Clinical laboratory medicine faces converging structural pressures: workforce shortages, growing data complexity, and rapid advances in artificial intelligence (AI). Some prominent developers, including Demis Hassabis of Google DeepMind, have projected the arrival of artificial general intelligence (AGI) around 2030; such timelines remain contested and should be read as one plausible scenario rather than an established forecast. This Opinion Paper does not depend on any specific arrival date. Instead, it argues that the trajectory of increasingly capable AI is itself reason enough to reconsider how the clinical laboratory creates value. At the core of this reconsideration is a shift from "Clinical Lab 1.0" (reactive test confirmation) toward "Clinical Lab 2.0", a proactive, lab-initiated care loop in which the laboratory stratifies populations by risk, closes care gaps, prompts targeted intervention, and measures its impact on patient outcomes. We propose that increasingly capable AI can accelerate this loop, and we synthesize three converging capabilities - agentic reasoning for longitudinal biomarker surveillance, causal world modeling via "Virtual Cells", and medical digital twin integration - into a single conceptual roadmap. We reconceive laboratory professionals as "Diagnostic Data Scientists" who govern AI-assisted workflows spanning the full brain-to-brain loop, within interoperable infrastructures aligned with HL7 FHIR standards. We also consider the equity dimension and acknowledge substantial barriers to realizing this vision. Key proposals include a new generation of algorithmic quality indicators (aQIs) that extend established quality indicator frameworks to encompass continuous monitoring of AI model performance. The mechanisms described are aspirational and require empirical validation.
Yuji et al. (Sun,) studied this question.