Sep 2026· Supportive Care in Cancer· Vol 34· 40 references
Cancer survivorship and care
Abstract
Abstract Background Young adult cancer survivors (ages 18–39) often experience persistent treatment-related symptoms that negatively affect daily functioning, psychosocial well-being, and quality of life, yet report unmet educational needs for symptom management. Generative artificial intelligence (AI) technologies, such as large language model-powered chatbots, offer scalable, personalized, and empathetic symptom support but remain largely untested in young adults’ survivorship care. Objective This qualitative descriptive study explored young adult cancer survivors’ perspectives, preferences, and experiences related to the use of generative AI chatbots for oncology symptom management. Methods Eighteen post-treatment young adult cancer survivors who had completed cancer treatment within the past two years were recruited from the University of Michigan Rogel Cancer Center. Participants completed the PROMIS-29, PROMIS Self-Efficacy for Managing Symptoms, and the Digital Health Literacy Scale to quantify symptom burden, self-efficacy, and digital readiness. Semi-structured interviews explored experiences with cancer treatment-related symptoms and AI use, including preferences for personalization, tone, functionality, and data governance. Results Qualitative analyses revealed three themes: (1) the need for personalized, emotionally supportive, and credible AI symptom guidance; (2) accessible and user-centered design drives engagement with AI symptom support; and (3) trust, privacy, and human oversight as preconditions for AI symptom support. Survivors viewed AI as a supplemental resource to enhance symptom monitoring, decision support, and emotional reassurance without replacing clinical care. Conclusion Generative AI chatbots were perceived as acceptable and potentially valuable for AYA symptom management. Findings can guide the development of tailored digital self-management interventions responsive to the complex and evolving needs of this population.
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
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The findings show that speed related agile practices are used to a greater extent in comparison to quality practices, and that software startups who adopt the Lean Startup approach do not sacrifice quality for speed more than other startups do.
Jevgenija Pantiuchina, Marco Mondini, Dron Khanna et al.· International Conference on...· 84 citations· ⚡4
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Microsoft Research Blog· microsoft.comOct 22, 2026
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.