Non-invasive automatic screening for Alzheimer's disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer's disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist's first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.

This study presents an automated deep learning approach to evaluate the oral diadochokinesis, a widely used clinical tool for assessing syllable repetition speed in motor speech disorders. Addressing the limitations of manual assessments—including subjectivity, time constraints, and inter-rater variability—we developed a system leveraging the Wav2Vec2 speech recognition model, combined with audio preprocessing (resampling, mono conversion, and normalisation) and temporal alignment techniques for syllable detection. In an initial assessment of the developed method, the system was evaluated on 16 recordings from two healthy speakers, analysed by three speech and language pathologists (SLPs) and compared to ground truth measurements. Results demonstrated superior accuracy of the machine learning system, with a mean squared error (MSE) of 0.07, compared to 1.18 for human raters. Statistical analysis (Wilcoxon signed-rank test: p = 0.98 for model vs. p = 0.00043 for SLPs) confirmed the model’s alignment with ground truth. While the system occasionally missed syllables (1–2 per recording), its precision in calculating syllables per second (SPS) and temporal consistency highlights its potential as a supplementary clinical tool. Key innovations include a user-friendly offline interface for data security and visualisations (Mel spectrograms, timing evenness, and distinctness metrics) to support clinical interpretation. The present study is subject to certain limitations. The study’s methodology is constrained by a small and homogeneous sample. Separately, the developed system’s performance is limited by unresolved challenges in the detection of subtle articulation errors. Future work will expand validation to diverse populations, including speakers with dysarthria, and refine human-in-the-loop integration to mitigate missed syllables. This study underscores the feasibility of combining deep learning with signal processing to enhance objectivity in speech assessments, offering a scalable solution to standardise the oral diadochokinesis test while preserving clinical expertise.

The performance of Automatic Speech Recognition (ASR)systems has constantly increased in state-of-the-art develop-ment. However, performance tends to decrease considerably inmore challenging conditions (e.g., background noise, multiplespeaker social conversations) and with more atypical speakers(e.g., children, non-native speakers or people with speech dis-orders), which signifies that general improvements do not nec-essarily transfer to applications that rely on ASR, e.g., educa-tional software for younger students or language learners. Inthis study, we focus on the gap in performance between recog-nition results for native and non-native, read and spontaneous,Swedish utterances transcribed by different ASR services. Wecompare the recognition results using Word Error Rate and an-alyze the linguistic factors that may generate the observed tran-scription errors.

As part of the PSST challenge, we explore how data augmentations, data sources, and model size affect phoneme transcription accuracy on speech produced by individuals with aphasia. We evaluate model performance in terms of feature error rate (FER) and phoneme error rate (PER). We find that data augmentations techniques, such as pitch shift, improve model performance. Additionally, increasing the size of the model decreases FER and PER. Our experiments also show that adding manually-transcribed speech from non-aphasic speakers (TIMIT) improves performance when Room Impulse Response is used to augment the data. The best performing model combines aphasic and non-aphasic data and has a 21.0% PER and a 9.2% FER, a relative improvement of 9.8% compared to the baseline model on the primary outcome measurement. We show that data augmentation, larger model size, and additional non-aphasic data sources can be helpful in improving automatic phoneme recognition models for people with aphasia.

Introduction: We present the Swedish Medical LLM Benchmark (SMLB), an evaluation framework for assessing large language models (LLMs) in the Swedish medical domain.

Method: The SMLB addresses the lack of language-specific, clinically relevant benchmarks by incorporating four datasets: translated PubMedQA questions, Swedish Medical Exams, Emergency Medicine scenarios, and General Medicine cases.

Result: Our evaluation of 18 state-of-the-art LLMs reveals GPT-4-turbo, Claude- 3.5 (October 2023), and the o3model as top performers, demonstrating a strong alignment between medical reasoning and general language understanding capabilities. Hybrid systems incorporating retrieval-augmented generation (RAG) improved accuracy for clinical knowledge questions, highlighting promising directions for safe implementation.

Discussion: The SMLB provides not only an evaluation tool but also reveals fundamental insights about LLM capabilities and limitations in Swedish healthcare applications, including significant performance variations between models. By open-sourcing the benchmark, we enable transparent assessment of medical LLMs while promoting responsible development through community-driven refinement. This study emphasizes the critical need for rigorous evaluation frameworks as LLMs become increasingly integrated into clinical workflows, particularly in non-English medical contexts where linguistic and cultural specificity are paramount.

Introduction The integration of large language models (LLMs) into healthcare holds immense promise, but also raises critical challenges, particularly regarding the interpretability and reliability of their reasoning processes. While models like DeepSeek R1-which incorporates explicit reasoning steps-show promise in enhancing performance and explainability, their alignment with domain-specific expert reasoning remains understudied.Methods This paper evaluates the medical reasoning capabilities of DeepSeek R1, comparing its outputs to the reasoning patterns of medical domain experts.Results Through qualitative and quantitative analyses of 100 diverse clinical cases from the MedQA dataset, we demonstrate that DeepSeek R1 achieves 93% diagnostic accuracy and shows patterns of medical reasoning. Analysis of the seven error cases revealed several recurring errors: anchoring bias, difficulty integrating conflicting data, limited consideration of alternative diagnoses, overthinking, incomplete knowledge, and prioritizing definitive treatment over crucial intermediate steps.Discussion These findings highlight areas for improvement in LLM reasoning for medical applications. Notably the length of reasoning was important with longer responses having a higher probability for error. The marked disparity in reasoning length suggests that extended explanations may signal uncertainty or reflect attempts to rationalize incorrect conclusions. Shorter responses (e.g., under 5,000 characters) were strongly associated with accuracy, providing a practical threshold for assessing confidence in model-generated answers. Beyond observed reasoning errors, the LLM demonstrated sound clinical judgment by systematically evaluating patient information, forming a differential diagnosis, and selecting appropriate treatment based on established guidelines, drug efficacy, resistance patterns, and patient-specific factors. This ability to integrate complex information and apply clinical knowledge highlights the potential of LLMs for supporting medical decision-making through artificial medical reasoning.

Introduction: The integration of large language models (LLMs) into personal health management presents transformative potential, but faces critical challenges in user experience (UX) design, ethical implementation, and clinical integration.

Method: This paper introduces a novel AI-driven journaling application, a functional prototype available open source, designed to encourage patient engagement through a natural language interface. This approach, termed “AI-assisted health journaling,” enables users to document health experiences in their own words while receiving real-time, context-aware feedback from an LLM. The prototype combines a personal health record with an LLM assistant, allowing for reflective self-monitoring and aiming to combine patient-generated data with clinical insights. Key innovations include a three-panel interface for seamless journaling, AI dialogue, and longitudinal tracking, alongside specialized modes for interacting with simulated healthcare expert personas.

Result: Preliminary insights from persona-based evaluations highlight the system's capacity to enhance health literacy through explainable AI responses while maintaining strict data localization and privacy controls. We propose five design principles for patient-centric AI health tools: (1) decoupling core functionality from LLM dependencies, (2) layered transparency in AI outputs, (3) adaptive consent for data sharing, (4) clinician-facing data summarization, and (5) compliance-first architecture.

Discussion: By transforming unstructured patient narratives into structured insights through natural language processing, this approach demonstrates how journaling interfaces could serve as a critical middleware layer in healthcare ecosystems-empowering patients as active partners in care while preserving clinical oversight. Future research directions emphasize the need for rigorous trials evaluating impacts on care continuity, patient-provider communication, and long-term health outcomes across diverse populations.

The integration of large language models (LLMs) into health care presents significant risks to patients and clinicians, inadequately addressed by current guidance. This paper adapts harm reduction principles from public health to medical LLMs, proposing a structured framework for mitigating these domain-specific risks while maximizing ethical utility. We outline tailored strategies for patients, emphasizing critical health literacy and output verification, and for clinicians, enforcing “human-in-the-loop” validation and bias-aware workflows. Key innovations include developing thoughtful use protocols that position LLMs as assistive tools requiring mandatory verification, establishing actionable institutional policies with risk-stratified deployment guidelines and patient disclaimers, and critically analyzing underaddressed regulatory, equity, and safety challenges. This research moves beyond theory to offer a practical roadmap, enabling stakeholders to ethically harness LLMs, balance innovation with accountability, and preserve core medical values: patient safety, equity, and trust in high-stakes health care settings.