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REVIEW 5 major objections 5 minor 86 references

Explainability and AI Confidence in Clinical Decision Support Systems: Effects on Trust, Diagnostic Performance, and Cognitive Load in Breast Cancer Care

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Low AI confidence makes clinicians pause; high confidence breeds overreliance.

desk verdict The high-confidence overreliance finding is likely an artifact of the outcome coding, but the low-confidence effects and the study design make this worth a serious referee. read the letter →

arxiv 2501.16693 v1 pith:3HAPWIAX submitted 2025-01-28 cs.HC

Olya Rezaeian , Alparslan Emrah Bayrak , Onur Asan This is my paper · ORCID
classification cs.HC
keywords clinicaldecisionsupportsystemsexplainableAItrustcalibrationconfidencescorescognitiveloadbreastcancerdiagnosisautomationbiashuman-AIinteraction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that the way a clinical AI displays its confidence changes how clinicians behave, not just what they say. In an online experiment with 28 healthcare professionals diagnosing breast ultrasound images, low AI confidence significantly lowered trust and agreement and lengthened diagnosis time, while high confidence slightly but significantly reduced diagnostic accuracy. The authors argue this is overreliance: clinicians followed high-confidence suggestions from a system that was only about 80 percent accurate. They also report that one explainability feature, tumor localization with probability estimates, raised stress. The study matters because confidence displays are a design choice that can either support appropriate reliance or nudge clinicians toward automation bias.

What carries the argument

The experimental apparatus is a web-based CDSS built on a U-Net segmentation model and a CNN classifier trained on a public breast ultrasound dataset with 780 images and 81 percent accuracy. The mechanism carrying the argument is the staged explainability design: baseline (no AI), classification only, probability distribution, tumor localization, and enhanced localization with high and low confidence regions. The outcome measures are trust and agreement ratings per image, agreement-above-neutral treated as adopting the AI's label, and two NASA-TLX items (mental demand and stress) after each stage. Mixed-effects models estimate the effect of confidence level while controlling for participant-level variation.

What would settle it

Re-run the study with a design where participants must always enter their final diagnosis explicitly, without the agreement-rating shortcut. If the high-confidence performance decrement disappears or reverses when the final diagnosis is recorded directly, the paper's overreliance claim is falsified by its own outcome construction.

Watch

Extended reading notes

Core claim

The central claim is that AI confidence scores act as a trust-calibration signal with asymmetric effects: low confidence makes clinicians more cautious and less accepting of AI advice, whereas high confidence increases trust enough to produce measurable overreliance and a small drop in diagnostic performance. Using an interrupted time series design, the authors compared a no-support baseline with four explainability conditions and modeled trust, agreement, performance, and diagnosis duration as functions of confidence level. The effect sizes are modest — low confidence reduced trust by 0.16 points and agreement by 0.19 points on a 5-point scale and increased diagnosis duration, while high confidence reduced performance by a small but statistically significant margin, which the authors attribute to automation bias rather than to the AI's advice being wrong more often.

Load-bearing premise

The result that high confidence hurts performance rests on treating an agreement rating above neutral as the participant adopting the AI's diagnosis; if that mapping misrepresents what clinicians actually decided, the overreliance finding could be an artifact of how performance was coded rather than a real behavioral change.

Editorial extensions

If this is right

  • Displaying low confidence will make clinicians more skeptical and slower, which may be desirable for error-prone AI but costly in time-critical settings.
  • Displaying high confidence on a system with imperfect accuracy will nudge a small but real fraction of decisions toward incorrect AI labels, so high-confidence displays should be paired with safeguards.
  • Tumor localization with probability estimates raises stress without improving mental demand, so explainability features are not neutral additions to the interface.
  • Age, experience, job role, gender, and race are associated with different perceptions of AI usefulness and complexity, so a single explanation design is unlikely to fit all clinician groups.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the asymmetric confidence effect suggests a practical design rule — degrade or flag low-confidence outputs rather than suppress them, since low confidence already triggers useful caution.
  • Editorial inference: the performance measurement issue could be resolved by a forced-choice final diagnosis per image; such a design would also let the authors separate agreement from adoption.
  • Editorial inference: testing the same confidence thresholds with a more accurate, calibrated model would show whether overreliance scales with model accuracy or is a fixed human tendency.
  • Editorial inference: the stress finding for localization suggests that visual complexity — not just confidence — drives cognitive load, and could be tested by isolating localization with and without probability numbers.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. This paper reports a web-based interrupted time-series experiment with 28 U.S. healthcare professionals who diagnosed breast ultrasound images under an AI-based CDSS with increasing levels of explainability. The authors model how AI confidence scores (none, low, high) affect trust, agreement, diagnostic performance, and diagnosis duration, and they analyze how explainability affects mental demand and stress. They report that low confidence significantly decreased trust and agreement and increased diagnosis duration, while high confidence was associated with a small but significant decrease in performance, which they interpret as overreliance. They also report that the localization intervention increased stress and that demographic factors were associated with perceptions of AI.

Significance. The study addresses a practically important question in clinical decision support, and the low-confidence results—decreased trust and agreement and increased diagnosis duration—are internally consistent, within-participant findings that could inform designs for calibrated reliance. The experiment uses a clinically relevant task and a realistic AI model trained on breast ultrasound data, which strengthens ecological validity relative to abstract prediction tasks. However, the headline claim that high confidence caused overreliance and reduced diagnostic accuracy rests on a performance measure that imputes the AI label on any trial with above-neutral agreement, so the effect is not a clean behavioral outcome. The abstract also overstates the high-confidence trust result, and the discussion contradicts the mental-demand table. These issues are load-bearing for the paper's main conclusions.

major comments (5)
  1. [Section 3.4, Performance] The outcome labeled 'diagnostic performance' is not an observed final diagnosis on trials where agreement is above neutral; the participant's decision is 'considered aligned with the AI's suggestion' and is therefore scored as the AI label. Table 2's high-confidence performance coefficient (β = −0.015, p = 0.020) is consequently not a direct measure of decision accuracy: high confidence and agreement are modeled separately but are not independent, and the same agreement ratings are used to construct the performance outcome. Because no independent human diagnosis was elicited on those trials, the interpretation in Section 4.2 and the abstract that high confidence 'led to overreliance, reducing diagnostic accuracy' is not supported by the data as reported.
  2. [Abstract and Section 6] The abstract states that 'high confidence scores substantially increased trust' and Section 6 repeats that high confidence 'substantially improved trust,' but Table 2 reports β = 0.103, p = 0.169 for high-confidence trust, which Section 4.2 correctly describes as not statistically significant. The abstract and conclusion should be corrected to match the reported result. The conclusion's statement that 'AI confidence score also elevated stress levels' is also unsupported, since stress is modeled by intervention in Table 1 and never by confidence score.
  3. [Section 5.1 versus Table 1] The Discussion states that the fourth condition 'resulted in increased mental demand,' but Table 1 reports β = −0.107, p = 0.501 for the 4th Intervention, a nonsignificant decrease, and Section 4.1 describes a slight decrease. This is an internal contradiction that affects the interpretation of RQ1; the text should be aligned with the table.
  4. [Sections 3.3 and 4.2] The experiment uses a fixed intervention order for all participants, and the mixed-effects models in Table 2 include no time or order term. The low- and high-confidence effects are therefore confounded with practice, fatigue, and cumulative interface exposure, so the causal language in Section 5.2 is stronger than the design supports. Please add a sensitivity analysis with session or trial order as a covariate, or explicitly temper the causal claims.
  5. [Section 4.3, Table 3] The demographic ANOVAs appear to treat repeated post-intervention observations as independent units; for example, gender on complexity perception is reported as F = 92.97, p = 6.61e-16, which is implausible with 28 participants unless each observation is counted as an independent case. Please specify the unit of analysis and the number of observations per cell, and use participant-level or mixed-effects models before drawing the demographic conclusions in RQ3.
minor comments (5)
  1. [Section 3.4] The agreement and trust scales are described as 5-point Likert scales 'from 0 to 5,' which is six response options; please clarify the actual scale and the neutral point.
  2. [Sections 3.3 and 3.4] Section 3.3 says participants give their own diagnosis when agreement is 'below 3,' while Section 3.4 refers to agreement 'above neutral'; specify how the neutral response (e.g., agreement = 3) is handled.
  3. [Tables 1 and 2] The intervention numbering is unclear: Section 3.3 lists Baseline plus Interventions I–IV, but Tables 1–2 use '1st/2nd/3rd/4th Intervention' without a consistent mapping; label all conditions the same way.
  4. [Section 3.4, AI Confidence Score] The 90% threshold used to dichotomize low versus high AI confidence is not justified; report a sensitivity analysis or motivate the cutoff.
  5. [General] There are multiple typographical errors, including 'mental memand' in Section 4.3 and 'V ariable' in the Table 1 and Table 2 captions; please proofread the manuscript.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study is empirical, the central behavioral effects are new measurements, and the only construct-level concern (performance coding from agreement) is a measurement-validity issue, not a circular reduction.

full rationale

This paper is a web-based user experiment with no derivation chain, no fitted model whose parameters are relabeled as predictions, and no uniqueness theorem or ansatz imported from prior work. The self-citations ([68] for the CDSS architecture, and [10,25,53] for background and prior related findings) are not load-bearing: the AI system is described concretely with its public training dataset and an 81% accuracy figure, and the trust, agreement, cognitive-load, and diagnosis-duration outcomes are newly collected behavioral data. The one construct that warrants scrutiny is the Section 3.4 performance definition, under which agreement above neutral is coded as adoption of the AI label, while only below-neutral trials elicit an independent clinician diagnosis. This makes the performance variable a composite of agreement and AI correctness rather than a pure measure of independent diagnostic skill, which is a genuine measurement-validity limitation and is not acknowledged in Section 5.4. However, it is not circular: the high-confidence performance coefficient (beta = -0.015, p = 0.020) is not equal by construction to the confidence-agreement effect (beta = 0.108, p = 0.121), and the direction of the performance effect depends on empirical AI and human accuracy rates, so the finding does not reduce to its own inputs. No step in the paper satisfies the defined circularity patterns, so the appropriate finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central statistical claims rest on a hand-picked 90% confidence threshold, an unstated agreement cutoff, and assumptions about data independence and participant representativeness that are questionable given the small sample, fixed intervention order, and negative variance component in the mixed model.

free parameters (2)
  • confidence threshold = 90%
    AI predictions are categorized as high confidence when the highest probability is 90% or above and low otherwise; this cutoff is chosen by hand and not justified, and it directly defines the independent variable in the confidence analyses.
  • agreement cutoff = above neutral, exact Likert value unspecified
    Performance coding treats agreement ratings above neutral as adoption of the AI suggestion; the threshold for 'above neutral' is not stated, yet it determines the performance outcome for each image.
assumptions (4)
  • domain assumption Ground-truth labels in the public breast ultrasound dataset are correct.
    Used to compute performance accuracy; no independent verification of the labels is provided.
  • domain assumption The fixed order of interventions (baseline, classification, probability, localization, confidence localization) does not confound intervention effects.
    The interrupted time series design assumes stable baseline trends, but no time-trend analysis or randomization is performed; practice and fatigue are not modeled.
  • standard math Mixed-effects and ANOVA model assumptions hold, including normal errors, independent observations, and positive variance components.
    The reported negative group variance (-0.881) in Table 1 suggests this assumption is violated.
  • domain assumption The 28 participants are representative of the clinical population of interest.
    Convenience recruitment via social platforms and professional networks, with no power analysis; demographic subgroups are extremely small.

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Cite this review

Pith. "Pith review of Explainability and AI Confidence in Clinical Decision Support Systems: Effects on Trust, Diagnostic Performance, and Cognitive Load in Breast Cancer Care." pith.science (2026). https://pith.science/paper/3HAPWIAX

@misc{pith2026250116693,
  author       = {Pith},
  title        = {Pith review of: Explainability and AI Confidence in Clinical Decision Support Systems: Effects on Trust, Diagnostic Performance, and Cognitive Load in Breast Cancer Care},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3HAPWIAX}},
  note         = {Machine review of arXiv:2501.16693}
}
read the original abstract

Artificial Intelligence (AI) has demonstrated potential in healthcare, particularly in enhancing diagnostic accuracy and decision-making through Clinical Decision Support Systems (CDSSs). However, the successful implementation of these systems relies on user trust and reliance, which can be influenced by explainable AI. This study explores the impact of varying explainability levels on clinicians trust, cognitive load, and diagnostic performance in breast cancer detection. Utilizing an interrupted time series design, we conducted a web-based experiment involving 28 healthcare professionals. The results revealed that high confidence scores substantially increased trust but also led to overreliance, reducing diagnostic accuracy. In contrast, low confidence scores decreased trust and agreement while increasing diagnosis duration, reflecting more cautious behavior. Some explainability features influenced cognitive load by increasing stress levels. Additionally, demographic factors such as age, gender, and professional role shaped participants' perceptions and interactions with the system. This study provides valuable insights into how explainability impact clinicians' behavior and decision-making. The findings highlight the importance of designing AI-driven CDSSs that balance transparency, usability, and cognitive demands to foster trust and improve integration into clinical workflows.

Figures

Figures reproduced from arXiv: 2501.16693 by the authors.

Figure 1
Figure 1. Experiment web interface with an interactive assessment pop-up window, where participants’ opinions [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Experiment Procedure [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Distribution of AI-related measures by age. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Distribution of AI-related measures by Gen [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.