Skip to content

Laboratory 4.0: on the path to the fourth generation of laboratory medicine

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.

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

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. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

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. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

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.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

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. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

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. · 157 citations · ⚡17

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.