Speech production integrates respiratory, laryngeal, articulatory, prosodic, linguistic and executive control, so neurodegeneration can leave measurable acoustic traces before conventional clinical scales change. Two decades of work have identified credible candidate speech and voice biomarkers for amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD), yet the field remains fragmented into small, single-condition, single-language models, many of which do not reproduce when evaluated on speakers entirely unseen during training (all recordings from each participant are confined to one data partition, preventing identity leakage between training and testing). In this Perspective we argue that a testable next step is a clinically grounded voice-biomarker foundation model (a single self-supervised backbone, pretrained on large, diverse, ethically sourced, multi-condition speech and adapted to explicit contexts of use), rather than further bespoke classifiers. We propose ALS bulbar-progression monitoring as a suitable lead context of use because some speech-derived measures appear more responsive than the coarse ALSFRS-R speech item; one ALS speech-analytics platform has received Breakthrough Device designation, an expedited-review status that is neither marketing authorization nor endpoint qualification. We use PD screening as a test case carrying the field’s central cautionary lessons about data leakage, modest real-world operating points, and the potential value of articulation-rich over phonation-only tasks. We separate established evidence from inference and proposed research, define what would justify the “foundation model” label, specify minimum methodological and governance standards, state the limitations candidly, and outline a prospective-validation and regulatory roadmap. To our knowledge, no speech- or voice-derived endpoint for ALS or PD was qualified by FDA or EMA as of the time of this publication; the foundation-model case is therefore presented as a research agenda, not an achieved capability.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.