ReviewInternational journal of analytical chemistry2026
The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.
Review in International journal of analytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.International journal of analytical chemistry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) plays a crucial role in modern analytical chemistry, offering solutions to long-standing challenges. Conventional techniques, such as spectrophotometric analysis and chromatography, often face issues like spectral overlap, matrix interference, and extensive experimental optimization. AI and machine learning (ML) approaches address these limitations by enabling spectral deconvolution, pattern recognition, prediction of retention factors, and automated optimization of separation conditions. Beyond enhancing traditional methods, AI supports the development of innovative analytical platforms. Modern analytical chemistry increasingly relies on smartphone- and paper-based sensors for on-site detection of biomarkers and pollutants. These portable, low-cost systems generate complex datasets requiring advanced computational tools, where AI can improve reliability and sensitivity when validated. AI also plays a vital role in synthesizing and optimizing nanomaterials such as carbon quantum dots (CQDs), accelerating experimental fine-tuning through predictive modeling and optimization algorithms. Moreover, AI facilitates the interpretation of large-scale data, providing deeper insights while reducing human error and analysis time. Despite these advancements, challenges remain regarding model interpretability and the integration of heterogeneous datasets. Addressing these requires explainable ML methods that bridge computational predictions with chemical reasoning. This review highlights current AI applications in chromatographic analysis, drug stability studies, and modern analytical chemistry, discusses implementation challenges, and explores future trends shaping the next generation of intelligent analytical systems.
Indexed as
Identifiers
What Socratic 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 Socratic graph.