Evidence map›Paper›PMID 39596433›Full record

ReviewInternational journal of molecular sciences2024

Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives.

Arnav Tripathy, Akshata Y Patne, Subhra Mohapatra, Shyam S Mohapatra

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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  14. Role of Nanomedicine in Transforming Pharmacotherapy for Substance Use Disorder (SUD).Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Arnav TripathyCenter for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
Akshata Y PatneCenter for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.ORCID 0009-0004-4482-2206
Subhra MohapatraCenter for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
Shyam S MohapatraCenter for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.ORCID 0000-0001-5838-0681

Funding

BLRD VA IK6 BX004212BLRD VA IK6 BX006032Department of Veteran Affairs IK6BX004212
6 · The paper itself

Abstract

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals.

Indexed as

Machine LearningNanotechnologyAlgorithmsHumansNanostructurescoevolutioncross-collaborationdeep learningmachine learningnanoscalenanotechnologyneuromorphic computing

Identifiers

PMID39596433
PMCPMC11594285

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.