2026-04-01
Rohit Dhawan, Mohit Dhawan
Modern CI/CD pipelines face a critical challenge. While AI tools accelerate code generation, static pipelines have become the primary bottleneck to delivery velocity. Flaky tests and pipeline noise create a persistent challenge, with reported failure rates ranging from 11 to 27 percent for test flakiness and 5–16 percent for noise-induced build failures. This forces teams to spend more time investigating false failures than building features. As systems scale across regions and dependencies, these problems compound and threaten the fundamental promise of continuous delivery. We introduce a framework that transforms CI/CD pipelines from deterministic scripts into intelligent, adaptive systems. At its core is the Sense-Analyze-Predict-Act-Learn loop, which we call SAPAL. This loop extends classical adaptive models with CI/CD specific capabilities including flakiness characterization, dependency risk scoring, multi-region awareness, and developer feedback. We operationalize this loop through a five-layer architecture spanning data collection, reliability intelligence, predictive modeling, adaptive execution, and human-AI collaboration. Three novel metrics quantify pipeline intelligence. Pipeline Health Index measures overall reliability. Test Stability Score identifies flaky patterns. Failure Prediction Confidence validates model accuracy. Three scenarios demonstrate application to real CI/CD challenges. Intelligent retry strategies, grounded in empirical studies of flaky test detection and resolution, project 60 percent reduction in flaky-induced build failures. ML-based test selection techniques from recent literature suggest 50 to 80 percent reduction in feedback time. Stability-aware deployment orchestration adapts rollout strategies to regional reliability patterns. These projections synthesize findings from published component studies rather than measurements from unified framework deployment. By enabling pipelines to learn from executions, predict with calibrated confidence, and adapt to behavior patterns, this framework provides a practical path toward reliable delivery at scale where intelligence is essential, not optional.
DOI: 10.3389/frai.2026.17765462026-04-01
Apoorva Aravindkumar, Marimuthu Ramadoss, Saqhibuddeen Ahmed Fakhruddin Ahmed, Vidhya Sampath, Kishor Lakshminarayanan
BackgroundExplainable artificial intelligence (XAI) is used in healthcare to make machine-learning outputs more transparent and clinically usable. This is important because many machine learning models work like a “black box” which can hide bias, reduce trust in the model. XAI addresses this problem by showing which features or image regions influenced a result, either for one patient or across a dataset.ObjectivesOur objective is to provide a clear, systematic review of how XAI is being used in healthcare. We summarize the main XAI methods, the data and models they are paired with, and how these explanations support clinical understanding across imaging, diagnosis, and rehabilitation.MethodsWe performed a systematic review with narrative synthesis (2020–2025) of 36 empirical studies across three verticals–Imaging (n = 10), Diagnosis (n = 16), and Rehabilitation (n = 10) that are identified via PubMed/MEDLINE, IEEE Xplore, and Google Scholar, following PRISMA 2020 guidelines. We included research studies that employed XAI in the three mentioned verticals. We excluded review articles and viewpoint studies. Screening numbers were - records identified 1,481; duplicates removed 647; other removals 187; screened 647; excluded 532; reports sought 115; not retrieved 31; assessed 84; full-text excluded 48; included 36. From each study we extracted ML models, XAI methods, study design, methodologies, and dataset/source. Meta-analysis was not undertaken due to heterogeneity.ResultsAcross 36 studies, SHAP was used in 21 studies, Grad-CAM in ~12/36, and LIME in ~11/36. A clear method-modality fit emerged with Imaging predominantly using saliency/heat-map methods, especially Grad-CAM, for spatial evidence. Diagnosis and Rehabilitation were dominated by feature-attribution tools like SHAP and LIME for global and case-level explanations. Many papers combined ≥ 2 explainers to cross-check interpretations namely SHAP+LIME, and Grad-CAM + LIME.ConclusionRecent healthcare XAI demonstrates consistent method-modality fit and frequently combine two or more methods, helping translate opaque predictions into clinician-oriented reasoning. To enable trustworthy deployment, future work should pair these practices with standardized XAI reporting, faithfulness/stability assessments, and external, cross-site validation.
DOI: 10.3389/frai.2026.17495272026-03-31
John Albanese
Large language models (LLMs) and retrieval-augmented generation (RAG) systems have achieved remarkable linguistic fluency, and many now implement persistent cross-session memory at the application layer. However, these mechanisms typically rely on external storage and reinjection of stored content rather than structural reorganization of memory relationships. As a result, they remain limited in their ability to integrate affective salience into a dynamically evolving internal memory topology capable of supporting coherent long-term behavior. To address this gap, we introduce Realtime Editable Memory Topology (REMT), an architectural framework for imbuing conversational agents with persistent autobiographical memory organized as an evolving graph of emotionally valenced nodes. REMT formalizes synthetic neuroplasticity through explicit update rules governing edge reinforcement, decay, and pruning, and introduces a bounded Mood Index that modulates retrieval bias and response generation as a function of accumulated affective experience. In this Perspective, we argue that memory-grounded architectures integrating insights from cognitive science, affective computing, and memory-augmented neural systems are necessary for building adaptive conversational agents with stable long-term interactional tendencies. We conclude by outlining a roadmap for empirical validation using an internally developed evaluation framework, with results to be reported in a future Original Research article.
DOI: 10.3389/frai.2026.17495172026-03-31
Anar Sultangaziyeva, Madina Sambetbayeva, Nurzhan Mukazhanov, Bayangali Abdygalym, Sandugash Serikbayeva
IntroductionClinical exome sequencing reports contain valuable genetic and phenotypic information but are typically stored in unstructured text form, making automated biomedical information extraction challenging. For the Russian language, publicly available annotated corpora for genetic report analysis remain extremely limited.MethodsWe present GENEXOM, the first multi-level annotated corpus of Russian-language clinical exome sequencing reports designed for biomedical information extraction. The corpus includes 5,318 reports (318 authentic and 5,000 synthetic) and comprises 16 entity types and 7 relation types aligned with HGVS, OMIM, ClinVar, and ACMG/AMP standards. Annotation was performed in the Label Studio platform by expert geneticists. Baseline transformer models (RuBERT, RuBioBERT, ModernBERT) were fine-tuned for Named Entity Recognition (NER) and Relation Extraction (RE).ResultsThe annotation achieved span-level F1-IAA = 0.83 and macro κ = 0.79 ± 0.04, indicating substantial inter-annotator agreement. Among the evaluated models, ModernBERT achieved the best performance with F1 = 0.88 ± 0.03 for NER and F1 = 0.836 ± 0.04 for RE on the held-out test set.DiscussionThe GENEXOM corpus provides a linguistically and clinically adapted resource for Russian medical NLP and supports downstream tasks such as variant interpretation, phenotype–disease mapping, and biomedical knowledge graph construction. The corpus and accompanying code are publicly available for research purposes.
DOI: 10.3389/frai.2026.17668992026-03-31
Aqib Anees, Syed Asim Jalal, Hassan Jalil Hadi, Naveed Ahmad, Mohamad Ladan
Wrong-turn violations in safety-critical spaces such as road roundabouts are a type of traffic violation that can lead to traffic congestion and increase the risk of road crashes. Although many researchers have focused on detecting various traffic violations, wrong-turn violations have not received enough attention. This may be due to a lack of relevant datasets. This study aims to address this gap. We developed a deep learning–based approach to detect wrong-turn traffic violations at roundabouts. The proposed system captures video from strategically placed cameras at roundabouts, which is then fed into an artificial intelligence (AI) model capable of detecting vehicles committing wrong-turn violations in real time. For this purpose, we utilized the popular You Only Look Once (YOLO) algorithm. Due to the absence of an existing dataset for this specific type of violation, we created our own. Images were collected and annotated from local roundabouts in Peshawar, Pakistan. The YOLO model was trained on this dataset and evaluated using standard performance metrics, including accuracy and recall. The results suggest that the proposed approach has strong potential for refinement and real-world implementation.
DOI: 10.3389/frai.2026.17027562026-03-30
Abedalmuhdi Almomany, Uzair Soomro, Anwar Al Assaf, Alaa Abd-Alrazaq, Rafat Damseh, Muhammed Sutcu, B. S. Ksm Kader Ibrahim, Barış Yıldız
Brain tumors pose a major challenge in neuro-oncology due to their high mortality rates and complex diagnosis. This review summarizes recent advances in using artificial intelligence (AI), particularly deep learning, in conjunction with thermal imaging and simulated thermal mapping for brain tumor detection. AI methods such as convolutional neural networks (CNNs), hybrid architectures, and bioheat transfer models, including the Pennes equation, are evaluated to determine how temperature variations, tumor biology, and image preprocessing influence malignancy classification. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), provide detailed structural information but are often costly, invasive, and limited in their ability to capture physiological data. Recent studies indicate that integrating AI with thermal imaging, either through direct infrared thermography or simulated thermal maps derived from MRI, enables non-invasive, physiology-aware diagnosis. The review examines current approaches to thermal data preprocessing, simulation, deep learning-based tumor segmentation, and malignancy prediction, as well as key evaluation metrics, model interpretability tools, and recent performance outcomes. Despite ongoing progress, challenges remain, including limited availability of multimodal datasets, variability in thermal signatures, and the need for clinical validation. Future research directions include large-scale data collection, advanced thermal modeling, multimodal fusion frameworks, and the development of explainable AI tools that meet clinical standards. In resource-limited settings, AI-powered thermal imaging may serve as a valuable supplement to traditional diagnostics, offering safer, more precise, and more accessible brain tumor detection. This technology has the potential to improve patient outcomes and transform neuro-oncology practices by integrating anatomical and functional insights. This review critically evaluates current evidence and identifies the challenges that must be addressed to facilitate the translation of promising research into clinical practice.
DOI: 10.3389/frai.2026.17444102026-03-26
Yang Tian, Ziyu Liu, Chaitanya Pallerla, Siavash Mahmoudi, Ramesh Bahadur Bist, Yiting Xiao, Terry Howell, Jeyamkondan Subbiah, Dongyi Wang
Ensuring food safety requires rapid and accurate detection of pathogens such as Escherichia coli O157:H7. Here, we report a portable electrochemical immunosensor coupled with machine learning (ML) that enables quantitative prediction even when the impedance response is not strictly linear with concentration. The sensor employs protein A-mediated oriented antibody immobilization on a gold electrode and measures target binding using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry. To move beyond single-parameter equivalent-circuit fitting, we apply distribution of relaxation times (DRT) deconvolution to resolve the impedance spectrum into mechanistic contributions associated with charge-transfer kinetics, double-layer charging, and transport-limited (diffusion/Warburg-type) processes, and use these DRT-derived features for concentration inference. Multiple ML models (partial least squares (PLS), Random Forest, histogram-based gradient boosting, support vector regression, ridge regression, and Gaussian process regression) were evaluated using leave-one-concentration-out cross-validation and independent hold-out testing, demonstrating accurate prediction on unseen concentration levels. Validation in poultry meat samples artificially inoculated with E. coli O157:H7 confirmed applicability in a food-relevant matrix, along with high selectivity against non-target bacteria and stable performance during storage. This work is novel in combining DRT-based mechanistic feature extraction with ML-based inference to deliver a field-deployable immunosensing platform for robust pathogen quantification in complex food samples.
DOI: 10.3389/frai.2026.1741144