Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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ACS sensors
Artificial Raman Expert: Autonomous Spectroscopic Reasoning Driven by Localized Large Language Models
Analytical sciences increasingly require autonomous systems capable of real-time heuristic reasoning and dynamic parameter adaptation. However, current automated laboratory platforms remain largely restricted to rigid synthesis workflows. Here, we introduce the Artificial Raman Expert (ARE), an autonomous AI-driven analytical framework powered by localized, open-source large language models (LLMs) to ensure strict data privacy and reproducibility. By integrating a structured knowledge base (A-Knowhow), ARE internalizes the tacit decision-making logic of seasoned spectroscopists. To ensure safe and scalable deployment, ARE's cognitive and optical reasoning was first validated within a risk-free virtual reality (VR) sandbox before being successfully translated to a physical Raman spectrometer. Across highly complex physicochemical scenarios, ARE demonstrated advanced cognitive capabilities: evaluating the spatial heterogeneity of pharmaceutical samples, performing spectral unmixing to isolate trace narcotics from severe forensic matrix interferences within just 3 analytical cycles, heuristically halting analyses after 8-10 iterations-despite human prompts mandating >30 positions-thereby reducing analytical time and resource consumption by ∼70%. Additionally, ARE conducted constraint-aware parameter tuning, dynamically capping laser exposure at 60 s to optimize signal-to-noise ratios while strictly preventing biological cell phototoxicity. By actively optimizing analytical resources and adapting to noisy data, ARE demonstrates the feasibility of context-aware autonomous spectroscopy. This work provides a foundational proof-of-concept for encoding tacit analytical expertise into AI-driven instrumentation, with potential for broader application across diverse analytical domains.
28 June 2026
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Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)
Acute leukemias (ALs) are a diverse group of hematological malignancies characterized by the abnormal proliferation of immature cells. Microscopic observation of cell morphology based on the French‑American‑British classification remains a fundamental diagnostic method for ALs. However, manual screening from bone marrow smear images is often inefficient, laborious and prone to subjective bias, leading to potential misdiagnosis or missed diagnosis. Artificial intelligence (AI), particularly machine learning (ML), has expanded human capabilities in analyzing complex datasets, leading to breakthroughs in multiple fields, including medical research and clinical practice. Increasingly, ML applications are being developed to diagnose hematological diseases by extracting and aggregating morphological characteristics from peripheral blood and bone marrow smears. However, applying ML methods to recognize cell morphology in hematological diseases presents unique challenges compared with other pathology subspecialties. The present review provided an overview of AI and ML applications in ALs diagnosis, focusing on cell segmentation and data mining methods from microscopy images, and highlights their advantages over manual microscopy.
18 Apr 2026
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