Papers for

digital health app developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

LLM enriched speech features improve early Alzheimer detection accuracy

LLM-Anchored Paralinguistic Enrichment for Alzheimer's Disease Detection

Abstract: Speech-based automatic detection of Alzheimer's disease (AD) provides a non-invasive and scalable approach to early cognitive screening. AD affects both lexical-semantic organization and speech production, including atypical pauses and word elongations. However, existing methods have yet to fully integrate these paralinguistic cues with linguistic content. We propose LLM-Anchored Paralinguistic Enrichment (LAPE), which enriches LLM-derived linguistic representations with paralinguistic cues through three coordinated innovations. The first is prosodic event textualization, which enables the LLM to model pauses and elongations jointly with lexical content by encoding them as explicit markers with bounded duration-aware repetition. The second is lexico-prosodic unitization and chunking, which preserves event identity and magnitude in both modalities by pooling only consecutive word units. The third is text-anchored paralinguistic fusion, which integrates local and utterance-level speech features by using NormGate to normalize and dynamically scale them relative to text. We evaluate LAPE on ADReSS and ADReSSo using participant-level cross-validation and leave-one-subject-out evaluation. LAPE achieves state-of-the-art performance across all four primary settings. Code will be released upon acceptance.

Wed 9 SeptComputation and LanguageSound
The gist
Detecting Alzheimer's disease early can be done by analyzing how people speak, since the disease changes both word choice and speech patterns like pauses. The researchers created a method called LAPE that combines language AI models with detailed speech cues, like pauses and stretched words, to get a better understanding. They tested their approach on standard Alzheimer speech datasets and found it outperforms previous tools. This way, speech analysis can become a more reliable, non-invasive way to screen for the disease early on.
Open → 2609.10896v1

Ai coaching requires layered human oversight for mental health safety

Scalable Oversight for AI in Mental Health: Lessons from 350,000 AI Coaching Conversations between Therapy Sessions

Abstract: Clinician review of every AI output is often proposed as a safeguard in mental healthcare, but vigilance research suggests this approach fails at scale and may paradoxically reduce safety. Drawing on our experience deploying an AI coaching tool across 350,000+ conversations between therapy sessions, we describe how we arrived at a three-layer human-on-the-loop oversight framework combining preventive design, real-time monitoring, and continuous clinician evaluation. We show how specific findings from clinical review drove iterative improvements, and offer practical recommendations for mental health professionals evaluating AI systems.

Tue 8 SeptComputers and Society
The gist
AI tools can help people between therapy sessions, but checking every AI message by clinicians is too hard and can actually lead to mistakes. The authors studied over 350,000 AI coaching chats and created a system with three layers: design that prevents problems, real-time checks, and ongoing doctor reviews. They improved the AI by learning from these reviews and give advice for mental health workers who use AI. This approach balances safety with scale better than just watching everything closely.
Open → 2609.09533v1