Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave signals map to clinically relevant biomarkers remains lacking. This survey presents a full-stack review of mmWave-based medical sensing systems, encompassing signal acquisition, physical feature extraction, modeling strategies, and potential medical and healthcare uses. We introduce a taxonomy that decouples low-level mmWave signal features—such as motion, material property, and structure—from high-level biomedical biomarkers, including respiration pattern, heart rate, tissue hydration, and gait. We then classify and contrast the modeling approaches—ranging from physics-driven analytical models to machine learning techniques—that enable this mapping. Furthermore, we analyze representative studies across vital signs monitoring, cardiovascular assessment, wound evaluation, and neuro-motor disorders. By bridging wireless sensing and medical interpretation, this work offers a structured reference for designing next-generation mmWave health monitoring systems. We conclude by discussing open challenges, including model interpretability, clinical validation, and multimodal integration.

Developmental language disorder affects approximately 7-10% of children in the United States and is associated with long-term difficulties in literacy, academic achievement, and social participation. Language sample analysis (LSA) is widely regarded as a gold-standard approach for evaluating children's language development; yet, it remains underused in clinical and research practice because it depends on labor-intensive and time-consuming manual transcription and coding. Therefore, we propose SATE, a scalable and automated system for child language assessment. It implements a modular machine learning pipeline that reproduces the full LSA workflow, including verbatim transcription, utterance segmentation, speaker identification, maze annotation, lexical and grammatical analyses, and language metrics computation. The pipeline is complemented by a user interface that supports efficient review and correction of model output, allowing researchers and clinicians to validate and refine automated results. Experimental evaluation on child speech corpora demonstrates that SATE substantially outperforms existing baselines, achieving 11.81% word error rate and strong correlations with authenticated results of language metrics. A counterbalanced human-in-the-loop study showed that SATE-assisted LSA reduced task completion time by 55.3% while maintaining comparable accuracy to manual LSA, and usability questionnaires yielded a mean SUS score of 81.25. These results provide evidence that SATE is a practical tool for child language sample analysis.