Sep 2026· Journal of Laboratory Medicine· 5 references
Abstract
Abstract Laboratory medicine has witnessed three generational paradigm shifts over the past 75 years – from manual work to early automation, from batch processing to patient-oriented selective analysis, and from human perianalytics to Total Laboratory Automation (TLA). Drawing on a personal trajectory spanning half a century of the discipline, this essay argues that a fourth generation, called “Laboratory 4.0”, is not a distant utopia but an expected reality, just like the so-called “Industry 4.0,” which is emerging in other areas of technological development. Three converging forces are driving this transformation: miniaturization (microfluidics, lab-on-a-chip platforms, and painless capillary sampling), decentralization (pervasive diagnostics available at any point and time without a central laboratory as intermediary), and artificial intelligence (from classical decision-support systems to generative large language models). A hybrid model is proposed in which incremental modernization within existing structures (until ∼2035) gives way to genuine decentralization and democratization of diagnostic services (∼2035 to 2045), followed by a fundamental redefinition of the discipline’s role in society from 2045 onwards. Underlying these technical developments is a new epistemic responsibility: as AI transforms raw data into medical knowledge autonomously, laboratory professionals must claim their irreplaceable role as the sole legally and professionally qualified institution responsible for translating laboratory data into medical insight. The future of laboratory medicine is not a swan song, it is an invitation to actively shape the laboratory 4.0.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
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 work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
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
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
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