IndicBERT-HPA: Reliability-Oriented Multilingual Orthopedic Decision Support with Selective Verification Deferral
This paper presents a framework for classifying free-text orthopedic clinical notes in English, Hindi, and Punjabi, introducing IndicBERT-HPA, a domain-adaptive encoder augmented with language-aware orthopedic adapter heads. The system is evaluated against multilingual transformers, a DistilBERT baseline, and zero-shot LLMs, with zero-shot LLMs found substantially less effective than task-adapted encoders for closed-set clinical classification. IndicBERT-HPA achieves Macro-F1 of 0.8792 and AUPRC of 0.902 under natural clinical prevalence. A deterministic selective-verification layer combining confidence gating, evidence-consistency checking, and language-risk screening improves accuracy from 71.5% to 84.4% at 72.3% coverage on a 5,000-record held-out set.
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Risk-Aware Hybrid Selective Classification for HIV Suspicion Identification in Spanish Clinical Notes
This paper proposes a hybrid selective classification framework for clinical NLP that explicitly handles both aleatoric and epistemic uncertainty to avoid overconfident predictions in medical triage settings. The system combines Mondrian conformal prediction with a Multi-Centroid Mahalanobis Distance veto, evaluated on HIV suspicion identification in Spanish clinical notes. The authors demonstrate that standard uncertainty metrics and baseline classifiers suffer coverage collapse under strict reliability constraints, while their dual-verification approach isolates a trustworthy operational domain. The work critiques inflated benchmark metrics that arise from forcing deterministic classification on inherently ambiguous clinical instances.
Sentence-Level Clinical Provenance Categorization for Multidisciplinary Hospital Summarization Using Fine-Tuned Llama-3
This pilot study presents a pipeline for categorizing sentence-level clinical provenance across multi-source hospital notes, targeting structured summarization in high-complexity settings like the NICU. The authors fine-tune Llama-3 8B and 70B models on MedSecId (MIMIC-III annotations), achieving Macro F1 above 92% in-domain. Cross-domain evaluation reveals a scale-dependent transfer effect: SFT substantially improves the 70B model (+7% Macro F1) but yields only marginal gains for the 8B model. A quantized fine-tuned 70B model outperforms its full-precision baseline while reducing compute, suggesting quantized adaptation is viable for structured clinical NLP tasks.
Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech
This paper presents the first NLP-based dementia detection study for Filipino speech, constructing a parallel bilingual dataset of 4,000 DementiaBank-derived transcripts with manual Filipino translations. Five model families are evaluated across monolingual, zero-shot cross-lingual, and bilingual fine-tuning settings. English-trained BERT degrades sharply on Filipino (Macro-F1 = 0.455), but bilingual fine-tuning recovers performance to Macro-F1 = 0.969–0.973 across all transformer models. The key finding is that multilingual clinical NLP performance is driven by linguistic coverage during training rather than model scale or architecture.
BODHI: Contrastive embedding training for causal discovery in Large Behavioural Models
Researchers identify a critical failure mode in biomedical language model embeddings: off-the-shelf encoders (BioBERT, PubMedBERT, BioM-ELECTRA) assign high cosine similarity (0.76–0.92) to causally unrelated cross-domain pairs, achieving 0% accuracy on cross-domain discrimination. The paper introduces BODHI, a contrastive training approach using hard negatives mined from a biomedical knowledge graph, which improves within-vs-across-domain separation from 1.05x to 2.30x and raises discrimination gap by +0.392. The work targets Large Behavioural Models (LBMs)—foundation models that reason over personal life graphs—where false embedding proximity directly produces false causal edges. Additional contributions include an OpenVINO inference optimization achieving 133x latency reduction (1367ms to 10ms) on Intel AMX hardware, plus a counterintuitive finding that FP16 outperforms INT8 on this silicon.
Automated ICD Classification of Psychiatric Diagnoses Using NLP and LLMs
This study evaluates NLP and ML approaches for automating the mapping of free-text psychiatric descriptions to ICD diagnostic codes, using a dataset of 145,513 Spanish clinical records. Methods range from classical BoW/TF-IDF representations to transformer-based embeddings including e5_large, BioLORD, and Llama-3-8B. Fine-tuned e5_large achieved the best performance with a micro-F1 of 0.866, outperforming classical methods by capturing semantic nuance and medical terminology. The work highlights challenges of long-tail label distributions and ambiguity specific to psychiatric clinical language.
Computational audit finds ClinicalBERT amplifies demographic bias beyond training data distributions
Researchers present a systematic audit of representational bias in ClinicalBERT, a BERT-based model pretrained on MIMIC-III clinical discharge summaries, using two probing methodologies: Log Probability Bias Analysis and Masked Language Model probing across 98 clinical sentence templates and eight intersectional race-gender combinations. Of 32 statistically significant findings, 65.6% contradict observed corpus distributions, rising to 80% for Black patients and 87.5% for agency attribution under MLM probing. The key finding is that bias in ClinicalBERT operates predominantly through model-internal amplification rather than simple inheritance from training data, which has direct implications for clinical AI safety and deployment. This challenges the assumption that auditing training corpora is sufficient to characterize model bias.
IHUBERT: Persian RoBERTa-base model trained on 45GB semantically deduplicated corpus
Researchers introduce IHUBERT, a 125M-parameter monolingual Persian pretrained language model trained from scratch using the RoBERTa-base architecture on a 45GB curated subset of the Sepahr-Danesh collection (~7-8B tokens). The work features a multi-stage preprocessing pipeline including vector-database-based semantic deduplication for domain-balanced pretraining, and a 139k-vocabulary BPE tokenizer optimized for Persian morphology. IHUBERT is evaluated across seven Persian NLU benchmarks, achieving state-of-the-art results on extractive QA (PQuAD F1 88.35) and NLI (FarsTail Macro-F1 0.835). The paper contributes both a new model and a semantic deduplication methodology applicable to low-resource language pretraining.
Systematic evaluation of LLM prompt sensitivity in healthcare settings reveals safety risks
Researchers conduct a sensitivity analysis of both general-purpose and medical-specific LLMs using the MedMCQA benchmark, testing robustness to lexical and syntactic prompt perturbations. The study finds that even minor phrasing changes can alter clinical advice, and adversarial prompts can produce dangerous outputs such as incorrect dosages or omitted critical findings. Both general-purpose models (GPT-3.5, Llama 3) and domain-specific models (ClinicalBERT, BioLlama3, BioBERT) exhibit this fragility, with syntactic reordering and misleading contextual cues proving more destabilizing than simple paraphrasing.