Rohit Ghosh

1.5k total citations · 1 hit paper
8 papers, 886 citations indexed

About

Rohit Ghosh is a scholar working on Artificial Intelligence, Civil and Structural Engineering and Ocean Engineering. According to data from OpenAlex, Rohit Ghosh has authored 8 papers receiving a total of 886 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Civil and Structural Engineering and 2 papers in Ocean Engineering. Recurrent topics in Rohit Ghosh's work include Machine Learning in Healthcare (2 papers), Asphalt Pavement Performance Evaluation (2 papers) and Infrastructure Maintenance and Monitoring (2 papers). Rohit Ghosh is often cited by papers focused on Machine Learning in Healthcare (2 papers), Asphalt Pavement Performance Evaluation (2 papers) and Infrastructure Maintenance and Monitoring (2 papers). Rohit Ghosh collaborates with scholars based in United States, India and Australia. Rohit Ghosh's co-authors include Prashant Warier, Pooja Rao, Sasank Chilamkurthy, Vidur Mahajan, Swetha Tanamala, Norbert G. Campeau, Vasantha Kumar Venugopal, Benjamin J. Vaccaro, Nihar R. Desai and Tariq Ahmad and has published in prestigious journals such as The Lancet, Journal of the American Heart Association and Transportation Research Record Journal of the Transportation Research Board.

In The Last Decade

Rohit Ghosh

7 papers receiving 867 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study

2018 626 citations Sasank Chilamkurthy, Rohit Ghosh et al. The Lancet profile →

Peers

Rohit Ghosh

RNMI

AI

EPIDE

NEURO

HI

Prashant Warier United States

Vasantha Kumar Venugopal India

Sasank Chilamkurthy United States

Swetha Tanamala United States

Sehyo Yune South Korea

Quinlan D. Buchlak Australia

Shahein Tajmir United States

Guy Handelman Ireland

Michelle Livne Germany

Jonathan Gryak United States

Prashant Warier United States

Rohit Ghosh

290 ×1.0

RNMI

216 ×1.0

AI

185 ×1.0

EPIDE

165 ×1.0

NEURO

160 ×1.0

HI

Citations per year, relative to Rohit Ghosh Rohit Ghosh (= 1×) peers Prashant Warier

Countries citing papers authored by Rohit Ghosh

Since Specialization

Citations

This map shows the geographic impact of Rohit Ghosh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rohit Ghosh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rohit Ghosh more than expected).

Fields of papers citing papers by Rohit Ghosh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rohit Ghosh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rohit Ghosh. The network helps show where Rohit Ghosh may publish in the future.

Co-authorship network of co-authors of Rohit Ghosh

This figure shows the co-authorship network connecting the top 25 collaborators of Rohit Ghosh. A scholar is included among the top collaborators of Rohit Ghosh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rohit Ghosh. Rohit Ghosh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Ghosh, Rohit, et al.. (2025). Generation of synthetic dataset to improve deep learning models for pavement distress assessment. Innovative Infrastructure Solutions. 10(1).

2.

Ghosh, Rohit, et al.. (2023). Development of Smart Medicine Box using IoT Environment. 1–5. 4 indexed citations

3.

Ghosh, Rohit & Omar Smadi. (2021). Automated Detection and Classification of Pavement Distresses using 3D Pavement Surface Images and Deep Learning. Transportation Research Record Journal of the Transportation Research Board. 2675(9). 1359–1374. 32 indexed citations

4.

Miller, P. Elliott, Sumeet Pawar, Benjamin J. Vaccaro, et al.. (2019). Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database. Journal of Cardiac Failure. 25(6). 479–483. 51 indexed citations

5.

Mogadala, Aditya, et al.. (2019). Big Data Analytics: Systems, Algorithms, Applications. 6 indexed citations

6.

Chilamkurthy, Sasank, Rohit Ghosh, Swetha Tanamala, et al.. (2018). Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet. 392(10162). 2388–2396. 626 indexed citations breakdown →

7.

Joshi, Prem Lal, et al.. (2018). Big Data Analytics for Micro-Seismic Monitoring. Offshore Technology Conference Asia. 8 indexed citations

8.

Ahmad, Tariq, Lars H. Lund, Pooja Rao, et al.. (2018). Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. Journal of the American Heart Association. 7(8). 159 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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