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AI-based Cognitive-linguistic Features for Dementia Assessment in Picture Description

Published 16 Jun 2026 in eess.AS | (2606.18054v1)

Abstract: Picture descriptions provide valuable insights into several clinical constructs related to cognitive-linguistic abilities. However, operationalizing these constructs into quantitative measures remains challenging, limiting interpretability and clinical utility. We introduced seven constructs tailored to the Cookie Theft picture description task and prompted LLMs to evaluate them, generating severity scores and example-based explanations. Among the examined LLMs, Claude 3.5 Sonnet performed the best, producing severity scores that significantly distinguish cognitively impaired individuals from healthy controls. The model achieves a high accuracy of 85% on the ADReSS dataset. Expert evaluation of Claude's scores and explanations yields a 3.99/5 average agreement. The findings demonstrate the potential of LLMs to operationalize clinical constructs and generate interpretable evaluations, offering a promising approach for accessible cognitive screening tools.

Summary

  • The paper introduces an LLM-based paradigm that directly quantifies seven cognitive-linguistic constructs from structured picture descriptions to assess dementia.
  • It employs structured prompts, few-shot examples, and multi-model benchmarking to generate clinically interpretable severity scores that correlate with standard tests like the MMSE.
  • Results highlight that the Claude LLM achieved superior discriminative power, enabling an XGBoost classifier to reach 85% accuracy, while open-source models lagged behind.

AI-Based Cognitive-Linguistic Feature Extraction for Dementia Assessment from Picture Description

Introduction

This paper presents a systematic framework for operationalizing cognitive-linguistic constructs in dementia assessment using LLMs, particularly through analysis of the "Cookie Theft" picture description task. Traditional linguistic analysis in clinical neuropsychology encounters considerable challenges: manual feature extraction is laborious, measurement of complex constructs is ambiguous, high-dimensional features are hard to interpret, and current automated methods often lack both clinical transparency and task specificity. Addressing these limitations, the authors introduce a paradigm wherein LLMs are directly prompted to evaluate clinically relevant cognitive-linguistic constructs, generating both severity scores and rationale-driven explanations.

Framework and Methodology

Seven constructs, specifically adapted for the Cookie Theft picture description, were informed by clinical literature and refined for LLM input: Saliency of Information, Semantic Categories, Referential Cohesion, Causal and Temporal Relations, Mental State Language, Structural Language and Speech, and General Cognition and Perception. The authors designed highly structured prompts providing construct definitions, output templates, and demonstration examples for few-shot adaptation.

Data were sourced from the DementiaBank Pitt Corpus and the Wisconsin ADRC dataset. Both manual and automatic (WhisperX) speech transcripts were tested. Multiple LLMs were benchmarked: Claude 3.5 Sonnet, two GPT-4o versions, and open-source LLaMA-3.2-3B. Test-retest reliability, discriminative validity across clinical and control groups, and correlations with MMSE/RAVLT scores were computed to establish clinical relevance and repeatability.

Additionally, open-source models were further adapted via QLoRA-based fine-tuning and logistic regression. Dementia case/control classification was benchmarked on the ADReSS dataset using XGBoost, employing LLM-derived severity scores as features, with interpretability assessed using SHAP values.

Results

LLM Construct Discrimination and Validity

Claude 3.5 Sonnet demonstrated consistently superior discriminative power across all seven constructs versus other LLMs, with Hedge's g effect sizes indicative of medium to strong group separation and ICCs reflecting moderate test-retest reliability. Notably, severity scores generated by Claude correlated robustly with MMSE, confirming construct validity.

Scores derived from GPT-4o performed slightly worse but maintained significant discrimination. LLaMA, even with task-specific adaptation, yielded marginal effect sizes and poor differentiation between groups, highlighting the current limitations of small open-source LLMs for this application, especially when confronted with milder clinical cohorts (as in W-ADRC).

Dementia Detection Performance

Severity scores generated by Claude enabled an XGBoost classifier to achieve 85% accuracy and 0.84 F1 on the balanced ADReSS dataset, outperforming both other LLMs and prior LLM-based pipelines, though still trailing BERT-based methodologies in raw accuracy. ASR-generated transcripts led to a reduction in performance for all models except for LLaMA on W-ADRC, where cleaner audio conditions improved ASR quality and mitigated this gap.

Construct-level SHAP analysis revealed that "Saliency of Information" and "Semantic Categories" provided the greatest predictive value for dementia classification, while "Causal and Temporal Relations" and "Mental State Language" contributed the least—a finding corroborated by both statistical analysis and expert review.

Clinical Interpretability

A preliminary clinical validation was conducted with eight speech-language pathologists evaluating model outputs. The average agreement for Claude's explanations reached 3.99/5, with an ICC of 0.63 among raters, signifying moderate expert consensus. Disagreements centered on construct definitions (too broad or under-specified), over-penalization for informal language or lack of mental state attributions, and occasional failures to recognize severe impairments in event sequencing.

Implications and Limitations

This body of work demonstrates that advanced LLMs can directly operationalize complex, task-specific cognitive-linguistic constructs—something traditional NLP pipelines cannot achieve without extensive feature engineering. The framework enables automatic, interpretable scoring for cognitive screening tools from naturalistic language samples, a significant advantage for scalable, remote, or resource-constrained clinical settings.

However, the study also reveals key challenges. Model performance is highly contingent on construct clarity, transcript quality (manual vs ASR), and underlying model scale. The open-source LLaMA model, even with QLoRA adaptation, failed to match the robustness of proprietary LLMs. Moreover, construct-level evaluation surfaced boundary ambiguities and domain adaptation issues (e.g., SLPs questioned the relevance of "Mental State Language" in the absence of explicit instructions).

Clinical agreement, while encouraging, remains only moderate—necessitating further refinement in prompt design, construct definitions, and few-shot example selection. The study also highlights a gap in the evaluation of explanation validity and practical clinical deployment due to privacy constraints and local compute limitations.

Directions for Future Research

  • Refinement and Standardization: Greater care in task-specific construct specification and prompt engineering will enhance model alignment with clinical reasoning and inter-rater reliability.
  • Higher-Capacity Open-Source Models: The development or utilization of larger, more capable open-source LLMs is necessary for wide deployment in PHI-restricted settings.
  • Improved ASR-Windows: ASR pipelines must be fine-tuned to preserve clinically salient linguistic phenomena, especially in noisy or variable audio datasets.
  • Explanation Validity: Rigorous, large-scale clinical validation of model-generated explanations is needed to assess their real-world interpretability and trustworthiness.
  • Integration with Multimodal Data: Extending the framework to accommodate acoustic, prosodic, and neuropsychological features will better capture the multidimensional nature of cognitive-linguistic impairment.
  • Adaptation to Diverse Task Demands: Incorporating task context (e.g., explicit prompting of mental state language) may mitigate construct misalignment observed here.

Conclusion

The framework presented in this paper provides a strong foundation for interpretable, automatable dementia screening using picture description tasks and state-of-the-art LLMs. Results affirm that advanced LLMs can operationalize complex clinical constructs and generate both actionable scores and rationales, with performance and interpretability approaching requirements for clinical adoption. Key barriers remain in open-source model efficacy, construct standardization, and explanation reliability—each representing an avenue for critical future progress in LLM-based cognitive assessment.

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Explain it Like I'm 14

What’s this paper about?

This paper asks a simple question: Can AI listen to (or read) how someone describes a picture and spot early signs of memory or thinking problems, like those seen in Alzheimer’s disease? The authors use a well-known picture called “Cookie Theft” (a mom at a sink while two kids secretly grab cookies) and show that modern AI can “grade” people’s descriptions in clear, human-like ways that doctors can understand.

What were the researchers trying to find out?

They focused on two main goals:

  • Can a LLM—a powerful AI that understands and scores text—turn important clinical ideas about language into simple numbers that show how a person is doing?
  • Are those scores good at telling apart healthy people from people with cognitive impairment (trouble with thinking and memory), and are they understandable to speech-language pathologists (SLPs)?

To do this, they tailored seven specific things to look for in the “Cookie Theft” picture descriptions:

  • Saliency of information: Do you focus on the most important parts of the picture?
  • Semantic categories: Do you use specific, accurate words (like “boy” instead of “child”)?
  • Referential cohesion: Do your pronouns (he, she, it) make it clear who you mean?
  • Causal and temporal relations: Do you describe events in a sensible order and explain cause-and-effect?
  • Mental state language: Do you infer what people in the picture might be thinking or feeling?
  • Structural language and speech: Are your sentences and word choices accurate and fluent?
  • General cognition and perception: Do you organize your description well and avoid repeating yourself?

How did they do the study?

Think of the AI like a careful teacher grading a short oral or written assignment with a 0–3 scale (0 = normal, 3 = severe problem) for each of the seven areas above.

Here’s the basic approach, in everyday terms:

  • People described the “Cookie Theft” picture. Some were healthy; some had cognitive impairment.
  • The researchers fed those descriptions into different AIs (LLMs). Some transcripts were typed by humans, others were created automatically from audio using an AI speech-to-text tool (like an automated captioner).
  • The AI was “prompted” with clear instructions, simple definitions for each of the seven areas, and a few examples to learn how to grade (this is called “few-shot learning,” like showing a student a few sample essays and how they’re scored).
  • The AI gave each person seven scores (one per area) and wrote short explanations with examples from the person’s own words.
  • The team checked how well these scores matched real clinical information (like MMSE, a standard 0–30 memory/thinking test), whether they separated healthy and impaired groups, and how consistent they were if a person was tested more than once.
  • They also tried two extra approaches for privacy-friendly, local use: (1) fine-tuning a small open-source AI (LLaMA) and (2) using traditional machine learning (logistic regression) on text embeddings to predict the seven scores the best AI produced.

What did they find, and why does it matter?

Here are the big takeaways:

  • The best-performing AI (Claude 3.5 Sonnet) did a strong job: Its scores clearly separated healthy people from those with cognitive impairment and matched standard cognitive test scores fairly well. Using those seven scores, a machine-learning classifier reached about 85% accuracy on a common benchmark (ADRESS) for telling who had Alzheimer’s and who didn’t.
  • Human experts mostly agreed with the AI’s explanations: Speech-language pathologists rated their agreement at about 4 out of 5 on average. That means the AI’s feedback didn’t just give numbers—it made sense to clinicians.
  • Not all AIs were equal: GPT-4o came in second. Smaller or open-source models (like a small LLaMA) did noticeably worse out-of-the-box.
  • Automated transcripts can help or hurt: On one dataset with older, noisier recordings, automatic speech-to-text made performance drop. On a newer dataset with clearer audio, automatic transcripts worked better. In short, audio quality matters.
  • Some areas were easier to judge than others: “Saliency of information” and “Semantic categories” contributed most to accurate decisions. “Mental state language” and “Causal and temporal relations” were weaker—sometimes the AI was too strict (penalizing people for not guessing feelings when the task didn’t ask for it) or too lenient (missing broken cause-and-effect).
  • Local, privacy-friendly options need work: Fine-tuning the small open-source model didn’t help much. A simpler method (logistic regression trained to mimic the best AI’s scores) did better, but still not great. This shows why powerful AI models are helpful—but also why we need better local solutions for clinics with private data.

What’s the bigger impact?

This research shows a promising path for quick, low-burden screening:

  • It turns complex clinical ideas into easy-to-read numbers and short explanations, making AI assessment more transparent than black-box systems.
  • It could help clinicians spot early signs of cognitive trouble using a short picture description task—something many clinics already do—without requiring long, exhausting testing.
  • With more work (clearer instructions for tricky areas, better speech-to-text tuned for clinical speech, and stronger local models), this approach could become an accessible tool to support early detection and monitoring—especially in settings with limited specialist time.

In short, the study suggests that modern AI can listen to how we describe a simple picture and provide understandable, clinically meaningful clues about our thinking and language abilities. It’s not a replacement for a doctor, but it could become a helpful early warning system that’s fast, explainable, and widely available.

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Knowledge Gaps

Knowledge gaps, limitations, and open questions

Below is a single list of specific, actionable gaps and open questions that remain unresolved and could guide future research.

  • Construct definitions and scope: Several constructs (e.g., “Saliency of information,” “General cognition and perception”) are underspecified and partially overlapping; develop operationalized, non-overlapping rubrics with explicit anchor examples per severity level to reduce ambiguity and score inflation.
  • Elicitation–construct mismatch: “Mental state language” may not be elicited by the standard Cookie Theft prompt; test alternative instructions or probes that explicitly invite mental state inferences and quantify their impact on construct scores.
  • Causal/temporal reasoning reliability: LLMs showed leniency or hallucinations on “Causal and temporal relations”; design targeted prompting, adversarial test cases, and structured evaluation sets to measure and improve causal inference fidelity.
  • Explanation faithfulness vs. plausibility: Current clinician ratings gauge plausibility, not whether rationales truly reflect model-internal evidence; implement faithfulness tests (e.g., input perturbations, highlight-agnostic evaluations) and measure explanation consistency and sufficiency.
  • Ground-truth scarcity at construct level: Severity labels per construct largely come from the model (pseudo-labeling) or limited expert examples; curate a larger, multi-rater, construct-level gold standard with adjudication and report inter-rater agreement and scoring guidelines.
  • Psychometric validation: No internal consistency, test invariance, factor structure, or minimal clinically important difference (MCID) analyses were conducted; perform reliability/validity studies (e.g., CFA, IRT) to establish the measurement properties of the seven constructs.
  • Test–retest repeatability: ICCs are only moderate; investigate protocol changes (prompting, scoring anchors), aggregation strategies (multiple descriptions per subject), and calibration methods to improve within-subject reliability.
  • Score calibration and drift: The 0–3 scale lacks calibrated thresholds tied to clinical benchmarks; establish clinically meaningful cut points, monitor version-induced drift across LLM updates, and report per-sample uncertainty or confidence intervals.
  • Domain shift and external validity: Performance dropped on W-ADRC and with milder impairment; conduct cross-site, cross-cohort, and prospective validations with pre-registered protocols, including MCI and non-AD dementias for differential diagnosis.
  • Demographic and fairness analyses: Beyond age/sex/education adjustment, there is no subgroup bias audit; evaluate fairness across race/ethnicity, dialect, SES, multilingual status, and hearing/speech comorbidities, and implement mitigation if disparities are found.
  • Informal and dialectal language handling: The model over-penalizes colloquialisms and elliptical constructions; develop dialect-aware prompts, normalization strategies, or fine-tuning with diverse speech to avoid misclassifying typical variation as impairment.
  • Task specificity vs. generalization: The approach is tailored to Cookie Theft; test transferability to other picture description tasks (e.g., Picnic, Frog Story), open-ended narratives, and dialogue to examine construct stability across tasks.
  • Language and cultural generalization: Assess performance in other languages and cultural contexts where “saliency” and semantic categories differ; build multilingual, culturally diverse corpora and validate construct definitions cross-culturally.
  • Audio information underuse: The pipeline relies on transcripts plus pause markers; systematically integrate acoustic–prosodic features (e.g., speech rate, articulation, prosody, dysfluencies) and evaluate multimodal gains per construct.
  • ASR robustness and calibration: ASR errors affected construct scores variably across datasets; quantify error sensitivity per construct, compare ASR engines, fine-tune ASR on clinical speech, and evaluate diarization/pausing thresholds’ clinical validity.
  • Data preprocessing choices: Removing expert annotations (except pauses) may discard clinically salient cues; run ablations comparing with/without annotations to quantify the impact on construct scoring and classification.
  • Label circularity in adaptation: Using Claude-generated scores as training targets for LLaMA/logistic regression risks propagating model biases; prioritize human-annotated labels for supervised fine-tuning and use Claude labels only as weak supervision with correction.
  • Few-shot example design: Only five examples were used and may not cover construct boundaries or diverse error types; study few-shot selection (diversity/coverage), number of shots, and retrieval-augmented prompting on construct fidelity.
  • Prompt sensitivity and reproducibility: Results may be prompt- and temperature-sensitive; perform systematic prompt ablations, self-consistency ensembling, and release prompts/versioning to ensure reproducibility and stability.
  • Longitudinal clinical utility: Correlations with MMSE are cross-sectional; test whether construct trajectories predict conversion (e.g., CU→MCI, MCI→AD), rate of decline, and treatment response, and define actionable thresholds for triage.
  • Clinical workflow integration: Specify how scores and explanations affect clinician decisions, time savings, and patient outcomes; run user-centered trials assessing acceptability, trust, and decision support value in real clinic settings.
  • Comparator baselines: Head-to-head comparisons with strong traditional pipelines (e.g., combined acoustic–linguistic models, SOTA BERT/wav2vec systems) are limited; conduct controlled benchmarks with shared data splits and report calibration and reliability.
  • Construct importance disparities: “Mental state language” and “Causal/temporal” contributed least to classification; reassess their definitions, elicitation, and scoring criteria, or consider refining or replacing low-yield constructs.
  • Model size and local deployment: Only a small open-source model (LLaMA-3.2-3B) was tested; evaluate larger local models, quantization/distillation strategies, and on-device feasibility under PHI constraints while maintaining accuracy and reliability.
  • Privacy, security, and cost: Proprietary LLM reliance raises PHI, cost, and latency concerns; quantify compute/cost trade-offs, implement privacy-preserving inference (e.g., local/private LLMs), and evaluate security and compliance in deployment.
  • Data overlap and leakage risk: Despite some exclusions, dataset overlaps (e.g., with ADRESS) persist; provide full overlap audits, subject-level splits, and pre-registration to eliminate leakage and ensure honest generalization estimates.
  • SHAP interpretation limits: Feature importance was computed on a downstream classifier; validate that per-construct scores themselves are stable and meaningful, and examine multicollinearity among constructs affecting SHAP attributions.
  • Measurement of uncertainty and confidence: The system provides point scores only; add per-construct uncertainty estimates and abstention mechanisms to support safe clinical decision-making.
  • Open science and reproducibility: Release code, prompts, example sets, and (where permissible) de-identified data or synthetic surrogates to enable independent replication and extension.
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Practical Applications

Practical Applications Derived from the Paper’s Findings

Below are actionable, real-world applications grounded in the paper’s methods and results. They are grouped into immediate and longer-term horizons, with sector links and feasibility notes.

Immediate Applications

  • Clinician-facing picture-description screener with interpretable profiles (Healthcare)
    • Use the seven construct scores (0–3) and example-based explanations from an advanced LLM (e.g., Claude 3.5 Sonnet) to triage and document cognitive-linguistic deficits from the Cookie Theft task. Supports intake, differential focus during evaluation, and progress tracking.
    • Outputs: Structured 7-dimensional profile, clinician-readable rationale with transcript quotes, and a summary.
    • Tools/workflow: Record task → manual transcript or high-quality ASR (WhisperX) → LLM scoring template → EMR note paste-in or PDF.
    • Dependencies: HIPAA/PHI compliance (BAA with vendor or de-identification), clinician oversight, English transcripts, stable ASR quality; moderate test–retest reliability and construct clarity (especially for causal/mental-state constructs) must be communicated.
  • Telehealth cognitive check-ins for at-risk adults (Healthcare; Digital Health)
    • Remote, low-burden monitoring via smartphone or web: patients describe the picture, transcripts processed with the LLM to track within-person changes over time.
    • Outputs: Longitudinal score trends, alerts for significant deviations.
    • Tools/workflow: Patient-facing app + WhisperX → LLM scoring → trend dashboard for clinicians.
    • Dependencies: Consent and data governance, variable mic/audio quality, ASR quality affects accuracy; not a standalone diagnostic.
  • Clinical documentation assistant for SLPs and neuropsychologists (Healthcare; Software)
    • Auto-generate defensible notes that summarize deficits and attach quoted evidence, reducing documentation time and increasing consistency across providers.
    • Outputs: Structured note sections aligned to the seven constructs; export to EMR.
    • Dependencies: Integration with EMR systems, accuracy review by clinicians, prompt adherence to construct scope.
  • Research feature engineering for cognitive-linguistic studies (Academia)
    • Use LLM-derived construct scores as interpretable, low-dimensional features for statistical analyses and ML classification (e.g., ADRESS accuracy of 85%).
    • Outputs: Replicable features for group comparisons, correlation with MMSE/RAVLT, and SHAP-informed insights.
    • Tools/workflow: Batch transcript processing (manual or ASR) → score matrix → modeling pipelines.
    • Dependencies: Dataset shift across cohorts (education/age/language) needs covariate control; ensure transcript quality comparability.
  • Lightweight proxy model for PHI-constrained settings (Healthcare; Software)
    • Train a logistic regression on BERT embeddings to predict construct scores (teacher: advanced LLM outputs), enabling local inference when cloud LLMs are restricted.
    • Outputs: On-prem severity scores with acceptable performance on some constructs.
    • Tools/workflow: Local BERT embedding service → LR inference → reporting.
    • Dependencies: Lower performance than advanced LLMs; requires internal validation and periodic recalibration.
  • Educational aid for SLP training and calibration (Education; Healthcare)
    • Use LLM explanations and quoted evidence to illustrate construct manifestations; supports rubric training and inter-rater calibration exercises.
    • Outputs: Annotated exemplars, practice cases, and calibration reports.
    • Dependencies: Clear construct definitions; manage LLM’s low tolerance for informal speech to avoid over-penalization.
  • ASR quality triage for clinical audio (Software; Healthcare)
    • Use performance drop patterns and pause tagging to flag low-quality recordings and recommend re-capture or manual transcription.
    • Outputs: ASR quality flags, expected confidence bands for construct scores.
    • Dependencies: Audio quality variability; standard operating procedures for re-recording.
  • Screening enrichment for clinical research and trials (Healthcare; Pharma/Clinical Trials)
    • Use the seven scores to enrich recruitment (e.g., identify mild impairment), stratify participants, and baseline profile cognitive-linguistic domains.
    • Outputs: Screening reports; domain-level summaries.
    • Dependencies: IRB approvals, alignment to inclusion/exclusion criteria; ensure interpretability and standardized administration.
  • Community memory-clinic intake workflow (Healthcare; Public Health)
    • Add a 5–10 minute picture-description task during intake to prioritize patients for full neuropsych testing.
    • Outputs: Quick risk flag and domain-specific pointers.
    • Dependencies: Staff training, informed consent, local data policies; use as decision support, not a diagnosis.
  • Prototype SaaS/API for picture-description analysis (Software)
    • Offer a standardized API implementing the prompt and template for developers building cognitive assessment apps.
    • Outputs: JSON with seven scores, explanations, and summaries.
    • Dependencies: Compliance (HIPAA/GDPR), rate limits/costs for LLM calls; service-level agreements for latency/uptime.

Long-Term Applications

  • FDA-cleared digital biomarker for cognitive impairment (Healthcare; Regulatory/Policy)
    • Validate, calibrate, and standardize the seven-domain score set as a regulated digital biomarker for screening and longitudinal monitoring.
    • Outputs: Normative databases (age/education/language), cutoffs, clinical decision support rules.
    • Dependencies: Large, multi-site, multi-language trials; reliability, fairness audits; post-market surveillance; reimbursement pathways.
  • On-device, privacy-preserving assessment suite (Healthcare; Software; Edge AI)
    • Develop high-performing local models (larger open-source LLMs + fine-tuned ASR) to enable PHI-safe deployments without cloud dependencies.
    • Outputs: Comparable accuracy to cloud LLMs; low-latency edge inference.
    • Dependencies: Model compression/distillation, hardware constraints, continual updates; evidence of parity with cloud performance.
  • Multimodal cognitive assessment (Healthcare; Neuroscience; Robotics)
    • Combine audio, transcripts, video gaze/gesture, and eye-tracking to improve “Saliency,” “Causal/temporal relations,” and “General cognition” scoring.
    • Outputs: Richer, more reliable construct measures; better sensitivity/specificity.
    • Dependencies: Sensor integration, user consent; increased complexity of deployment and interpretation.
  • Generalized, task-agnostic cognitive-linguistic profiling (Healthcare; Education; Software)
    • Extend beyond Cookie Theft to story retell, conversational agents, and daily conversations; build a “Cognitive Communication Assessment Suite.”
    • Outputs: Cross-task profiles with calibrated mappings across tasks and domains.
    • Dependencies: Task harmonization, construct redefinition; additional few-shot exemplars; careful bias controls.
  • Passive at-home monitoring via ambient devices (Daily Life; Consumer Health; Smart Home)
    • Detect longitudinal changes in connected speech during routine interactions with voice assistants; surface trends to users and clinicians with consent.
    • Outputs: Periodic summaries and change alerts.
    • Dependencies: Privacy and opt-in mechanisms, false-positive mitigation, household speaker diarization, robust ASR in noisy environments.
  • Policy standards for interpretable AI in cognitive assessment (Policy; Standards Bodies)
    • Codify requirements for explanation quality, reliability thresholds (ICC/WSCV), fairness metrics, and data governance for LLM-based cognitive tools.
    • Outputs: Best-practice guidelines; certification frameworks; procurement criteria for public health programs.
    • Dependencies: Stakeholder consensus, evidence synthesis, alignment with medical device regulations.
  • Reimbursement and clinical workflow integration (Healthcare; Payers/Providers)
    • Create CPT/reimbursement pathways for digital speech biomarkers and set referral workflows in primary care (e.g., automatic neurology referrals above risk thresholds).
    • Outputs: Billing codes, clinical pathways, referral dashboards.
    • Dependencies: Clinical utility studies, health economics, payer engagement.
  • Bias and fairness remediation across dialects/languages and education levels (Healthcare; Academia)
    • Systematically evaluate and correct score biases to prevent over-pathologizing informal or sociolectal speech and to ensure equitable performance.
    • Outputs: Adjusted scoring models, fairness reports, calibration tools.
    • Dependencies: Diverse multilingual corpora; collaboration with sociolinguists and communities.
  • Enhanced construct definitions and scoring rubrics (Academia; Healthcare)
    • Refine “Mental state language” and “Causal/temporal relations” (currently less discriminative) and clarify “Saliency” and “General cognition” scopes; develop adjudicated ground truth with larger SLP panels.
    • Outputs: Revised prompts, improved few-shot exemplars, higher inter-rater reliability.
    • Dependencies: Expert consensus processes; iterative human-in-the-loop evaluations.
  • Decentralized and hybrid clinical trials with speech endpoints (Pharma/Clinical Trials)
    • Use construct scores as remote endpoints for efficacy monitoring in MCI/AD trials; reduce participant burden and increase frequency of assessments.
    • Outputs: Trial datasets with validated digital endpoints; real-time dashboards.
    • Dependencies: Regulatory acceptance of digital endpoints; site training; device standardization.
  • EHR-integrated decision support and population health analytics (Healthcare; Data Platforms)
    • Embed scores and trajectories into EHRs with risk stratification, panel management, and population screening analytics in primary care networks.
    • Outputs: Risk flags, recommended actions, registry dashboards.
    • Dependencies: Interoperability (FHIR), governance, change management for clinicians.
  • Caregiver-facing coaching and support tools (Daily Life; Consumer Health)
    • Provide simplified summaries and suggestions for communication strategies based on specific deficits (e.g., cohesion or syntax).
    • Outputs: Personalized caregiver tips and progress visualizations.
    • Dependencies: Clear disclaimers, accessibility, safeguarding against medical misinterpretation; usability studies.
  • Workforce training simulators for SLPs and clinicians (Education; Simulation Software)
    • Build OSCE-like modules where learners score transcripts, compare to LLM+expert consensus, and receive targeted feedback per construct.
    • Outputs: Training modules, performance analytics.
    • Dependencies: Curricular alignment; availability of expert-adjudicated gold standards.
  • Insurance adjudication support for therapy planning (Finance; Healthcare)
    • Use standardized, interpretable constructs to justify therapy focus areas and track improvements.
    • Outputs: Structured reports aligning deficits to therapy plans and outcomes.
    • Dependencies: Regulatory and payer acceptance; safeguards against misuse; robust reliability and validity evidence.

Notes on common assumptions/dependencies across applications:

  • Data quality: ASR and recording quality materially impact some constructs; manual transcripts outperform ASR in older datasets.
  • Model choice: Advanced proprietary LLMs currently outperform small open-source models; on-device parity requires further R&D.
  • Generalizability: Norms must be established across age, education, dialects, and languages; current evidence is primarily English and specific cohorts.
  • Role of clinicians: Tools are decision support, not diagnostic; clinician oversight is required.
  • Privacy/regulatory: PHI handling, HIPAA/GDPR compliance, and, for medical use claims, regulatory clearances are prerequisites.
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Glossary

  • ADRESS dataset: A balanced subset of DementiaBank used for benchmarking dementia classification. "ADRESS is a balanced subset of the DementiaBank dataset [34], with 156 participants (78 with Alzheimer's disease, 78 controls)."
  • ASR (automatic speech recognition): Technology that converts spoken audio into text. "automatic speech recognition (ASR)-generated transcripts served as another input source for the LLMs."
  • BERT (Bidirectional Encoder Representations from Transformers): A transformer-based LLM used to produce text embeddings. "using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model [41]"
  • Bonferroni correction: A multiple-comparisons adjustment that controls family-wise error by tightening significance thresholds. "Bonferroni correction was used for multiple test correc- tions."
  • Boston Diagnostic Aphasia Examination: A standardized assessment that includes the Cookie Theft picture description task. "All participants completed the standard Cookie Theft picture description task from the Boston Diagnostic Aphasia Examination [33]."
  • Causal and temporal relations: A clinical-linguistic construct assessing the logical sequence and cause–effect description of events. "These include: 1) Saliency of information; 2) Se- mantic categories; 3) Referential cohesion; 4) Causal and tem- poral relations; 5) Mental state language; 6) Structural language and speech; and 7) General cognition and perception."
  • Cookie Theft picture description task: A widely used picture-based speech task to assess cognitive-linguistic abilities. "We focus on the widely used Cookie Theft picture description task [33]"
  • DementiaBank: A public corpus of speech and language data for dementia research. "Throughout the paper, the Pitt corpus from DementiaBank is referred to simply as DementiaBank for brevity."
  • Few-shot learning: An approach where models learn a task from a small number of examples. "Finally, five examples are included in the prompt to support few-shot learning by the LLMs."
  • General cognition and perception: A construct evaluating overall cognitive functioning as reflected in how the picture is perceived, organized, and described. "These include: 1) Saliency of information; 2) Se- mantic categories; 3) Referential cohesion; 4) Causal and tem- poral relations; 5) Mental state language; 6) Structural language and speech; and 7) General cognition and perception."
  • Hedge's g: An effect size measure for differences between group means. "Welch's t-test and Hedge's g were then used to evaluate the abil- ity of the scores to differentiate between Clinical and Control groups,"
  • Intraclass Correlation Coefficient (ICC): A reliability metric assessing agreement/consistency across repeated measurements. "Intraclass Correlation Coefficient (ICC) and Within-Subjects Coefficient of Variation (WSCV)."
  • Logistic regression: A statistical classifier modeling the probability of categorical outcomes. "Another adaptation strategy was training a logistic regres- sion model on DementiaBank to predict the severity scores."
  • Mental state language: A construct capturing references to characters’ thoughts, intentions, and emotions. "These include: 1) Saliency of information; 2) Se- mantic categories; 3) Referential cohesion; 4) Causal and tem- poral relations; 5) Mental state language; 6) Structural language and speech; and 7) General cognition and perception."
  • Mini-Mental State Examination (MMSE): A widely used cognitive screening test with scores from 0 to 30. "Mini- Mental State Examination (MMSE) scores [36]"
  • Nested cross-validation: A model evaluation strategy with inner loops for hyperparameter tuning and outer loops for performance estimation. "we adopted a nested cross-validation strategy with majority voting for model de- velopment."
  • Pearson correlation coefficient (PCC): A statistic measuring linear correlation between two variables. "Pearson correlation coefficient (PCC) was computed to evaluate alignment with existing cognitive scores (e.g. MMSE and RAVLT total)."
  • Protected health information (PHI): Individually identifiable health data subject to privacy regulations. "As W-ADRC contains pro- tected health information (PHI), this analysis is conducted on DementiaBank"
  • QLoRA: A parameter-efficient fine-tuning method using quantized low-rank adapters. "using QLoRA [40] by training a 4-bit adapter."
  • Rey Auditory Verbal Learning Test (RAVLT): A neuropsychological test of verbal learning and memory. "Rey Auditory Verbal Learning Test [38] total scores (RAVLT total), which measure learning and memory abilities, are also provided."
  • Referential cohesion: A construct evaluating clarity and consistency of pronoun and referent usage in discourse. "These include: 1) Saliency of information; 2) Se- mantic categories; 3) Referential cohesion; 4) Causal and tem- poral relations; 5) Mental state language; 6) Structural language and speech; and 7) General cognition and perception."
  • Saliency of information: A construct assessing how well key elements of the picture are prioritized and described. "These include: 1) Saliency of information; 2) Se- mantic categories; 3) Referential cohesion; 4) Causal and tem- poral relations; 5) Mental state language; 6) Structural language and speech; and 7) General cognition and perception."
  • Semantic categories: A construct concerning the specificity and appropriateness of vocabulary used (e.g., “boy” vs. “child”). "These include: 1) Saliency of information; 2) Se- mantic categories; 3) Referential cohesion; 4) Causal and tem- poral relations; 5) Mental state language; 6) Structural language and speech; and 7) General cognition and perception."
  • SHAP (SHapley Additive exPlanations): A game-theoretic method to explain model predictions by attributing feature contributions. "SHapley Additive exPla- nations (SHAP) [44] were used to quantify the contribution of each clinical construct to the model's predictions."
  • Speaker diarization: The process of segmenting audio by speaker identity over time. "WhisperX provides faster transcription with accurate speaker diarization and word-level timestamps."
  • Structural language and speech: A construct covering accuracy and complexity in phonology, syntax, and semantics. "These include: 1) Saliency of information; 2) Se- mantic categories; 3) Referential cohesion; 4) Causal and tem- poral relations; 5) Mental state language; 6) Structural language and speech; and 7) General cognition and perception."
  • Teacher forcing: A training technique for sequence models where ground-truth tokens are fed during decoding. "Teacher forcing was applied during training."
  • Tree SHAP: A SHAP variant providing exact feature attributions for tree-based models. "To be specific, Tree SHAP was applied to each of the ten fine-tuned XGBoost models,"
  • Welch's t-test: A two-sample t-test that does not assume equal variances between groups. "Welch's t-test and Hedge's g were then used to evaluate the abil- ity of the scores to differentiate between Clinical and Control groups,"
  • WhisperX: An ASR system that offers faster transcription with diarization and word-level timing. "we used WhisperX [37] to automatically transcribe the speech recordings."
  • Within-Subjects Coefficient of Variation (WSCV): A metric quantifying variability of repeated measures within the same subject. "Higher ICC and lower WSCV indicate better reliability."
  • Word-level timestamps: Time alignments for each recognized word in ASR output. "WhisperX provides faster transcription with accurate speaker diarization and word-level timestamps."
  • XGBoost (Extreme Gradient Boosting): A scalable, regularized gradient boosting framework for classification and regression. "an extreme Gradient Boosting (XGBoost) model [42] was trained to classify demen- tia patients and healthy controls in the ADRESS [43] dataset."
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