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.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
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
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
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