Artificial intelligence (AI) has been transforming many aspects of medical care. In prostate cancer, ongoing progress in AI has improved research and patient care. Recent advances in machine learning and deep learning have produced tools that help diagnose cancer, assess risk, and predict outcomes. In screening, AI-based risk calculators improve detection and help avoid unnecessary biopsies. Deep learning algorithms, particularly convolutional neural networks, have demonstrated expert-level performance in pathology, identifying malignancy and assigning Gleason grades with high accuracy. These tools also streamline workflow, flagging challenging cases for review and quantifying prognostic markers, such as Ki-67 and cribriform patterns. In addition, AI-based models can predict molecular alterations, microsatellite instability, and lymph node metastasis directly from histology images, providing cost-effective alternatives to traditional assays. The development of multimodal models integrates digital pathology and clinical parameters, enabling personalized treatment recommendations and improved outcome prediction. Natural language processing and large language models further expand AI's potential, facilitating information extraction from clinical notes and enhancing patient education. Despite these advances, most studies remain retrospective with heterogeneous endpoints. Performance often drops when models are tested at new sites because of differences in patient populations and slide preparation. Access to large, well-annotated datasets is limited, and technical variation hampers reproducibility. To move toward clinical use, the field needs prospective, multicenter validation, preanalytical and analytical standardization, and clear reporting of failure modes and human oversight. Emerging approaches, including self-supervised pretraining, transformer-based image models, and language-vision systems, are likely to improve generalization and support more personalized care.
Software startups are newly created companies with no operating history and oriented towards producing cutting-edge products. However, despite the increasing importance of startups in the economy, few scientific studies attempt to address software engineering issues, especially for early-stage startups. If anything, startups need engineering practices of the same level or better than those of larger companies, as their time and resources are more scarce, and one failed project can put them out of business. In this study we aim to improve understanding of the software development strategies employed by startups. We performed this state-of-practice investigation using a grounded theory approach. We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible. This strategy allows startups to verify product and market fit, and to adjust the product trajectory according to early collected user feedback. The need to shorten time-to-market, by speeding up the development through low-precision engineering activities, is counterbalanced by the need to restructure the product before targeting further growth. The resulting implications of the GSM outline challenges and gaps, pointing out opportunities for future research to develop and validate engineering practices in the startup context.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 179 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models. Software startup ...
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
In the context of software startups, project failure is embraced actively and considered crucial to obtain validated learning that can lead to pivots. A pivot is the strategic change of a business concept, product or the different elements of a business model. A better understanding is needed on different types of pivots and different factors that lead to failures and trigger pivots, for software entrepreneurial teams to make better decisions under chaotic and unpredictable environment. Due to the nascent nature of the topic, the existing research and knowledge on the pivots of software startups are very limited. In this study, we aimed at identifying the major types of pivots that software startups make during their startup processes, and highlighting the factors that fail software projects and trigger pivots. To achieve this, we conducted a case survey study based on the secondary data of the major pivots happened in 49 software startups. 10 pivot types and 14 triggering factors were identified. The findings show that customer need pivot is the most common among all pivot types. Together with customer segment pivot, they are common market related pivots. The major product related pivots are zoom-in and technology pivots. Several new pivot types were identified, including market zoom-in, complete and side project pivots. Our study also demonstrates that negative customer reaction and flawed business model are the most common factors that trigger pivots in software startups. Our study extends the research knowledge on software startup pivot types and pivot triggering factors. Meanwhile it provides practical knowledge to software startups, which they can utilize to guide their effective decisions on pivoting.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study 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
We present our ongoing work building a Raspberry Pi cluster consisting of 300 nodes. The unique characteristics of this single board computer pose several challenges, but also offer a number of interesting opportunities. On the one hand, a single Raspberry Pi can be purchased cheaply and has a low power consumption, which makes it possible to create an affordable and energy-efficient cluster. On the other hand, it lacks in computing power, which makes it difficult to run computationally intensive software on it. Nevertheless, by combining a large number of Raspberries into a cluster, this drawback can be (partially) offset. Here we report on the first important steps of creating our cluster: how to set up and configure the hardware and the system software, and how to monitor and maintain the system. We also discuss potential use cases for our cluster, the two most important 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
Software startups operate under various uncertainties and the demand on their ability to deal with change is high. Agile methods are considered a suitable and viable development approach for them. However, the competing needs for speed and quality may render certain agile practices less suitable than others in the startup context. The adoption of agile practices can be further complicated in software startups that adopt the Lean Startup approach. To make the best of agile practices, it is necessary to first understand whether and how they are used in software startups. This study targets at a better understanding of the use of agile practices in software startups, with a particular focus on lean startups. Based on a large survey of 1526 software startups, we examined the use of five agile practices, including quality related (regular refactoring and test first), speed related (frequent release and agile planning) and communication practice (daily standup meeting). The findings show that speed related agile practices are used to a greater extent in comparison to quality practices. Daily standup meeting is least used. 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
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
