Our goal is to create a virtual club to discuss the latest developments in the field of AI in medicine. This will be a participant-driven journal club and we will cover papers in form of lightning talks and deep dive and discussion. All NYU affiliates are welcome. It will be fun!
We meet every other Friday 11am-noon. For calendar invite reach out to narges.razavian@nyulangone.org
| Date | Theme | List of Papers |
|---|---|---|
| Upcoming Dec 10th 2021 | AI in Ophthalmology | Session Lead By Dr. Lama Al-Aswad - Vice Chair for Innovation, Department of Ophthalmology Federated AI By:Dinah Chen, MD - Innovation Fellow, Department of Ophthalmology Incorporating Decision Intelligence in Healthcare: Potential Use of AI in Cost-Effectiveness Modelling By: Shefali Sood, MD - MD/MPA Candidate, Department of Ophthalmology Innovation Lab A Segmentation-Free Approach for Point-Wise Visual Field Estimation from OCT Images By:Zhiqi Chen - PhD Candidate, Tandon School of Engineering Zoom recordings |
| Dec 3rd 2021 | AI in Genomics | Sequence-based studies of genetic regulatory control: From molecules to traits By: Aravinda Chakravarti - Director, Center for Human Genetics and Genomics Readings: Lee DW, Kapoor A, Safe A, Song L, Halushka MK, Crawford GE, Chakravarti A: Human cardiac cis-regulatory elements, their cognate transcription factors and regulatory DNA sequence variants. Genome Research 28:1577-1588, 2018. PMID: 30139769 https://pubmed.ncbi.nlm.nih.gov/30139769/ Kapoor A, Lee D, Zhu L, Soliman EZ, Grove ML, Boerwinkle E, Arking DE, Chakravarti A: Multiple SCN5A variant enhancers modulate its cardiac gene expression and the QT interval. Proc Natl Acad Sci (USA) 116:10636-10645, 2019. PMID: 31068470. https://pubmed.ncbi.nlm.nih.gov/31068470/ Chatterjee, Karasaki KM, Fried LE, Kapoor A, Chakravarti A: A multi-enhancer RET regulatory code is disrupted in Hirschsprung disease. Genome Research 31:1-10, 2021.https://pubmed.ncbi.nlm.nih.gov/34782358/ Zoom recordings |
| Nov 12th 2021 | Artificial Intelligence in Otolaryngology | Artificial Intelligence and Cochlear Implants By: Elad Sagi - Department of Otolaryngology – Head & Neck Surgery Reading: Crowson MG, Lin V, Chen JM, Chan TCY., Machine Learning and Cochlear Implantation-A Structured Review of Opportunities and Challenges., Otol Neurotol. 2020;41(1):e36-e45. doi:10.1097/MAO.0000000000002440, https://pubmed.ncbi.nlm.nih.gov/31644477/ Zoom recordings |
| Oct 15th 2021 | NYU Langone Population Health Data Hub & DataCore | Overview of Population Health Data Hub (PHDH): Vision and Current State By: Andrew Fair - Population Health Data Hub / Thorpe Epi Lab Chuan Hong – Population Health Data Hub Nikolai Bourdain – Research Digital Experience / Pop Health Data Hub Hamzad Persaud – DataCore / Pop Health Data Hub Zoom recordings |
| Oct 1st 2021 | AI Moonshots and Federated Learning for Healthcare | AI Moonshots (Part 2) Self Supervised Learning and Foundation Models By: Narges Razavian Primer on Federated Learning for Healthcare By: Vincent Major Readings: Self Supervised Learning and Foundation Models [1] Bommasani R, et.al. On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258. 2021 Aug 16. [2] Yann LeCun and Ishan Misra Self-supervised learning: The dark matter of intelligence 2021 Mar 4 Federated Learning for Healthcare: [1] McMahan B. et.al. Federated Learning: Collaborative Machine Learning without Centralized Training Data Google AI Blog 2017 [2] Rieke N. et.a. The future of digital health with federated learning npj Digital Medicine 2020 [3] Dayan I. et.al. Federated learning for predicting clinical outcomes in patients with COVID-19 Nature Medicine 2021 Zoom recordings |
| Sept 17th 2021 | Intro to the new season and AI moonshots | Introduction to new Season By: Yin Aphinyanaphongs Notes from discussions: (1) Population Health Data Hub:link For more information reach out to Andrew Fair. (2) NYULH Perlmutter Cancer Center Data Hub: link For more information reach out to Megan Winner and Ye Yuan. AI Moonshots or What Keeps AI Community Awake at Night (Part 1: Self Supervised Learning and Foundation Models) By: Narges Razavian Readings: [1] Bommasani R, et.al. On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258. 2021 Aug 16. [2] Yann LeCun and Ishan Misra Self-supervised learning: The dark matter of intelligence 2021 Mar 4 Zoom recordings |
| Date | Theme | List of Papers |
|---|---|---|
| Feb 19th 2021 11am - noon Eastern |
Introduction & AI dos and donts |
Introduction to the meeting series Slides Zoom recordings |
| March 5th 2021 | Member Spotlights | Ye Yuan, MD PhD NYU Langone Department of Radiation Oncology Slides Nicolas Coudray, PhD Coudray, N., Ocampo, P.S., Sakellaropoulos, T. et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med 24, 1559–1567 (2018). https://www.nature.com/articles/s41591-018-0177-5 Slides Zoom recordings |
| March 19th 2021 | Imaging and Reconstruction | Fred Kwon Papers discussed: Kwon, Young Joon (Fred) et al. “Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication of Patients with COVID-19 from the Emergency Department.” Radiology. Artificial Intelligence e200098. 16 Dec. 2020 https://pubs.rsna.org/doi/10.1148/ryai.2020200098 Kwon, Y. J., Toussie, D., Azour, L., Concepcion, J., Eber, C., Reina, G. A., ... & Costa, A. B. (2020, November). Appropriate Evaluation of Diagnostic Utility of Machine Learning Algorithm Generated Images. In Machine Learning for Health (pp. 179-193). PMLR. http://proceedings.mlr.press/v136/kwon20a.html Slides 1 Slides 2 Slides 3 Zoom recordings |
| April 2nd 2021 | Spotlights on multiple AI domains | Lama Al-Aswad Professor of Ophthalmology Professor of Population Health Vice Chair for Innovations Director, Teleophthalmology, Artificial Intelligence and Innovations Associate Director. Glaucoma Fellowship NYU Langone Grossman School of Medicine Michael Martini Graduate School of Biomedical Sciences Icahn School of Medicine at Mount Sinai Narges Razavian Assistant Professor, Department of Population Health and Department of Radiology Center for Healthcare Innovation and Delivery Science Predictive Analytics Unit Affiliate Faculty at NYU Center for Data Science Zoom recordings |
| April 16nd 2021 | FDA regulatory aspects in AI/ ML | Yin Aphinyanaphongs Assistant Professor, Department of Population Health Assistant Professor, Department of Medicine Part 1: AI/ ML Software as a Device Action Plan Reading Part 2: How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals paper Member highlight: Jayne Kim, PhD Manager of Regulatory Affairs within the Office of Science and Research at NYU Langone Health. Slides Zoom recordings |
| April 30th 2021 | Overview of AI in Radiology | Sumit Chopra Associate Professor Courant Institute and Department of Radiology - Grossman School of Medicine, NYU Director of Machine Learning Research, Department of Radiology - NYU Langone Health Talk 1: AI for Medical Imaging Gregory Lemberskiy Postdoctoral Fellow at NYU Grossman School of Medicine Talk 2: AI in Image Acquisition Patricia Johnson Assistant Professor at NYU Grossman School of Medicine Talk 3: AI in Image Reconstructio Krzysztof Geras Assistant Professor, Department of Radiology Talk 4: AI in Image Analysis Ben Zhang Data Scientist Department of Radiology NYU Langone Talk 5: AI for Workflow Optimization Zoom recordings |
| May 28th 2021 | AI in Hematology | Marc Braunstein Assistant Professor, Department of Medicine at NYU Long Island School of Medicine Course Co-Director, Hematology-Oncology System, NYU Long Island School of Medicine Talk 1: Machine Learning in Hematology Readings: Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods Staging System to Predict the Risk of Relapse in Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation Prognostic model for multiple myeloma progression integrating gene expression and clinical features Zoom recordings |
We strongly encourage departments to each take over the sessions, champion talks from researchers within their groups, and be part of the leadership of this club.