Faisal Mahmood, PhD featured on Eric Topol’s “Ground Truths”

Faisal Mahmood, PhD featured on Eric Topol’s “Ground Truths”

Faisal Mahmood, Associate Professor of Pathology, was  featured on the July 28th, 2024 episode of Dr. Eric Topol’s Ground Truths podcast to discuss A.I.’s transformation of Pathology.  Ground Truths is a weekly podcast addressing facts, data, and analytics about biomedical matters.

 

October ABC Seminar: Zhi Huang, PhD – Univ of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

October ABC Seminar: Zhi Huang, PhD – Univ of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

The integration of Artificial Intelligence (AI) in clinical pathology has faced significant hurdles due to constraints in data collection and challenges associated with model transparency and interpretability.  In this talk, we introduce a novel digital pathology AI framework named nuclei.io, which leverages active learning and incorporates real-time feedback from human experts. This innovative approach empowers pathologists to quickly generate diverse datasets and develop models for various clinical applications. To demonstrate the effectiveness of our framework, we conducted two user studies employing such human–AI collaboration strategy. These studies focused on two key areas: the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. The results from these studies showed significant enhancements in sensitivity, accuracy, and diagnostic efficiency with the integration of AI. Our findings underscore the benefits of the human-in-the-loop AI framework, highlighting its potential to transform the field of digital pathology.

Speaker:  Zhi Huang, PhD
Affiliation: Perelman School of Medicine, University of Pennsylvania
Position:  Assistant Professor (incoming), Dept of Pathology and Laboratory Medicine, Dept of Biostatistics, Epidemiology and Informatics
Research Links: https://www.zhihuang.ai
Host: Andrew Song, PhD – Mahmood Lab

Date: Monday, October 21, 2024
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Zhi Huang is an incoming tenure-track assistant professor at the University of Pennsylvania starting January 2025. He obtained his PhD in Electrical and Computer Engineering (ECE) from Purdue University in August 2021. Since August 2021, He has been a postdoctoral fellow at Stanford University. His research focuses on AI/ML innovation and its application to medicine, with topics including vision-language foundation model for pathology, human-AI collaboration, neurodegenerative diseases, etc. His research has drawn wide public attention (including the New York Times, Stanford Magazine, and Stanford Scope) and has resulted in translational innovations. In 2022, Zhi Huang co-founded nuclei.io — a human-in-the-loop AI platform for digital pathology. It was selected as one of only 9 Stanford Catalyst 2023 cohort innovations.

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ABC Seminar: Zhi Huang, PhD – University of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

Speaker:  Zhi Huang, PhD
Affiliation: Perelman School of Medicine, University of Pennsylvania
Position:  Assistant Professor (incoming)
Dept of Pathology and Laboratory Medicine
Dept of Biostatistics, Epidemiology and Informatics
Host: Andrew Song, PhD – Mahmood Lab

Date: Monday, October 21, 2024
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Abstract: The integration of Artificial Intelligence (AI) in clinical pathology has faced significant hurdles due to constraints in data collection and challenges associated with model transparency and interpretability.  In this talk, we introduce a novel digital pathology AI framework named nuclei.io, which leverages active learning and incorporates real-time feedback from human experts. This innovative approach empowers pathologists to quickly generate diverse datasets and develop models for various clinical applications. To demonstrate the effectiveness of our framework, we conducted two user studies employing such human–AI collaboration strategy. These studies focused on two key areas: the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. The results from these studies showed significant enhancements in sensitivity, accuracy, and diagnostic efficiency with the integration of AI. Our findings underscore the benefits of the human-in-the-loop AI framework, highlighting its potential to transform the field of digital pathology.

Research Links: https://www.zhihuang.ai

Zhi Huang is an incoming tenure-track assistant professor at the University of Pennsylvania starting January 2025. He obtained his PhD in Electrical and Computer Engineering (ECE) from Purdue University in August 2021. Since August 2021, He has been a postdoctoral fellow at Stanford University. His research focuses on AI/ML innovation and its application to medicine, with topics including vision-language foundation model for pathology, human-AI collaboration, neurodegenerative diseases, etc. His research has drawn wide public attention (including the New York Times, Stanford Magazine, and Stanford Scope) and has resulted in translational innovations. In 2022, Zhi Huang co-founded nuclei.io — a human-in-the-loop AI platform for digital pathology. It was selected as one of only 9 Stanford Catalyst 2023 cohort innovations.

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Mahmood Lab releases PathChat, a vision-language AI assistant for Pathology

Mahmood Lab releases PathChat, a vision-language AI assistant for Pathology

Developed by the Mahmood Lab, PathChat is a vision-language AI assistant for Pathology that can analyze histology images and answer diverse pathology-related queries.

See a demonstration of PathChat.

The PathChat publication can be found in the June edition of Nature here:  https://www.nature.com/articles/s41586-024-07618-3 

Nature Medicine Publications detail Mahmood Lab’s Design of AI Foundation Models to Advance Pathology

Nature Medicine Publications detail Mahmood Lab’s Design of AI Foundation Models to Advance Pathology

Two foundation models for pathology AI developed by the Mahmood Lab published in Nature Medicine: UNI and CONCH.

Foundation models, advanced artificial intelligence systems trained on large-scale datasets, hold the potential to provide unprecedented advancements for the medical field. In computational pathology (CPath), these models may excel in diagnostic accuracy, prognostic insights, and predicting therapeutic responses.

These foundation models were adapted to over 30 clinical, diagnostic needs, including disease detection, disease diagnosis, organ transplant assessment, and rare disease analysis. The new models overcame limitations posed by current models, performing well not only for the clinical tasks the researchers tested but also showing promise for identifying new, rare and challenging diseases.

Jakob Nikolas Kather MD, Msc – “Deep Learning-based biomarkers for precision oncology”

Jakob Nikolas Kather MD, Msc – “Deep Learning-based biomarkers for precision oncology”

Precision oncology requires complex biomarkers which are often based on molecular and genetic tests of tumor tissue. For many of these tests, universal implementation in clinical practice is limited. However, for virtually every cancer patient, tissue slides stained with hematoxylin and eosin (H&E) are available. Deep Learning can extract biomarkers for better treatment decisions from these images. This talk will summarize the state of the art of Deep Learning in oncology for precision oncology biomarkers. It will cover the technical foundations, emerging use cases and established applications which are available for clinical use already.

Prof. Dr. Jakob Nikolas Kather, MD, MSc
Else Kröner Professor for Clinical Artificial Intelligence
Technical University, Dresden

Professor Jakob Kather holds dual appointments in medicine and computer science at the Technical University (TU) Dresden, Germany, serves as a senior physician in medical oncology at the University Hospital Dresden and holds an additional affiliation with the National Center for Tumor Diseases (NCT) in Heidelberg. His research is focused on applying artificial intelligence in precision oncology. Prof. Kather’s research team at TU Dresden is using deep learning techniques to analyze a spectrum of clinical data, including histopathology, radiology images, textual records, and multimodal datasets. Guided by the belief that medical and tech expertise needs to be combined, medical researchers in his team learn computer programming and data analysis, while computer scientists are immersed in cancer biology and oncology. Prof. Kather chairs the “Working group on Artificial Intelligence” at the German Society of Hematology and Oncology (DGHO) and is a member of the pathology task force of the American Association for Cancer Research (AACR). His work is supported by numerous European and national grants, which enable the team to develop new deep learning methods for medical data analysis techniques and to apply them in precision oncology.

Hybrid Talk
In person: TBD
On Zoom

Time: TBD

Hosted by:  Mahmood Lab

Harris H. Wang, PhD Columbia University-“Spatial metagenomics, culturomics, and engineering of human and environmental microbiomes”

Harris H. Wang, PhD Columbia University-“Spatial metagenomics, culturomics, and engineering of human and environmental microbiomes”

BWH Computational Pathology Special Seminar

Harris H. Wang, PhD
Associate Professor of Systems Biology
Interim Chair, Department of Systems Biology, Columbia University

Microbes that colonize the gastrointestinal tract play important roles in host metabolism, immunity, and homeostasis. Microbes that live in soil are responsible for a variety of key decomposition and remediation activities in the biosphere. Better tools to study and alter these microbiomes are essential for unlocking their vast potential to improve human health and combat climate change. This talk will describe our recent efforts to develop next-generation tools to study and modify microbial communities. Specifically, I will discuss new advances in spatial metagenomics, AI-enabled culturomics, and precision microbiome editing to program different microbiomes with new traits. These capabilities provide a foundation to accelerate the development of microbiome-based products and therapies.

Research Links:  http://wanglab.c2b2.columbia.edu/

Harris Wang is an Associate Professor of Systems Biology and serves as the Interim Chair of the Department of Systems Biology at Columbia University, Vagelos College of Physicians and Surgeons. He is also jointly appointed the Department of Pathology and Cell Biology. Dr. Wang received his B.S. degrees in Mathematics and Physics from MIT and his Ph.D. in Biophysics from Harvard University. His research group mainly develops enabling genomic technologies to characterize the mammalian gut microbiome and to engineer these microbes with the capacity to monitor and improve human health. Dr. Wang is an Investigator of the Burroughs Wellcome Fund and the recipient of numerous awards, including the Vilcek Prize for Creative Promise in Biomedical Science, Blavatnik National Award Finalist, NSF CAREER, Sloan Research Fellowship, and the Presidential Early Career Award for Scientists and Engineers (PECASE).

Date: Friday October 27, 2023
Time:  2:00-3:00pm ET
In Person: Wolf Conference Center, Hale BTM 02006B, 60 Fenwood Rd Boston 02115
Online: https://partners.zoom.us/j/89199829402
Meeting ID: 891 9982 9402

Algorithmic fairness in artificial intelligence for medicine and healthcare: Nature Biomedical Engineering

Algorithmic fairness in artificial intelligence for medicine and healthcare: Nature Biomedical Engineering

In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also review emerging technology for mitigating biases via disentanglement, federated learning and model explainability, and their role in the development of AI-based software as a medical device.

Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, Sahai S, Mahmood F. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023 06; 7(6):719-742. PMID: 37380750.