Proactive-Unsupervised Screening

• Proactive-unsupervised screening is a set of methods that identifies atypical, high-risk cases without relying on explicit negative labels by leveraging uncertainty estimation and latent pattern discovery.
• It employs diverse techniques such as Dirichlet uncertainty models, autoencoders, clustering, and generative models across domains like power systems, medical imaging, and clinical monitoring.
• By shifting from reactive detection to proactive intervention, these approaches enable early warning, effective triage, and risk scoring in large, unlabeled datasets.

Searching arXiv for recent and directly relevant papers on proactive and unsupervised screening to ground the article. arxiv_search(query="proactive unsupervised screening", max_results=10) Proactive-unsupervised screening is a family of screening paradigms that seeks to identify atypical, high-risk, or future-critical cases before downstream failure, delayed review, or exhaustive enumeration, while avoiding explicit supervision for the target anomaly class, the target deployment domain, or confirmed negatives. In its most explicit formulation, contingency analysis in power systems is redirected from “pre-defined scenario screening to proactive-unsupervised screening,” where potentially risky scenarios are generated by learning how previous ones occurred (Tran et al., 6 Oct 2025). Taken together, recent work suggests that the same logic appears across medical imaging, clinical trial monitoring, population health, pharmacy review, and streaming video understanding: systems estimate uncertainty, model normality, discover latent structure, or generate future scenarios so that ungradable images, anomalous trajectories, unusual profiles, or emerging threats can be flagged early (Araújo et al., 2022, Zhao et al., 13 Jan 2026, Wang et al., 2 Jul 2026).

1. Conceptual scope and definitional boundaries

The literature does not use unsupervised in a single narrow sense. In glaucoma screening, the term refers to out-of-distribution detection “without providing examples of OOD in training,” even though referable glaucoma classification still uses in-distribution labels (Araújo et al., 2022). In AI-based tuberculosis screening, unsupervised domain adaptation means that the target domain contributes unlabeled images only, with no labeled target data required for adaptation (Ravin et al., 2021). In type 2 diabetes risk estimation, the unsupervised claim arises because the method “does not require explicit negatives or a binary classification setting,” instead transferring latent multimorbidity and polypharmacy patterns from diagnosed to undiagnosed populations (Kumar et al., 27 May 2025).

The term proactive is likewise heterogeneous. In clinical trial monitoring it denotes “$k$-step ahead” dynamic prediction and alerting before anomalous values are finalized in electronic data capture (Zhao et al., 13 Jan 2026). In digital ecosystem monitoring it denotes a shift from reactive detection toward anticipation of emerging synthetic threats, including anomaly detection in embedding spaces, unsupervised coordination detection on multi-layer graphs, and agentic verification layers (Chung et al., 28 May 2026). In medical video, proactive operation becomes a timing problem: a model must decide not only what to answer, but whether and when to raise alerts under temporally bounded evidence (Wang et al., 2 Jul 2026).

A recurrent misconception is that proactive screening is necessarily unsupervised. It is not. “EmoScan” is explicitly supervised, using a manually labeled dataset of 6,014 Romanized Sinhala tweets and a neural network with attention for depression symptom screening; it is proactive only in the sense of early identification and intervention support (Hewapathirana et al., 2024). A second misconception is that proactive-unsupervised screening is synonymous with autonomous diagnosis. Several studies instead frame it as first-pass flagging, triage, or monitoring rather than definitive clinical judgment, especially in pharmacy review, cognitive screening, and conversational systems (2011.01925, Tian et al., 15 Nov 2025, Hu et al., 10 Apr 2026).

2. Core methodological families

One major family replaces conventional confidence scores with explicit uncertainty modeling. In glaucoma screening, the network outputs evidence values $e_k = \text{softplus}(\text{logit}_k)$, with Dirichlet parameters $\alpha_k = e_k + 1$, class probabilities $\hat{p}_k = \alpha_k/S$, and total uncertainty $u = K/S$; low total evidence therefore yields high uncertainty (Araújo et al., 2022). This supports class prediction and sample-wise uncertainty estimation without OOD exposure.

A second family learns normal appearance or in-distribution structure and scores deviations. This includes multi-scale deep denoising autoencoders followed by one-class SVMs for retinal OCT anomaly segmentation and clustering (Seeböck et al., 2018), α-GAN and VAE-GAN variants for fetal ultrasound anomaly detection (Chotzoglou et al., 2020), GANomaly for atypical pharmacological profiles (2011.01925), and hybrid CNN-transformer reconstruction-discriminator architectures for mammography OOD detection (Zhang et al., 2024). In these systems, atypicality is expressed through reconstruction error, discriminator output, encoder discrepancy, or one-class boundary violation.

A third family derives screening signals from latent structure rather than explicit anomaly exemplars. In ASD screening, K-Means, Gaussian Mixture Model, Agglomerative Clustering, and DBSCAN are evaluated with post-hoc cluster-to-label mapping (Fink, 20 Feb 2025). In T2DM screening, Non-negative Matrix Factorization is applied to diagnosed cases, $X \approx WH$, and combined with feature prevalence and KL divergence to construct a continuous risk score for undiagnosed individuals (Kumar et al., 27 May 2025). In whole-heart function assessment, masked image modeling creates a latent space and temporally disentangled embeddings from unlabeled 2D+T cardiac MR data (Zhang et al., 5 Nov 2025).

A fourth family is generative and predictive rather than purely discriminative. DDPM-CS learns perturbation diffusion and samples potentially novel high-risk contingencies from noise (Tran et al., 6 Oct 2025). Bayesian latent class monitoring computes posterior predictive credible regions for multiple endpoints and flags future observations falling outside them (Zhao et al., 13 Jan 2026). MedStreamBench does not define a detector, but it formalizes how such systems should be evaluated when answers and alerts are time-dependent rather than static (Wang et al., 2 Jul 2026).

3. Medical imaging and visual screening

Medical imaging supplies the clearest demonstrations of proactive-unsupervised screening because screening datasets often contain abundant normal or in-distribution examples but limited annotations for rare anomalies, poor quality, or external-domain failures. In the AIROGS glaucoma setting, the Dirichlet model was trained without OOD supervision and used artificial masking of high-Grad-CAM regions in validation images to set an uncertainty threshold. The method produced a bimodal uncertainty histogram for ID and OOD images, achieved $\kappa = 0.69$ on the test set versus $0.58$ for the softmax baseline, and on the challenge final test reported pAUC $0.84$, TPR@95 $0.75$, $e_k = \text{softplus}(\text{logit}_k)$0 $e_k = \text{softplus}(\text{logit}_k)$1, and gAUC $e_k = \text{softplus}(\text{logit}_k)$2 (Araújo et al., 2022). A notable operational result is that uncertainty rose when the optic disc region was occluded, but not when random image regions were masked; the model also assigned low uncertainty to globally poor-quality images if the optic disc remained visible.

A closely related problem is domain shift rather than OOD quality failure. For tuberculosis chest X-rays, Domain Invariant Feature Learning builds a tri-network system with a Feature Generator, Domain Discriminator, and Label Classifier on top of a ResNet-50 backbone. The target domain remains unlabeled, but adversarial optimization forces source and target representations toward domain invariance (Ravin et al., 2021). The reported gains are substantial: for China $e_k = \text{softplus}(\text{logit}_k)$3 India, accuracy rises from $e_k = \text{softplus}(\text{logit}_k)$4 to $e_k = \text{softplus}(\text{logit}_k)$5; for USA $e_k = \text{softplus}(\text{logit}_k)$6 China, it rises from $e_k = \text{softplus}(\text{logit}_k)$7 to $e_k = \text{softplus}(\text{logit}_k)$8, approaching the upper baseline of $e_k = \text{softplus}(\text{logit}_k)$9.

Mammography has produced two distinct unsupervised paradigms. “Siamese Networks with Soft Labels” exploits contralateral symmetry as weak supervision, fits a two-component GMM to embedding distances, and replaces hard pair labels with soft probabilities in a Siamese loss (Vorst et al., 2024). After unsupervised pretraining, the learned encoder outperformed SimCLR and BYOL in mammogram patch classification, including abnormal-versus-normal AUC $\alpha_k = e_k + 1$0 on VinDr-256 and $\alpha_k = e_k + 1$1 on OPTIMAM-256. HAND instead models in-distribution mammograms directly through a CNN encoder, transformer encoder, CNN decoder, and latent discriminator, while synthetic OOD transformations and gradient reversal deliberately worsen OOD reconstructions (Zhang et al., 2024). Its reported OOD detection AUC was $\alpha_k = e_k + 1$2 internally and $\alpha_k = e_k + 1$3 on the external Mayo dataset, with biopsy clip AUC $\alpha_k = e_k + 1$4.

Other imaging studies emphasize marker discovery and functional representation learning. In retinal OCT, a multi-scale DDAE trained on healthy images, followed by one-class SVM anomaly detection and spherical K-means clustering, identified stable anomaly categories whose volumes yielded $\alpha_k = e_k + 1$5 accuracy for three-class healthy/early AMD/late AMD classification and AUC $\alpha_k = e_k + 1$6 for healthy versus intermediate AMD (Seeböck et al., 2018). In fetal anomaly screening, an α-GAN trained only on clinically confirmed normal controls achieved average AUC around $\alpha_k = e_k + 1$7 to $\alpha_k = e_k + 1$8 for Hypoplastic Left Heart Syndrome detection, with attention-based anomaly scores outperforming several one-class baselines (Chotzoglou et al., 2020). In cardiac MR, masked autoencoding with 70% random masking produced a latent space aligned with cardiac phenotypes, and temporal embeddings showed nearly perfect kNN grouping by phase, including $\alpha_k = e_k + 1$9, $\hat{p}_k = \alpha_k/S$0, and $\hat{p}_k = \alpha_k/S$1 same-segment clustering for $\hat{p}_k = \alpha_k/S$2 (Zhang et al., 5 Nov 2025).

Self-supervised pretraining can also be specialized for downstream unsupervised anomaly detection. PMSACL synthesizes multiple pseudo-abnormal classes through MedMix augmentation and optimizes a multi-centring plus contrastive objective so that normal images form dense clusters while pseudo-abnormal variants are well separated (Tian et al., 2021). On Hyper-Kvasir, PMSACL with PaDiM achieved AUC $\hat{p}_k = \alpha_k/S$3, specificity $\hat{p}_k = \alpha_k/S$4, and sensitivity $\hat{p}_k = \alpha_k/S$5, and it also improved UAD performance on fundus screening and COVID-19 chest X-ray benchmarks.

4. Structured populations, claims, and behavioral data

Outside imaging, proactive-unsupervised screening often appears as latent pattern discovery in large, weakly labeled, or unlabeled populations. In ASD screening, clustering methods were applied to 704 adult individuals after data cleaning, label encoding, and standard scaling over 19 features. The Gaussian Mixture Model achieved the best final clustering-to-label accuracy at $\hat{p}_k = \alpha_k/S$6, with ARI $\hat{p}_k = \alpha_k/S$7 and silhouette $\hat{p}_k = \alpha_k/S$8, while cross-validation yielded mean accuracy $\hat{p}_k = \alpha_k/S$9 and mean ARI $u = K/S$0 (Fink, 20 Feb 2025). The central implication is that clinically meaningful partitions can sometimes be recovered without labels during model fitting.

The T2DM framework scales this logic to administrative claims data. Using the US Merative MarketScan Commercial Claims and Encounters database, the study analyzed 17.1 million individuals, including 1.34 million diagnosed T2DM and 15.78 million undiagnosed persons, across 66,716 diagnosis and medication codes. NMF on diagnosed cases selected $u = K/S$1 latent components by an elbow criterion, and 1,629 features with $u = K/S$2 were retained (Kumar et al., 27 May 2025). Risk scores in the undiagnosed population classified 3.18% (502,144) as High Risk, 20.14% as Moderate, and 76.68% as Low; the resulting categories showed 75% agreement with clinical SME assessment and 89% agreement with an AI model. This is a particularly clear example of unsupervised screening defined by the absence of reliable negatives rather than the absence of any labels whatsoever.

Medication safety monitoring offers a different variant: anomaly detection over routine operations. A prospective study reconstructed 2,846,502 orders into 1,063,173 pharmacological profiles and compared pharmacists’ judgments with an unsupervised model family that included statistical frequency, classical anomaly detectors, an autoencoder, and a GANomaly variant (2011.01925). Profile-level performance was the strongest, with AUPR $u = K/S$3 for GANomaly versus $u = K/S$4 for the autoencoder and $u = K/S$5 for isolation forest. Order-level AUPR was only $u = K/S$6, but pharmacists still regarded the model as a useful screening tool, especially for prioritization and rare-error safety netting. The paper also makes a critical definitional point: atypical is not equivalent to erroneous.

Early pandemic identification shows how unsupervised screening can operate when confirmed cases are scarce. A hybrid anomaly detector combined unsupervised random-forest distances, KNN graph construction, density, and distance scores into $u = K/S$7, followed by log transformation and z-score-based thresholding (Ghajari et al., 2024). On COVIDx chest X-ray features, the method achieved average AUC $u = K/S$8, exceeding isolation forest at $u = K/S$9 and KNN at $X \approx WH$0. The authors position this as useful precisely because supervised classification is least reliable at outbreak onset.

5. Generative, predictive, and time-aware screening

The most expansive versions of proactive-unsupervised screening do not merely score present observations; they generate unseen critical cases or predict future anomalous ones. In DDPM-CS for power systems, each system state is treated as a structured matrix, Gaussian noise is added through a forward process, a U-Net learns the reverse denoising process, and inference starts from pure noise to generate new contingencies (Tran et al., 6 Oct 2025). The model was trained on the worst 10% of scenarios derived from continuation power flow and evaluated on IEEE-6, IEEE-14, and IEEE-30 systems. Most generated scenarios ranked among severe contingencies, and mean absolute error for critical load profiles was $X \approx WH$1 for IEEE-6, $X \approx WH$2 for IEEE-14, and $X \approx WH$3 for IEEE-30.

In longitudinal clinical trials, the Bayesian latent class model for multiple endpoints moves proactivity into dynamic risk monitoring. Subjects are assigned latent classes, endpoint trajectories include class-specific quadratic time effects plus site- and subject-level random effects, and posterior prediction is updated at every review point (Zhao et al., 13 Jan 2026). A new measurement is flagged if it falls outside a posterior credible region, such as an 80% region, for the next $X \approx WH$4 steps. In simulation, both the “Branching Out” and Highest Density Region algorithms achieved mean in-region proportion $X \approx WH$5, mean bias $X \approx WH$6, and mean sqrt MSE $X \approx WH$7. In real trial data, Week 2 prediction for EASI and IGA exceeded 95% in-region, while later predictions were approximately 77–78%; with EASI and SCORAD, first predictions were about 95% and later predictions about 87%.

Time-aware evaluation has become its own research problem. MedStreamBench integrates 22 medical datasets and 5,419 QA instances over retrospective, present, future, and proactive settings, and constrains models to prefix-limited evidence windows (Wang et al., 2 Jul 2026). In the proactive setting, the output space is restricted to no_alert, uncertain, or alert: <reason>, and scoring includes content correctness $X \approx WH$8, responsiveness $X \approx WH$9, post-evidence stability $\kappa = 0.69$0, and overall score $\kappa = 0.69$1 for streaming future or proactive tasks. The benchmark’s main empirical message is that models that appear competent offline often degrade markedly in streaming and proactive conditions, indicating that screening quality depends on timing discipline as well as recognition accuracy.

6. Evaluation, deployment, and recurring tensions

A persistent tension in proactive-unsupervised screening is the relationship between utility and interpretability. In several imaging studies, the alerting signal is clinically meaningful only if it tracks diagnostically relevant structure rather than superficial corruption. The glaucoma Dirichlet model’s dependence on the optic disc rather than random masked regions is therefore central, not incidental (Araújo et al., 2022). In retinal OCT, anomaly clusters were reviewed qualitatively and some aligned with recognizable pathology while others did not match known markers, raising the possibility of marker discovery rather than only error detection (Seeböck et al., 2018).

Another tension concerns the role of human operators. Pharmacists preferred concrete order-level predictions even though profile-level performance was objectively better, illustrating that deployment preferences need not track aggregate metrics (2011.01925). Conversational systems for neurocognitive screening sharpen this issue further. TalkTive analyzed 246 cognitive-assessment conversations, annotated 2,732 reactive backchannels and 2,037 proactive backchannels, and in a study with 36 older adults found that proactive backchanneling was more appreciated than reactive backchanneling (Ding et al., 2022). Yet the broader CAI literature emphasizes a conflict between emotional support and standardized administration: users and caregivers seek encouragement and clear result explanation, whereas clinicians resist interaction styles that could compromise test validity or blur the line between recommendation and diagnosis (Hu et al., 10 Apr 2026).

Repeated unsupervised use introduces both benefits and complications. Mini-SPACE, a home-based serious game for cognitive impairment screening, showed good test-retest reliability with ICC(2,1) $\kappa = 0.69$2 and ICC(2,3) $\kappa = 0.69$3, and prediction of MoCA improved with repeated measures (Tian et al., 15 Nov 2025). At the same time, the study reports learning effects, a reminder that proactive screening systems can become more valid after familiarization but also require interpretation rules that account for practice. MedStreamBench makes an analogous point in video: a model should not merely answer correctly once, but maintain stable post-evidence behavior without premature alerting (Wang et al., 2 Jul 2026).

A final recurring controversy is whether unsupervised screening should be evaluated as detection, triage, or diagnosis. The corpus favors triage and early warning. HAND is framed as a quality-control gate for external mammograms rather than a lesion diagnostician (Zhang et al., 2024). The T2DM framework outputs percentile-based risk categories rather than a binary disease label (Kumar et al., 27 May 2025). Clinical trial anomaly screening is intended to generate objective alerts to stakeholders, not final adjudication (Zhao et al., 13 Jan 2026). This suggests that proactive-unsupervised screening is best understood as an anticipatory layer in a larger decision pipeline: it narrows attention, surfaces atypical structure, and supports timely intervention, while leaving definitive interpretation to downstream models, experts, or formal diagnostic workflows.