ReviewTopics in current chemistry (Cham)2026
AI and Robotics Advancement in Analytical Mineral Characterization and Mining Processes: A Review and Research Trends Analysis.
Review in Topics in current chemistry (Cham), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
The mining sector is undergoing a major transformation, as it moves shifting from traditional, labor-intensive methods to adopting digital technologies within the framework of Industry 4.0. Machine learning (ML), artificial intelligence (AI), and robotics are emerging as key innovative tools to improve safety, operational efficiency, and sustainability across the entire mining value-chain, from exploration and mineral processing to mineral characterization and environmental management. The integration of AI and ML with spectroscopic techniques has revolutionized the mining industry by enhancing efficiency, accuracy, throughput, and operational performance. This review discusses recent advances in AI, ML, and robotics applications in mining processes and mineral characterization. It explores the influence and highlights the integration of ML tools such as ANN, PCA, k-NN, and SVM with advanced analytical chemistry techniques, including XRF, XRD, SEM-EDX, LIBS, ICP-OES, ICP-MS, LA-ICP-MS, and HSI for mineral identification. Additionally, a bibliometric analysis using Scopus publications over the past 20 years provides insights into research trends and hotspots, providing recent insights into publication patterns and research. The review further offers an overview of recent technological developments, economic benefits, policy implication changes, and future directions, while emphasizing gaps related to the standardization of prospects for mining, demonstrating substantial growth in the integration of AI-driven analytical technologies within the analytical chemistry characterization of minerals, while also highlighting gaps related to the standardization of technologies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.