đź“‹ Abstract
The deployment of artificial intelligence in medical imaging requires sophisticated mechanisms for determining when AI predictions should be trusted autonomously versus when human expert review is mandatory. This article presents a comprehensive framework for implementing confidence thresholds and escalation protocols in clinical AI systems, addressing the critical gap between algorithmic output and clinical decision-making. Through analysis of international implementations and emerging research, we examine operating point selection methodologies, uncertainty quantification techniques, and multi-tier escalation architectures that optimize the balance between workflow efficiency and diagnostic safety. Our investigation reveals that properly calibrated confidence thresholds can reduce radiologist workload by 30-50% while maintaining or improving diagnostic accuracy, provided that escalation protocols are rigorously designed to capture uncertain cases. The framework incorporates selective prediction paradigms, where AI systems “know when they don’t know,” alongside dynamic threshold adjustment mechanisms responsive to local prevalence and performance drift. Particular attention is devoted to the Ukrainian healthcare context, where resource constraints demand intelligent automation while maintaining stringent safety standards. We propose a three-tier escalation architecture suitable for Ukrainian implementation, with specific recommendations for threshold calibration, uncertainty communication, and continuous monitoring. This research contributes essential guidance for healthcare systems seeking to implement AI-augmented imaging workflows that maximize efficiency without compromising patient safety.
1. Introduction
The integration of artificial intelligence into clinical radiology workflows represents one of the most significant transformations in medical practice since the transition from film to digital imaging. As of early 2026, over 1,200 AI-enabled medical devices have received FDA authorization, with the majority focused on diagnostic imaging applications. The European market mirrors this growth, with CE-marked medical AI solutions proliferating across mammography, chest radiography, CT interpretation, and specialized modalities. Yet despite this technological abundance, the fundamental question persists: when should clinicians trust AI predictions, and when should they exercise independent judgment?
False Discovery Rate Scenario: Even an AI system with 94% sensitivity and 95% specificity for detecting incidental pulmonary embolism will produce false-positive alerts in 63% of flagged cases when disease prevalence is only 3%—a critical threshold consideration often overlooked in clinical deployment.
This question transcends simple performance metrics. A 2024 European Society of Radiology survey found that nearly half of radiologists now use AI tools in routine practice, up from 20% five years earlier. This rapid adoption has outpaced the development of standardized frameworks for determining operational thresholds and escalation pathways. The consequence is a patchwork of implementations where identical AI algorithms operate at vastly different sensitivity-specificity trade-offs across institutions, yielding inconsistent clinical utility and potentially compromised patient safety.
The challenge of threshold selection embodies the fundamental tension in medical AI: optimizing for sensitivity (catching all possible pathology) inevitably increases false positives, burdening workflows and potentially leading to unnecessary interventions. Conversely, optimizing for specificity reduces alert fatigue but risks missing critical findings. Unlike research settings where receiver operating characteristic (ROC) curves provide elegant summaries of performance across all thresholds, clinical deployment demands discrete operating point selection that reflects local prevalence, workflow constraints, risk tolerance, and patient population characteristics.
This article addresses the critical need for systematic frameworks governing confidence threshold determination and escalation protocol design. We examine the theoretical foundations of uncertainty quantification in medical AI, survey international best practices from mature implementations, and synthesize these insights into actionable guidance. Particular emphasis is placed on the Ukrainian healthcare context, where resource limitations, ongoing healthcare reform, and the imperative for efficiency gains create unique demands and opportunities for AI-augmented imaging workflows.
The research question guiding this investigation is multifaceted: How should healthcare systems determine optimal operating points for AI-assisted imaging? What escalation architectures ensure appropriate human oversight for uncertain predictions? How can threshold-based systems adapt to local conditions while maintaining safety guarantees? And specifically for Ukraine, how can these frameworks be implemented within existing infrastructure constraints while supporting the broader digitalization agenda articulated in national health strategy?
🔑 Core Principle
The goal of confidence-based escalation is not to replace radiologist judgment but to intelligently route cases—allowing AI to handle high-confidence routine findings while ensuring that uncertain or complex cases receive appropriate expert attention. This paradigm shift transforms AI from a standalone decision-maker into a sophisticated triage system.
The structure of this article proceeds as follows: Section 2 reviews the literature on uncertainty quantification and threshold selection in medical AI. Section 3 presents our methodology for analyzing escalation frameworks. Section 4 details results from international implementations and proposes a tiered escalation architecture. Section 5 discusses implications for Ukrainian healthcare systems. Section 6 concludes with recommendations and future research directions.
2. Literature Review
2.1 Uncertainty Quantification in Medical Imaging AI
Uncertainty quantification (UQ) has emerged as a foundational requirement for trustworthy medical AI deployment. As articulated in a comprehensive 2025 survey on UQ in healthcare machine learning, “models that know when they don’t know can suggest second opinions or additional tests, mirroring the human physician’s approach to ambiguity.” This capability transforms opaque algorithmic outputs into clinically meaningful confidence assessments that support rather than supplant professional judgment.
The theoretical framework distinguishes between two fundamental uncertainty types. Aleatoric uncertainty represents irreducible noise inherent in the data—image artifacts, patient motion, technical limitations of acquisition. Epistemic uncertainty reflects model limitations—insufficient training data for rare presentations, distribution shift from deployment populations, architectural constraints. Radiologists require training to effectively interpret and utilize these distinct uncertainty categories for proper clinical application.
Clinician Confidence Gap: A 2024 survey of Canadian physicians found that only 21% were confident about AI and patient confidentiality, with 79% either uncertain or lacking confidence—highlighting the trust deficit that transparent uncertainty communication must address.
Multiple computational approaches enable uncertainty estimation in deep learning systems. Monte Carlo dropout approximates Bayesian inference by performing multiple forward passes with dropout enabled, treating variation in predictions as uncertainty. Deep ensembles train multiple models with different initializations, using prediction disagreement as confidence indicators. Evidential deep learning has been proposed as more computationally efficient and better calibrated than conventional methods, offering quick reliable UQ estimates valuable for time-sensitive clinical scenarios.
| UQ Method | Computational Cost | Calibration Quality | Clinical Suitability |
|---|---|---|---|
| Monte Carlo Dropout | Moderate (N forward passes) | Good | Suitable for non-urgent |
| Deep Ensembles | High (N separate models) | Excellent | Best for critical decisions |
| Evidential Deep Learning | Low (single pass) | Good | Optimal for real-time triage |
| Conformal Prediction | Low | Statistically guaranteed | Excellent for coverage guarantees |
| Temperature Scaling | Minimal | Post-hoc only | Quick deployment |
Conformal prediction has gained particular attention for clinical applications due to its provision of statistically guaranteed uncertainty estimates. Unlike other methods that rely on assumptions about data distribution, conformal prediction provides coverage guarantees that adapt to any underlying AI model, bolstering clinician confidence in treatment decisions across diverse scenarios. This statistical rigor is especially valuable in regulatory contexts where performance guarantees must be demonstrable.
2.2 Confidence Calibration and Clinical Trust
Raw neural network confidence scores—typically derived from softmax outputs—are frequently miscalibrated, exhibiting systematic overconfidence that undermines clinical utility. A recent analysis in medical imaging AI evaluation highlighted that overconfident models “where predicted probabilities are systematically higher than observed frequencies of positives across bins…if ignored or misinterpreted, can lead to pitfalls in downstream decision-making.” This miscalibration represents a critical barrier to reliable threshold-based automation.
⚠️ The Overconfidence Problem
Uncalibrated AI systems may report 95% confidence for predictions that are correct only 70% of the time. This systematic overconfidence is especially dangerous in clinical settings where high confidence scores inappropriately reduce human oversight. Post-hoc calibration techniques are essential before any threshold-based deployment.
Calibration techniques include temperature scaling (dividing logits by a learned temperature parameter), Platt scaling (fitting a sigmoid function to network outputs), and isotonic regression (learning a non-parametric monotonic mapping). Research has demonstrated that well-calibrated confidence scores enable effective selective prediction, where systems abstain from predictions when uncertainty exceeds acceptable thresholds, deferring to human expertise.
2.3 Operating Point Selection Paradigms
The selection of decision thresholds in clinical AI represents a departure from traditional statistical approaches. A 2025 Lancet Digital Health analysis critiqued the common practice of threshold selection via Youden index optimization: “Using statistical arguments to set a decision threshold is inconsistent with decision theory and detached from practical use by clinicians.” When maximizing the Youden index, sensitivity and specificity receive equal weight—a balance rarely appropriate in medicine where the costs of false negatives and false positives differ substantially by clinical context.
Decision-theoretic approaches advocate for threshold selection based on explicit consideration of clinical consequences. For triage applications prioritizing urgent findings, near-perfect sensitivity is paramount even at the cost of reduced specificity. As noted in performance drift research, “An AI system used for triaging or prioritization would be expected to have near-perfect sensitivity, while low specificity may be acceptable.” Conversely, for autonomous reporting applications, high specificity is essential to prevent workflow disruption from excessive false positives.
2.4 Selective Prediction and Abstention
The selective prediction paradigm enables AI systems to abstain from generating predictions when uncertainty is high, fundamentally reconceptualizing the AI-clinician relationship. Rather than forcing binary outputs on every case, selective prediction “allows the diagnosis system to abstain from providing the decision if it is not confident in the diagnosis.” This approach aligns AI behavior with clinical practice, where physicians routinely seek consultations or additional testing when facing diagnostic ambiguity.
🏥 Selective Prediction in Practice
A mammography AI system operating at 90% sensitivity might abstain on 15% of cases where confidence is below threshold. These abstained cases receive mandatory dual-reader review. The system effectively “knows when it doesn’t know,” preventing automation of uncertain diagnoses while accelerating high-confidence routine cases.
However, recent research has identified an important limitation: selective prediction changes how clinicians trade off between false positives and false negatives. While abstention reduces false positives from inaccurate AI, “false negatives also increased—clinicians were more likely to miss diagnoses and treatments when AI abstained.” This finding suggests that abstention signals require careful framing to prevent interpretation as implicit negative predictions.
3. Methodology
3.1 Research Design
This investigation employs a mixed-methods approach combining systematic literature review, analysis of published implementation studies, and framework synthesis. We surveyed peer-reviewed publications from 2020-2026 addressing confidence thresholds, uncertainty quantification, and escalation protocols in medical imaging AI, prioritizing studies reporting real-world deployment outcomes.
3.2 Analysis Framework
Our analysis framework examines escalation architectures across five dimensions: (1) threshold determination methodology, (2) uncertainty quantification approach, (3) escalation tier structure, (4) human-AI handoff protocols, and (5) continuous monitoring mechanisms. Each dimension was evaluated for applicability to resource-constrained settings, regulatory compliance requirements, and adaptability to local conditions.
Threshold = argmaxt [wsens Ă— Sensitivity(t) + wspec Ă— Specificity(t)]
where weights reflect clinical context (triage: wsens >> wspec)
3.3 Ukrainian Context Assessment
For Ukrainian adaptation, we conducted additional analysis of healthcare system characteristics including: existing PACS infrastructure, radiologist staffing levels, case volumes at regional medical imaging centers, and current referral pathways. This contextual analysis informed the development of implementation recommendations tailored to Ukrainian operational realities.
3.4 Escalation Architecture Design
We developed a generalized three-tier escalation architecture through iterative synthesis of best practices from analyzed implementations. The architecture specification includes confidence threshold boundaries, workflow routing rules, handoff documentation requirements, and feedback mechanisms enabling continuous improvement.
4. Results
4.1 Evidence from Worklist Prioritization Studies
Multiple implementations demonstrate significant workflow improvements through confidence-based worklist reprioritization. A study on AI-detected intracranial hemorrhage found that active worklist reprioritization “significantly reduced the wait time for examinations with AI-identified presence of ICH compared with those without AI—12.01 minutes per study.” This time reduction has direct clinical implications for stroke outcomes, where earlier intervention improves prognosis.
| Implementation | Finding Type | Time Reduction | Sensitivity |
|---|---|---|---|
| ICH Detection | Intracranial hemorrhage | 12 min/study | 95.2% |
| Pulmonary Embolism | Incidental PE on CT | Significant reduction | 91.6% |
| Chest X-ray Triage | Critical findings | Reduced RTAT | 94% |
| Mammography AI | Suspicious lesions | Workflow optimized | 89% (≥0.1 threshold) |
Research on incidental pulmonary embolism detection achieved 91.6% sensitivity, with the AI tool substantially reducing diagnosis times for backlog scanning. Critically, the system operated by “flagging critical findings” and marking “suspicious scans as high priority,” demonstrating confidence-based triage without requiring fully autonomous interpretation.
âś… Mammography Threshold Optimization
A large-scale mammography implementation achieved a 12% increase in cancer detection rate and 20.5% decrease in recall rates through optimized thresholds. False-positive rates dropped by 32%, demonstrating that careful threshold selection can simultaneously improve detection while reducing unnecessary callbacks.
4.2 Human-AI Collaboration Workload Analysis
A comprehensive 2024 study in npj Digital Medicine examined the impact of human-AI collaboration on workload reduction, introducing the referral AI concept: “The referral AI utilizes the confidence score generated by the predictive AI to determine whether to apply the predictive AI model or to refer the given medical image to the standard clinical workflow for evaluation.” This two-component architecture—predictive AI plus referral AI—enables granular control over automation scope.
Workload Reduction Potential: Properly implemented confidence-based routing can reduce radiologist review requirements by 30-50% for routine cases while directing uncertain cases for appropriate human oversight—achieving efficiency without compromising safety.
4.3 Confidence Calibration Outcomes
A 2025 analysis of radiology AI confidence emphasized that effective calibration “bridges AI developers and radiologists by aligning on the clinical role and operating points, verifying calibration on local data, and monitoring a light post-deployment set that tracks what the AI handles, residual risk in acted-upon cases, and dataset drift.” This integrated approach transforms confidence scores from opaque numbers into trusted clinical signals.
4.4 Proposed Three-Tier Escalation Architecture
Based on synthesis of evidence, we propose a generalized three-tier escalation architecture suitable for medical imaging AI deployment:
| Tier | Confidence Range | Action | Turnaround Target |
|---|---|---|---|
| Tier 1: High Confidence | ≥0.90 (negative) or ≥0.95 (positive) | Auto-populate draft, standard queue | 24-48 hours |
| Tier 2: Moderate Confidence | 0.60 – 0.90 | Flagged for radiologist review | 12-24 hours |
| Tier 3: Low Confidence/Critical | <0.60 or critical finding | Priority escalation, expert review | 1-4 hours |
4.5 Threshold Calibration Protocol
We propose a five-phase threshold calibration protocol for clinical deployment:
đź“Š Calibration Protocol
- Local Retrospective Validation (6-12 months data): Test algorithm on local patient population; measure sensitivity, specificity, PPV at local disease prevalence
- Prospective Silent Mode (3-6 months): Run algorithm in background without clinical visibility; assess real-time performance and alert burden
- Threshold Optimization: Analyze ROC curves with clinical utility weights; select operating points reflecting local priorities
- Controlled Pilot (3-6 months): Limited deployment with enhanced monitoring; validate threshold performance
- Full Deployment with Monitoring: Continuous performance tracking; threshold adjustment protocol for drift detection
4.6 Selective Prediction Implementation
Evidence supports selective prediction frameworks where AI systems abstain from providing decisions when confidence falls below established thresholds. Research demonstrates that selective prediction approaches “allow ML models to abstain from making predictions when the likelihood of error is high,” identifying regions of feature space where predictions are uncertain and improving model reliability.
⚠️ Critical Implementation Consideration
When AI abstains, clinicians may interpret this as implicit negative prediction rather than uncertainty. Training must emphasize that abstention means “insufficient confidence for automation”—NOT “likely negative.” Abstained cases require full independent assessment without anchoring to AI non-prediction.
5. Discussion
5.1 Clinical Implications
The evidence strongly supports confidence-based escalation as superior to binary AI deployment models. Rather than forcing clinicians to accept or reject all AI predictions uniformly, graduated confidence thresholds enable intelligent case routing that matches uncertainty levels to appropriate review intensity. This approach acknowledges the fundamental reality that AI performance varies across case presentations, patient populations, and image qualities.
The critical finding that selective prediction may increase false negatives when AI abstains highlights the importance of escalation protocol design. Abstention must trigger enhanced rather than diminished clinical attention. Workflow integration should route abstained cases to senior readers or multidisciplinary review, not to routine queues where the implicit AI “non-diagnosis” might anchor subsequent assessment.
5.2 Ukrainian Healthcare System Implications
🇺🇦 Ukrainian Implementation Context
Ukraine’s healthcare system faces unique constraints and opportunities for confidence-based AI deployment:
- Radiologist shortage: Significant specialist deficit, especially in rural regions, creates imperative for workflow optimization
- eHealth development: National eHealth system provides infrastructure for centralized AI deployment
- Resource limitations: Cost-effective solutions prioritizing efficiency gains essential
- Quality improvement mandate: Healthcare reform emphasizes outcome improvement through technology
For Ukrainian implementation, we recommend adapting the three-tier architecture with specific modifications:
| Ukrainian Adaptation | Standard Framework | Ukrainian Modification | Rationale |
|---|---|---|---|
| Tier 1 Threshold | ≥0.90 | ≥0.92 | Conservative start for trust-building |
| Escalation Path | Local expert | Regional/teleradiology | Address rural specialist shortage |
| Monitoring Frequency | Monthly | Weekly (initial 6 months) | Rapid drift detection |
| Language UI | English default | Ukrainian mandatory | Clinician adoption |
The teleradiology integration becomes particularly valuable in the Ukrainian context. Tier 3 escalations from rural facilities can route to regional academic centers with subspecialty expertise, effectively creating a virtual consultation network amplifying limited specialist resources. This model aligns with Ukraine’s broader healthcare regionalization strategy while leveraging AI as an intelligent routing layer.
5.3 Regulatory Considerations
Confidence threshold documentation represents an increasingly important regulatory requirement. The CLAIM 2024 Update emphasizes reporting requirements for AI medical imaging studies, including specification of decision thresholds and operating conditions. European MDR implementation requires demonstration of consistent performance within specified operating parameters, necessitating explicit threshold documentation in technical files.
🔑 Regulatory Alignment
Threshold selection must be documented as part of intended use specification. Regulatory bodies increasingly require evidence that operating points were selected through systematic methodology rather than arbitrary cutoffs. The proposed calibration protocol generates documentation satisfying these requirements.
5.4 Continuous Monitoring Requirements
Post-deployment monitoring represents an essential component of threshold-based systems. Research identifies key metrics requiring ongoing surveillance:
- Performance drift: Tracking sensitivity/specificity over time to detect degradation
- Calibration stability: Verifying that confidence scores remain well-calibrated on new data
- Escalation rates: Monitoring proportion of cases in each tier for anomalies
- False discovery rates: Tracking proportion of AI-flagged cases that are true positives
5.5 Limitations and Future Directions
Several limitations constrain current threshold-based implementations. Most uncertainty quantification methods add computational overhead potentially impacting turnaround times. Threshold optimization requires substantial local validation data that may be unavailable in low-volume settings. And the interpretability of confidence scores remains challenging—clinicians may struggle to internalize what “0.73 confidence” means practically.
Future research should address development of uncertainty communication interfaces that convey confidence in clinician-friendly terms, perhaps mapping numerical scores to categorical levels (e.g., “high,” “moderate,” “low”) with associated recommended actions. Additionally, federated approaches to threshold optimization could enable smaller institutions to benefit from aggregate data while maintaining local customization.
6. Conclusion
Confidence thresholds and escalation protocols represent the critical translation layer between AI algorithmic capability and clinical utility. The evidence reviewed demonstrates that properly implemented threshold-based systems can achieve significant workflow improvements—30-50% workload reduction, substantially decreased turnaround times for critical findings, improved cancer detection rates with reduced false-positive recalls—while maintaining safety through appropriate human oversight of uncertain cases.
âś… Key Recommendations
- Calibrate before deployment: Post-hoc calibration essential for raw neural network outputs
- Select thresholds systematically: Use decision-theoretic approaches reflecting clinical context, not statistical optimization alone
- Implement tiered escalation: Three-tier architecture balances efficiency with safety
- Frame abstention carefully: AI non-prediction must trigger enhanced review, not reduced attention
- Monitor continuously: Threshold performance requires ongoing verification against drift
- Adapt locally: Generic thresholds require validation and adjustment for local populations
For Ukrainian healthcare systems, confidence-based AI deployment offers a mechanism to amplify limited radiologist resources while maintaining diagnostic quality. The proposed framework—with conservative initial thresholds, teleradiology-integrated escalation paths, and intensive early monitoring—provides a pathway to implementation appropriate for the Ukrainian context. Success requires investment in validation infrastructure, training for confidence interpretation, and commitment to continuous improvement based on real-world performance data.
The transformation from AI as standalone diagnostic tool to AI as intelligent workflow orchestrator represents a maturation of the field. When machines can articulate their uncertainty, and when clinical systems are designed to respond appropriately to that uncertainty, the promise of AI-augmented medicine moves closer to realization. The confidence threshold is not merely a technical parameter—it is the point where algorithmic output meets clinical responsibility, and getting it right is essential for both efficiency and patient safety.
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This article is part of the “Machine Learning for Medical Diagnosis” research series.
© 2026 Oleh Ivchenko | Stabilarity Hub | ONPU