the evaluative paradigm was seen as a strong structural fit for clinical reasoning, but adoption remained contingent on conditions that neither prototype yet fully satisfied.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenTwo researchers applied categories independently, comparing interpretations and resolving discrepancies.
“Two researchers applied these categories independently, comparing interpretations and resolving discrepancies.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe four dimensions of productive friction capture both the design variables the two prototypes manipulate and the themes participants spontaneously raised.
“These dimensions capture both the design variables our two prototypes manipulate and the themes participants spontaneously raised.”
Quote in the paperSubstantial claim- Rests on an assumption: The two simulated prototypes isolate interaction style rather than algorithmic performance.
Classified as probably not a substantial claim.
Study design and conductreasoningGiven the small sample size (N=11), quantitative data were used to corroborate qualitative findings rather than support independent inferential claims.
“Given the small sample size (N = 11), the quantitative data were used to corroborate the findings emerging from the qualitative analysis rather than to support independent inferential claims.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningIn both modes, adaptive commitment ensures that finalising a diagnosis feels like a deliberate choice.
“In both modes, adaptive commitment ensures that finalising a diagnosis feels like a deliberate choice.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Interpretationown dataThe statements from participants constitute phenomenological evidence of the conditions under which clinicians experience themselves as responsible agents versus ratifiers of machine output.
“Read through an ethical lens, these statements constitute phenomenological evidence of the conditions under which clinicians experience themselves as responsible agents versus ratifiers of machine output.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe four dimensions can each be tuned independently and must be tuned, since their optimal settings vary with case complexity, time pressure, and clinician expertise.
“Read this way, the prototype contrast is evidence that the four dimensions can each be tuned independently and must be tuned, since their optimal settings vary with case complexity, time pressure, and clinician expertise.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationcitationProductive friction is graded and context-sensitive, varying with case complexity and user expertise, unlike cognitive forcing functions.
“Unlike cognitive forcing functions (Buc ¸inca, Malaya, and Gajos 2021), which impose a uniform deliberation barrier before any AI suggestion, productive friction is graded and context-sensitive, varying with case complexity and user expertise.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Buc ¸inca, Malaya, and Gajos 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
PremisecitationGrote and Berens (2020) argued that optimising for compliance efficiency in high-stakes automated systems may systematically undermine the human judgement that regulation assumes is present.
“Grote and Berens ( 2020) have argued that optimising for compliance efficiency in high-stakes automated systems may systematically undermine the human judgement that regulation assumes is present.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Grote and Berens ( 2020) source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenIf agreeing with an AI requires less effort than disagreeing, the decision may be formally human but substantively automated.
“If agreeing with the AI requires less effort than disagreeing, the decision may be formally human but substantively automated.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductcitationPhase 0 was conducted remotely and independently two weeks prior to the main study, helping to mitigate anchoring or recall effects.
“This phase was conducted remotely and independently by participants two weeks prior to the main study, helping to mitigate anchoring or recall effects (Cockburn, Quinn, and Gutwin 2017).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Cockburn, Quinn, and Gutwin 2017 source
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningEfficiency and agency constitute a design tension to be managed through adaptive, context-sensitive support that calibrates deliberation cost to diagnostic uncertainty.
“Efficiency and agency thus constitute a design tension to be managed through adaptive, context-sensitive support that calibrates deliberation cost to diagnostic uncertainty.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Interpretationown dataThe reflective engagement participants described when forming an impression before AI output translates into a 'think-first' gate that withholds AI output until the clinician has recorded an initial impression.
“The reflective engagement participants described when forming an impression before AI output (P3, P8) translates into a 'think-first' gate that withholds AI output until the clinician has recorded an initial impression.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenSystem-set thresholds introduce meta-level automation risk, while clinician-controlled settings may incentivise self-serving minimisation under time pressure.
“System-set thresholds introduce meta-level automation risk, while clinician-controlled settings may incentivise self-serving minimisation under time pressure.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe first form of friction obstructs reasoning, whereas the second activates it.
“The first form of friction obstructs reasoning, whereas the second activates it.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe appropriate paradigm is contingent on case complexity and clinician experience, motivating decision support that can adaptively offer either mode.
“Overall, our findings indicate that the appropriate paradigm is contingent on case complexity and clinician experience, motivating decision support that can adaptively offer either mode.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationProductive friction's primary goal is in-the-moment decision quality rather than long-term retention, though the data suggest pedagogical benefits as a secondary effect.
“Unlike desirable difficulty (Bjork and Bjork 2011), its primary goal is in-the-moment decision quality rather than long-term retention, though our data suggest pedagogical benefits as a secondary effect.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bjork and Bjork 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim. May combine two claims.
Premisenone givenRadiologists produce diagnoses while also enacting a role that carries authority and accountability for patient care.
“Radiologists produce diagnoses while also enacting a role that carries authority and accountability for patient care.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenEach participant engaged with both systems in randomised order during think-aloud sessions.
“Each participant engaged with both systems in randomised order during think-aloud sessions, followed by an adapted UTAUT survey and a semistructured interview.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe source of residual unease differed: in the oracular condition it stemmed from prescriptive authority without explanation; in the evaluative condition it stemmed from the cognitive cost of comparing multiple options.
“In the oracular condition it stemmed from prescriptive authority without explanation; in the evaluative condition it stemmed from the cognitive cost of comparing multiple options, a qualitatively different and arguably more productive form of friction, whose implications we return to under RQ3.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThere is a counter-tradition in HCI that treats friction as a resource rather than a defect.
“These observations connect to a counter-tradition in HCI that treats friction as a resource rather than a defect, from reflective design (Sengers et al. 2005) to slow technology (Hallnäs and Redström 2001) and seamful design (Chalmers 2003;Inman and Ribes 2019;Ehsan et al. 2024).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Sengers et al. 2005 source
- cites Hallnäs and Redström 2001
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Chalmers 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
Study design and conductcitationQualitative analysis used hybrid inductive-deductive thematic analysis following Braun and Clarke's guidelines.
“We analysed the interview and think-aloud transcripts using a hybrid inductive-deductive thematic analysis, following Braun and Clarke's guidelines (Braun and Clarke 2021).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Braun and Clarke 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Study design and conductnone givenFixed case-to-prototype assignment introduces a potential confound between case content and interaction paradigm; baseline complexity matching mitigates but does not eliminate this threat.
“Finally, fixed case-to-prototype assignment introduces a potential confound between case content and in-teraction paradigm; baseline complexity matching mitigates but does not eliminate this threat.”
Quote in the paperNot a substantial claim- Rests on an assumption: The case-complexity matching via baseline classification adequately balances cases across prototypes.
The paper relies on this without support.
The two simulated prototypes isolate interaction style rather than algorithmic performance.
“Two web-based interfaces, mimicking decision-support systems, were developed to isolate interaction style rather than algorithmic performance.”
The paper relies on this without support.
The 20 MRI cases and 10 tasks per prototype are sufficient to elicit stable preferences and reasoning patterns.
“In the study, participants completed 10 diagnostic tasks with each prototype, drawn from a balanced pool of 20 MRI cases curated by an experienced radiologist on the research team.”
The paper relies on this without support.
Participants' self-reported preferences and think-aloud accounts reflect their actual clinical reasoning and would generalise to real clinical settings.
“The design-probe approach captures preferences and reasoning patterns rather than behavioural outcomes, and ecological validity is necessarily limited for this proof-of-concept.”
The paper relies on this without support.
The case-complexity matching via baseline classification adequately balances cases across prototypes.
“This enabled case-complexity matching across prototypes.”
The paper relies on this without support.
The design-probe study with mock-ups and a small set of cases can reveal design requirements for real-world clinical AI.
“Through a design-probe study with practising radiologists, we contrasted oracular and evaluative paradigms of AI decision support”
The paper relies on this without support.
Radiologists' preferences and experiences in the study reflect those of radiologists generally.
“Our findings suggest that clinical AI should be designed around radiologists' work practices rather than AI capabilities.”
The paper relies on this without support.
The oracular and evaluative prototypes adequately represent the two paradigms of AI decision support.
“we contrasted oracular and evaluative paradigms of AI decision support”
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.
Study cases are described as both MRI and TC slices
MajorThe methodology describes the study pool as MRI cases, while the prototype materials describe the cases as TC slices. The text also says the same cases were used in the baseline and prototype phases, so these are conflicting descriptions of the study material.
“drawn from a balanced pool of 20 MRI cases curated by an experienced radiologist on the research team.”
“Both prototypes used the same visual layout, patient metadata, and typology of TC slices to ensure parity of visual input.”
Curated case-pool size differs between sections
MinorOne section says the curated pool contained 20 cases; the materials description says the curated dataset contained 23 slices. Although 20 instances were used per participant, that does not reconcile the two stated sizes of the curated pool.
“drawn from a balanced pool of 20 MRI cases curated by an experienced radiologist on the research team.”
“A balanced dataset of 23 TC slices representing varying diagnostic complexity was curated.”
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 9
- Bjork, E. L.; and Bjork, R. A. 2011. Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. Psychology and the real world: Essays illustrating fundamental contributions to society, 2(59-68): 56-64.
- Buc ¸inca, Z.; Malaya, M. B.; and Gajos, K. Z. 2021. To trust or to think: cognitive forcing functions can reduce overre- liance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1): 1-21.
- Chalmers, M. 2003. Seamful design and ubicomp infras- tructure. In Proceedings of Ubicomp 2003 workshop at the crossroads: The interaction of HCI and systems issues in Ubicomp, volume 4, 577-584.
- Dekker, I.; and Hermans, F. 2026. Why the User- Friendliness of AI Undermines Education. Commentary on Wieczorek 2025. Philosophy & Technology, 39(2): 82.
- Fregosi, C.; Natali, C.; and Cabitza, F. 2025. Judicial Pro- tocols in Diagnostic AI: Contrastive Explanations to Pre- serve Human Agency. In Proceedings of the Workshops at the Fourth International Conference on Hybrid Human- Artificial Intelligence co-located with the Fourth Interna- tional Conference on Hybrid Human-Artificial Intelligence (HHAI2025). CEUR-WS.
- Hallnäs, L.; and Redström, J. 2001. Slow technology- designing for reflection. Personal and ubiquitous comput- ing, 5(3): 201-212.
- Hemmer, P.; Schemmer, M.; Vössing, M.; and Kühl, N. 2021. Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review. PACIS, 78.
- Le, T.; Miller, T.; Sonenberg, L.; and Singh, R. 2024. To- wards the new XAI: A hypothesis-driven approach to de- cision support using evidence. In ECAI 2024: 27th Eu- ropean Conference on Artificial Intelligence, 19-24 Octo- ber 2024, Santiago de Compostela, Spain-Including 13th Conference on Prestigious Applications of Intelligent Sys- tems (PAIS 2024), 850-857. SAGE Publications Pvt. Ltd 1 Oliver's Yard, 55 City Road, London, EC1Y 1SP.
- Schön, D. A. 2017. The reflective practitioner: How profes- sionals think in action. Routledge.
- Authors
- Rubegni, E., Natali, C., Valsecchi, C., Rizzo, S., Ayoub, O., & Facchini, A.
- Affiliations
- Lancaster University (GB)
- Vrije Universiteit Amsterdam (NL)
- Ente Ospedaliero Cantonale (CH)
- University of Applied Sciences and Arts of Southern Switzerland (CH)
- Kozminski University (PL)
- Venue
- Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
- Published
- 2026-10-11
- Volume
- 9
- Issue
- 3
- Pages
- 2206-2218
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W7196976256
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- clinical AI, radiology, interaction costs, diagnostic reasoning, professional agency, AI ethics, human-AI interaction, time delay
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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