Dr. Suvrankar Datta is an AI researcher and radiologist with clinical training from AIIMS Delhi, where he completed his M.D. in Radiodiagnosis. A Gold Medallist from JIPMER Puducherry, he has led pioneering AI-driven radiology projects, including automated rib fracture detection and LLM-based speech recognition for radiology reporting, advancing diagnostic precision and workflow efficiency. His research has received national and international recognition, most notably the RSNA Trainee Research Prize (2023). Beyond research, he plays an active role in medical advocacy and healthcare policy as President of FAIMA and through leadership positions in various medical associations.

At Ashoka University, Dr. Datta’s research focuses on integrating artificial intelligence with medical imaging, fostering interdisciplinary collaborations that bridge clinical practice with computational innovation. His work involves developing novel algorithms and multimodal AI frameworks, contributing to the advancement of digital healthcare and precision radiology. Through active engagement in academic research and thought leadership, he aims to shape the future of AI-driven medicine and healthcare policy.

Sarangi PK, Datta S, Panda BB, Panda S, Mondal H.
Evaluating ChatGPT-4’s Performance in Identifying Radiological Anatomy in FRCR Part 1 Examination Questions. Indian Journal of Radiology and Imaging. 2024 Nov (e-First).
Access at: https://doi.org/10.1055/s-0044-1792040
Sarangi PK, Datta S, Swarup MS, Panda S, Nayak DS, Malik A, Datta A, Mondal H.
Radiologic Decision-Making for Imaging in Pulmonary Embolism: Accuracy and Reliability of Large Language Models—Bing, Claude, ChatGPT, and Perplexity. Indian Journal of Radiology and Imaging. 2024 Oct;34(04):653-60.
Access at: https://doi.org/10.1055/s-0044-1787974
Sarangi PK, Datta S, Mondal H.
Comment on: ChatGPT: Chasing the Storm in Radiology Training and Education. Indian Journal of Radiology and Imaging. 2024 May 3.
Access at: https://doi.org/10.1055/s-0044-1786722
Dabass M, Chandalia A, Datta S, Mahapatra D.
Attention Learning-Enabled 3D Conditional Generative Adversarial Network for Lung Nodule Segmentation. In: Uddin MS, Bansal JC (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence (IJCACI 2022). Algorithms for Intelligent Systems. Springer, Singapore, 2024.
Access at: https://doi.org/10.1007/978-981-97-0180-3_24
Dabass M, Chandalia A, Datta S, Mahapatra D.
ALE-GAN: a 3D conditional generative adversarial network with attention learning modules for lung nodule segmentation. In: Das S, Saha S, Coello CAC, Rathore H, Bansal JC (eds) Advances in Data-Driven Computing and Intelligent Systems (ADCIS 2023). Lecture Notes in Networks and Systems, vol 890. Springer, Singapore, 2024.
Access here: https://doi.org/10.1007/978-981-99-9531-8_26
Dabass M, Chandalia A, Senasi R, Datta S.
Attention and residual-atrous convolutional learning-based CNN architecture for lung nodule segmentation and classification. In: Das S, Saha S, Coello CAC, Bansal JC (eds) Advances in Data-Driven Computing and Intelligent Systems (ADCIS 2023). Lecture Notes in Networks and Systems, vol 893. Springer, Singapore, 2024.
Access here: https://doi.org/10.1007/978-981-99-9518-9_8
Ellappan K, Datta S, Muthuraj M, Lakshminarayanan S, Pleskunas JA, et al.
Evaluation of factors influencing Mycobacterium tuberculosis complex recovery and contamination rates in MGIT960. Indian J Tuberc. 2020 Oct;67(4):466-471.
Access at: https://doi.org/10.1016/j.ijtb.2020.07.016

Suvrankar Datta

Suvrankar Datta

Study at Ashoka

Campus Location

Delhi Office

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