Karthik V. Sarma

715 total citations
18 papers, 446 citations indexed

About

Karthik V. Sarma is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Karthik V. Sarma has authored 18 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Karthik V. Sarma's work include AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Medical Imaging and Analysis (4 papers). Karthik V. Sarma is often cited by papers focused on AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Medical Imaging and Analysis (4 papers). Karthik V. Sarma collaborates with scholars based in United States and Canada. Karthik V. Sarma's co-authors include Corey Arnold, King Chung Ho, Arkadiusz Gertych, Jiayun Li, Beatrice S. Knudsen, William Speier, Steven S. Raman, Wenyuan Li, Shiwen Shen and Leonard S. Marks and has published in prestigious journals such as PLoS ONE, American Journal of Public Health and The Journal of Urology.

In The Last Decade

Karthik V. Sarma

16 papers receiving 432 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Karthik V. Sarma United States	9	242	186	93	80	58	18	446
Niels Olson United States	6	467 1.9×	370 2.0×	132 1.4×	99 1.2×	80 1.4×	9	734
Myeongchan Kim South Korea	8	176 0.7×	299 1.6×	116 1.2×	32 0.4×	97 1.7×	13	587
Maya Galperin-Aizenberg United States	13	185 0.8×	404 2.2×	275 3.0×	65 0.8×	83 1.4×	34	704
Varun Buch United States	8	132 0.5×	152 0.8×	55 0.6×	30 0.4×	30 0.5×	11	423
Cathal McCague United Kingdom	6	269 1.1×	467 2.5×	82 0.9×	41 0.5×	98 1.7×	11	737
Antônio Higor Freire de Morais Brazil	9	116 0.5×	251 1.3×	206 2.2×	32 0.4×	54 0.9×	31	562
Friederike Jungmann Germany	9	326 1.3×	235 1.3×	43 0.5×	44 0.6×	38 0.7×	15	648
Peng An China	8	138 0.6×	161 0.9×	47 0.5×	25 0.3×	21 0.4×	44	359
Joy T. Wu United States	10	205 0.8×	173 0.9×	52 0.6×	29 0.4×	27 0.5×	22	451
Atallah Baydoun United States	12	60 0.2×	146 0.8×	79 0.8×	50 0.6×	47 0.8×	27	364

Countries citing papers authored by Karthik V. Sarma

Since Specialization

Citations

This map shows the geographic impact of Karthik V. Sarma'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 Karthik V. Sarma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karthik V. Sarma more than expected).

Fields of papers citing papers by Karthik V. Sarma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Karthik V. Sarma. 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 Karthik V. Sarma. The network helps show where Karthik V. Sarma may publish in the future.

Co-authorship network of co-authors of Karthik V. Sarma

This figure shows the co-authorship network connecting the top 25 collaborators of Karthik V. Sarma. A scholar is included among the top collaborators of Karthik V. Sarma 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 Karthik V. Sarma. Karthik V. Sarma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Sarma, Karthik V., et al.. (2025). Assessing the Accuracy and Reliability of Large Language Models in Psychiatry Using Standardized Multiple-Choice Questions: Cross-Sectional Study. Journal of Medical Internet Research. 27. e69910–e69910. 1 indexed citations

Sarma, Karthik V., et al.. (2025). Integrating expert knowledge into large language models improves performance for psychiatric reasoning and diagnosis. Psychiatry Research. 355. 116844–116844.

Wang, Zichen, et al.. (2024). Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using Multiparametric MRI. 2365–2372. 4 indexed citations

Sarma, Karthik V., et al.. (2024). 4.29 Can Artificial Intelligence Make the Diagnosis? Evaluating the Accuracy of Large Language Models in Diagnosing Child and Adolescent Psychiatry Clinical Cases. Journal of the American Academy of Child & Adolescent Psychiatry. 63(10). S239–S240.

Sarma, Karthik V., et al.. (2023). Integrating Battlefield Documentation into Virtual Reality Medical Simulation Training: Virtual Battlefield Assisted Trauma Distributed Observation Kit (BATDOK). Military Medicine. 188(Supplement_6). 110–115. 3 indexed citations

Raman, Steven S., et al.. (2023). Federated Learning with Research Prototypes: Application to Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology. Academic Radiology. 30(4). 644–657. 12 indexed citations

Raman, A., Karthik V. Sarma, Steven S. Raman, et al.. (2021). Optimizing Spatial Biopsy Sampling for the Detection of Prostate Cancer. The Journal of Urology. 206(3). 595–603. 27 indexed citations

Sarma, Karthik V., A. Raman, Alan Priester, et al.. (2021). Harnessing clinical annotations to improve deep learning performance in prostate segmentation. PLoS ONE. 16(6). e0253829–e0253829. 7 indexed citations

Speier, William, Karthik V. Sarma, A. Raman, et al.. (2020). Semi-automated PIRADS scoring via mpMRI analysis. Journal of Medical Imaging. 7(6). 64501–64501. 4 indexed citations

10.

Sarma, Karthik V., Stephanie A. Harmon, Thomas Sanford, et al.. (2020). Federated learning improves site performance in multicenter deep learning without data sharing. Journal of the American Medical Informatics Association. 28(6). 1259–1264. 134 indexed citations

11.

Li, Jiayun, William Speier, King Chung Ho, et al.. (2018). An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies. Computerized Medical Imaging and Graphics. 69. 125–133. 41 indexed citations

12.

Li, Wenyuan, Jiayun Li, Karthik V. Sarma, et al.. (2018). Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE Transactions on Medical Imaging. 38(4). 945–954. 88 indexed citations

13.

Li, Jiayun, Karthik V. Sarma, King Chung Ho, et al.. (2017). A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.. PubMed. 2017. 1140–1148. 46 indexed citations

14.

Ho, King Chung, Fabien Scalzo, Karthik V. Sarma, Suzie El‐Saden, & Corey Arnold. (2016). A temporal deep learning approach for MR perfusion parameter estimation in stroke. 1315–1320. 15 indexed citations

15.

Oh, Andrea, Corey Arnold, Sitaram Vangala, et al.. (2015). Imaging–Histologic Discordance at Percutaneous Biopsy of the Lung. Academic Radiology. 22(4). 481–487. 6 indexed citations

16.

McNamara, Mary, Corey Arnold, Karthik V. Sarma, et al.. (2014). Patient portal preferences: Perspectives on imaging information. Journal of the Association for Information Science and Technology. 66(8). 1606–1615. 22 indexed citations

17.

McNamara, Mary, Karthik V. Sarma, Denise R. Aberle, Alex Bui, & Corey Arnold. (2014). Data model for personalized patient health guidelines: an exploratory study.. PubMed. 2014. 1835–44. 3 indexed citations

18.

Kulkarni, Sonali, Kavita Shah, Karthik V. Sarma, & Anish P. Mahajan. (2013). Clinical Uncertainties, Health Service Challenges, and Ethical Complexities of HIV “Test-and-Treat”: A Systematic Review. American Journal of Public Health. 103(6). e14–e23. 33 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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