BrainXAI ReSearch

Mission: Translating AI for Healthcare implementation

BrainXAI ReSearch is the research lab of BrainX, focused on adaptation and implementation of AI to healthcare. Our award winning work includes all aspects of AI in healthcare implementation including data-centric research, development of state of the art solutions for adoption of AI algorithms to clinical workspace, evaluation and validation of AI algorithms in healthcare, AI education, ethical application of AI, state of and strategy for AI adoption in healthcare.

Our AI experts include Raghav Awasthi, PhD., Shreya Mishra, PhD. and Dwarikanath Mahapatra, PhD., who work with our physician leadership including, Piyush Mathur, MD, Frank Papay, MD, Jacek B. Cywinski, MD, Kamal Maheshwari, MD, Ashish K Khanna, MD, Avneesh Khare, MBBS, MBA,ABAIM, and Chintan Dave, MD.

Our team collaborates with researchers and developers from across the world on multitude of projects. Our award winning work has been published in world’s leading journals such as Nature, BMJ, and has been presented at leading AI conferences such as NeurIPS, MLHC. Contact us by email, contact@brainxai.com , for research and collaboration opportunities.

BrainXAI ReSearch Interns (2023- 2024)

Rachel Grasfield is a third year medical student at Des Moines University. She completed her undergraduate degree in Psychology and Philosophy at Connecticut College and her post baccalaureate at Harvard University. Prior to medical school, she worked as a Research Coordinator at Brigham and Women's Hospital in the Department of Anesthesiology, Perioperative, and Pain Medicine. Last summer she was awarded a FAER fellowship which she completed at BWH. She is currently the Student Liaison for the Iowa Society of Anesthesiologists and sits on the Executive Board of the Women in Anesthesiology MSC.

Julia Maslinski is an undergraduate honors student studying Integrative Neuroscience and Mathematics at Binghamton University, where she researches Parkinson’s Disease in the Bishop Lab. Throughout her studies, she has developed a passion for the intersection between brain studies, computer science, and mathematics. She also conducted research in the Jing Wang lab of the NYU Grossman School of Medicine last summer, and plans on pursuing a PhD in Neural Computation with Machine Learning.

Member Publications

  1. Artificial Intelligence in Critical Care. Mathur P ,M.L. Burns, Int Anesthesiol Clin, 2019. 57(2): p. 89-102.

  2. Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.Maheshwari K, Cywinski J, Mathur P, et al. Clin Monit Comput. 2018

  3. Multimodal Machine Learning for Automated ICD Coding Proceedings of the 4th Machine Learning for Healthcare Conference. Keyang Xu, Mathur P, Frank Papay, Ashish K. Khanna, Jacek B. Cywinski, Kamal Maheshwari, Pengtao Xie, Eric P. Xing ; PMLR.106:197-215, 2019

  4. Machine learning models for perioperative research.Maheshwari K, Mathur P, Turan A. J Clin Anesth.2020;67:109990.

  5. In response to 'The clinical artificial intelligence department: a prerequisite for success'. Mathur P, Maheshwari K, Papay F. BMJ Health Care Inform.2020;27(3)

  6. Evaluation framework to guide implementation of AI systems into healthcare settings. Reddy S, Rogers W, Makinen VP, Coiera E, Brown P, Wenzel M, Weicken E, Ansari S, Mathur P, Casey A, Kelly B.BMJ Health Care Inform. 2021 

  7. Automated analysis of ambulatory surgery patient experience comments using artificial intelligence for quality improvement: A patient centered approach. Piyush Mathur, Jacek B Cywinski, Kamal Maheshwari, Francis A Papay.Intelligence-Based Medicine. 2021

  8. DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Baptiste Vasey, Piyush Mathur, Peter McCulloch, et al.Nature Medicine. 2021

  9. Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence. Maheshwari, Kamal ; Cywinski, Jacek B. ; Papay, Frank ; Khanna, Ashish K.; Mathur, Piyush .Anesthesia & Analgesia 2023. 

  10. 2019 Year in review: Machine Learning in Healthcare. Piyush Mathur,  Ashish K. Khanna,  Jacek B. Cywinski,  Kamal Maheshwari, et al.Researchgate.

  11. Explainable machine learning models to understand determinants of COVID-19 mortality in the United States.Mathur, P., Tavpritesh Sethi, Anya Mathur, Ashish Kumar Khanna, Kamal Maheshwari, Jacek B Cywinski, Simran Dua, Frank Papay. medRxiv 2020

  12. Artificial Intelligence in Healthcare - 2020 Year in Review. Maheshwari, Kamal ; Cywinski, Jacek B. ; Papay, Frank ; Khanna, Ashish K.; Mathur, Piyush, et al..Researchgate.2021.

  13. DiagnosisQA: A semi-automated pipeline for developing clinician validated diagnosis specific QA datasets. Shreya Mishra, Raghav Awasthi, Frank Papay, Kamal Maheshawari, Jacek B Cywinski, Ashish Khanna, Piyush Mathur. medRxiv. 2021.NeurIPS 2021.

  14.  Artificial Intelligence in Healthcare: 2021 Year in Review. Maheshwari, Kamal ; Cywinski, Jacek B. ; Papay, Frank ; Khanna, Ashish K.; Mathur, Piyush, et al.DOI: 10.13140/RG.2.2.25350.24645/1

  15. ShockModes: A Multimodal Model for Prognosticating Intensive Care Outcomes from Physician Notes and Vitals. Pal, R., S. Patel, A. Bhatnagar, H. Garg, P. Singh, R. S. Soun, A. Agarwal, A. Nagori, A. Khanna, R. Lodha, P. Mathur and T. Sethi . medRxiv: 2022.

  16. Quantitative and Qualitative evaluation of the recent Artificial Intelligence in Healthcare publications using Deep-Learning. Awasthi, R., Mishra, S., Cywinski, J. B., Maheshwari, K., Khanna, A. K., Papay, F. A., & Mathur, P. . Quantitative and Qualitative evaluation of the recent Artificial Intelligence in Healthcare publications using Deep-Learning. medRxiv, 2023.

  17.  Artificial Intelligence in Healthcare: 2022 Year in Review.Maheshwari, Kamal ; Cywinski, Jacek B. ; Papay, Frank ; Khanna, Ashish K.; Mathur, Piyush, et al. Researchgate.2023

  18. Extended-age Out-of-sample Validation of Risk Stratification Index 3.0 Models Using Commercial All-payer Claims. Scott Greenwald, George F. Chamoun, Nassib G. Chamoun, David Clain, Zhenyu Hong, Richard Jordan, Paul J. Manberg, Kamal Maheshwari, Daniel I. Sessler; Anesthesiology 2023; 138:264–273

  19. Endotypes of intraoperative hypotension during major abdominal surgery: a retrospective machine learning analysis of an observational cohort study. Kouz K, Brockmann L, Timmermann LM, Bergholz A, Flick M, Maheshwari K, Sessler DI, Krause L, Saugel B. Br J Anaesth. 2023 Mar;130(3):253-261.

  20. Development and Evaluation of a Risk-Adjusted Measure of Intraoperative Hypotension in Patients Having Nonemergent, Noncardiac Surgery. Christensen AL, Jacobs E, Maheshwari K, Xing F, Zhao X, Simon SE, Domino KB, Posner KL, Stewart AF, Sanford JA, Sessler DI.Anesth Analg. 2021 Aug 1;133(2):445-454.

  21. Assisted Fluid Management Software Guidance for Intraoperative Fluid Administration. Maheshwari K, Malhotra G, Bao X, Lahsaei P, Hand WR, Fleming NW, Ramsingh D, Treggiari MM, Sessler DI, Miller TE; Assisted Fluid Management Study Team. Anesthesiology. 2021 Aug 1;135(2):273-283.

  22. Hypotension Prediction Index with non-invasive arterial pressure waveforms in non-cardiac surgical patients. Maheshwari K, Buddi S, Jian Z, Settels J, Shimada T, Cohen B, Sessler DI, Hatib F. Performance of the J Clin Monit Comput. 2021 Feb;35(1):71-78.

  23. Hypotension Prediction Index for Prevention of Hypotension during Moderate- to High-risk Noncardiac Surgery. Maheshwari K, Shimada T, Yang D, Khanna S, Cywinski JB, Irefin SA, Ayad S, Turan A, Ruetzler K, Qiu Y, Saha P, Mascha EJ, Sessler DI. Anesthesiology. 2020 Dec 1;133(6):1214-1222. 

  24. Abnormal shock index exposure and clinical outcomes among critically ill patients: A retrospective cohort analysis. Maheshwari K, Nathanson BH, Munson SH, Hwang S, Yapici HO, Stevens M, Ruiz C, Hunley CF.  J Crit Care. 2020 Jun;57:5-12. 

  25. CovidNLP: A web application for distilling systemic implications of COVID-19 pandemic with natural language processing. Awasthi, Raghav, et al  MedRxiv (2020): 2020-04.

  26. Vacsim: Learning effective strategies for covid-19 vaccine distribution using reinforcement learning. Awasthi, Raghav, et al  Intelligence-Based Medicine 6 (2022): 100060

  27. Learning the mental health impact of COVID-19 in the United States with explainable artificial intelligence: observational study. Jha, I. P., Awasthi, Raghav, Kumar, A., Kumar, V., & Sethi, T. (2021) JMIR mental health 8.4 (2021): e25097.

  28. Global generalisability of AI-driven COVID-19 vaccination policies: a cross-sectional observational study.Awasthi, Raghav, Aditya Nagori, and Bouchra Nasri. medRxiv (2023): 2023-01.

  29. Use of artificial intelligence based models for learning better policy for maternal and child health. Sethi, T., and  Awasthi,Raghav. European Journal of Public Health 30.Supplement_5 (2020): ckaa165-291

  30. A Graph Embedding Approach for Deciphering the Longitudinal Associations of Global Mobility and COVID-19 Cases. Awasthi, Raghav, et al." medRxiv (2023): 2023-03

  31. Estimating the impact of health systems factors on antimicrobial resistance in priority pathogens.Awasthi, Raghav, et al. " Journal of Global Antimicrobial Resistance 30 (2022): 133-142. 

  32. Machine Learning Models to Predict Major Adverse Cardiovascular Events After Orthotopic Liver Transplantation: A Cohort Study.Jain V, Bansal A, Radakovich N, Sharma V, Khan MZ, Harris K, Bachour S, Kleb C, Cywinski J, Argalious M, Quintini C, Menon KVN, Nair R, Tong M, Kapadia S, Fares M.J Cardiothorac Vasc Anesth. 2021 Jul;35(7):2063-2069. doi: 10.1053/j.jvca.2021.02.006. Epub 2021 Feb 7.

  33. Bias in artificial intelligence algorithms and recommendations for mitigation. Nazer LH, Zatarah R, Waldrip S, Ke JXC, Moukheiber M, Khanna AK, Hicklen RS, Moukheiber L, Moukheiber D, Ma H, Mathur P. PLOS Digit Health. 2023 Jun 22;2(6):e0000278. 

  34. Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification Dwarikanath Mahapatra, Antonio Jose Jimeno Yepes, Shiba Kuanar, Sudipta Roy, Behzad Bozorgtabar, Mauricio Reyes, Zongyuan Ge. International Conference on Medical Image Computing and Computer-Assisted Intervention. – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_26

  35. Attention-Conditioned Augmentations for Self-Supervised Anomaly Detection and Localization. Bozorgtabar, B., & Mahapatra, D. (2023).Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14720-14728. https://doi.org/10.1609/aaai.v37i12.26720

  36. Graph Node Based Interpretability Guided Sample Selection for Active Learning, D. Mahapatra, A. Poellinger and M. Reyes, IEEE Transactions on Medical Imaging, vol. 42, no. 3, pp. 661-673, March 2023, doi: 10.1109/TMI.2022.3215017.

  37. Interpretability-guided inductive bias for deep learning based medical image D Mahapatra, A Poellinger, M Reyes - Medical image analysis, 2022

  38. Self-Supervised Generalized Zero Shot Learning for Medical Image Classification Using Novel Interpretable Saliency Maps, D. Mahapatra, Z. Ge and M. Reyes,  IEEE Transactions on Medical Imaging, vol. 41, no. 9, pp. 2443-2456, Sept. 2022, doi: 10.1109/TMI.2022.3163232.

  39. Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation, D. Mahapatra, A. Poellinger, L. Shao and M. Reyes, IEEE Transactions on Medical Imaging, vol. 40, no. 10, pp. 2548-2562, Oct. 2021, doi: 10.1109/TMI.2021.3061724.

  40. Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation, D. Mahapatra, B. Bozorgtabar, L. Shao, , IEEE CVPR 2020, pp 9611-9620.

  41. HumanELY: Human evaluation of LLM yield, using a novel web-based evaluation tool. Awasthi, R., S. Mishra, D. Mahapatra, A. Khanna, K. Maheshwari, J. Cywinski, F. Papay and P. Mathur (2023). medRxiv: 2023.2012.2022.23300458.

  42. Artificial Intelligence in Healthcare: 2023 Year in Review. Raghav Awasthi, Shreya Mishra, Rachel Grasfield, Julia Maslinski, Dwarikanath Mahapatra, Jacek B. Cywinski, Ashish K. Khanna, Kamal Maheshwari, Chintan Dave, Avneesh Khare, Francis A. Papay, Piyush Mathur. medRxiv. 2024. https://doi.org/10.1101/2024.02.28.24303482

  43. Artificial Intelligence in Healthcare: 2023 Year in Review Dataset. Julia Maslinski, Rachel Grasfield B, Raghav Awasthi, Shreya Mishra, Dwarikanath Mahapatra, Jacek B Cywinkski, Ashish K. Khanna, kamal maheshwari, Chintan Dave, Avneesh Khare, Francis A. Papay, Piyush Mathur(2024). figshare. Dataset. https://doi.org/10.6084/m9.figshare.25670019.v3

Book chapters

  1. Mathur P, Papay F., Building an artificial intelligence (AI) in medicine ecosystem. Intelligence-Based Medicine. 2020

  2. Bhavani S, Khanna A, Mathur P, Albumin in the critically ill.Perioperative Fluid Management.Second Edition.2020

  3. Mathur P, Cywinksi J, Papay F, Artificial Intelligence for perioperative fluid management.Perioperative Fluid Management.Second Edition.2020

  4. Font M, Khanna A, Mathur P, Case scenario for fluid therapy in Septic Shock.Perioperative Fluid Management.Second Edition.2020

  5. Impacting Perioperative quality and patient safety using Artificial Intelligence.Mathur P, Jacek B. Cywinski, Francis Papay.Artificial Intelligence: Applications in Healthcare Delivery.1st Edition.2020. 

  6. Mathur P, Ashish K Khanna, Tavpritesh Sethi.Artificial Intelligence and Management of Hypotension. ISCCM Critical Care Update 2022.

  7. Mathur P, Nirbus Bughrara, Benjamin Hoenig, Ashish K. Khanna   Artificial Intelligence Tools to Optimize Hemodynamics in the ICU.ISCCM Critical Care Update 2023.

  8. Mathur P, Sean McManus. Reem Khatib. Artificial intelligence applications in anesthesiology. Artificial Intelligence in Clinical Practice. How AI Technologies Impact Medical Research and Clinics. First Edition, 2023.

  9. Mathur P, Chao-Ping Wu. Artificial intelligence applications in critical care. Artificial Intelligence in Clinical Practice. How AI Technologies Impact Medical Research and Clinics. First Edition, 2023.

  10. Mathur P, Frank Papay. The Translational Application of AI in Healthcare. Translational Application of Artificial Intelligence in Healthcare. First edition. 2023

  11. Mathur P, Bart Geerts. Barriers and Solutions to Adoption of AI in Healthcare. Translational Application of Artificial Intelligence in Healthcare. First edition. 2023

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