ReviewDigital health
Transforming cutting-edge healthcare: Emerging trends in metabolomics and drug design using artificial intelligence and big data methodologies.
Review in Digital health. 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is a disruptive area which transforms cutting-edge healthcare technology to analyze clinical workflows, sharpen diagnostics, and improve precision medicine. The goal of this review is to identify approaches involving a collaborative examination to determine the key features influencing the adoption of artificial intelligence methodologies in advanced cutting-edge solutions for metabolomics and drug design. In clinical and translational settings, a comprehensive investigation of legal and ethical principles will be included to highlight the significance of omics analysis and drug design in the application of artificial intelligence tools with artificial intelligence in healthcare, the real-world uses, and difficulties tied to societal and regulatory issues. The real-world effects of artificial intelligence for researchers and technicians can provide guidance for tailored strategies focused on leveraging potential to improve high-dimensional data analysis on metabolomics and drug design. As artificial intelligence methodologies continue to evolve, efforts must be directed toward structured frameworks that uphold human oversight and engagement to optimize the utility of artificial intelligence algorithms and big data methodologies. The key contributions of this study include a comprehensive overview of cutting-edge artificial intelligence methodologies and software programs in metabolomics and drug design, and critical perspectives which can solidify the future directions in the development of algorithmic approaches to bridge metabolomics and drug design.
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.