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Are AI Foundation Models Efficient for Segmentation of Echocardiograms?

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Journal of the American Society of Echocardiography 2025年第6期38卷 514-516页

作者： Ferreira, Danielle L. Arnaout, Rima Division of Cardiology Department of Medicine University of California San Francisco San Francisco California United States Bakar Computational Health Sciences Institute University of California San Francisco San Francisco California United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California San Francisco San Francisco California United States Center for Intelligent Imaging Department of Radiology University of California San Francisco San Francisco California United States

Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Prediction-powered Generalization of Causal Inferences 41

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Prediction-powered Generalization of Causal Inferences

41st International Conference on Machine Learning, ICML 2024

作者： Demirel, Ilker Alaa, Ahmed Philippakis, Anthony Sontag, David MIT CSAIL United States Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard United States Department of Computational Precision Health UC Berkeley UCSF United States

Causal inferences from a randomized controlled trial (RCT) may not pertain to a target population where some effect modifiers have a different distribution. Prior work studies generalizing the results of a trial to a target population with no outcome but covariate data available. We show how the limited size of trials makes generalization a statistically infeasible task, as it requires estimating complex nuisance functions. We develop generalization algorithms that supplement the trial data with a prediction model learned from an additional observational study (OS), without making any assumptions on the OS. We theoretically and empirically show that our methods facilitate better generalization when the OS is "high-quality", and remain robust when it is not, and e.g., have unmeasured confounding. Copyright 2024 by the author(s)

关键词： Prediction models

A Narrative Review on the Application of Large Language Models to Support Cancer Care and Research

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Yearbook of medical informatics 2024年第1期33卷 90-98页

作者： Benson, Ryzen Elia, Marianna Hyams, Benjamin Chang, Ji Hyun Hong, Julian C. Department of Radiation Oncology University of California San Francisco CA United States Bakar Computational Health Sciences Institute University of California San Francisco CA United States School of Medicine University of California San Francisco CA United States Department of Radiation Oncology Seoul National University Hospital Seoul National University College of Medicine Seoul South Korea UCSF UC Berkeley Joint Program in Computational Precision Health (CPH) San Francisco CA United States

OBJECTIVES: The emergence of large language models has resulted in a significant shift in informatics research and carries promise in clinical cancer care. Here we provide a narrative review of the recent use of large language models (LLMs) to support cancer care, prevention, and research. METHODS: We performed a search of the Scopus database for studies on the application of bidirectional encoder representations from transformers (BERT) and generative-pretrained transformer (GPT) LLMs in cancer care published between the start of 2021 and the end of 2023. We present salient and impactful papers related to each of these themes. RESULTS: Studies identified focused on aspects of clinical decision support (CDS), cancer education, and support for research activities. The use of LLMs for CDS primarily focused on aspects of treatment and screening planning, treatment response, and the management of adverse events. Studies using LLMs for cancer education typically focused on question-answering, assessing cancer myths and misconceptions, and text summarization and simplification. Finally, studies using LLMs to support research activities focused on scientific writing and idea generation, cohort identification and extraction, clinical data processing, and NLP-centric tasks. CONCLUSIONS: The application of LLMs in cancer care has shown promise across a variety of diverse use cases. Future research should utilize quantitative metrics, qualitative insights, and user insights in the development and evaluation of LLM-based cancer care tools. The development of open-source LLMs for use in cancer care research and activities should also be a priority. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： large language models clinical decision support artificial intelligence natural language processing generative AI

Prediction-powered Generalization of Causal Inferences

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arXiv 2024年

关键词： Population statistics

Prediction-powered generalization of causal inferences 24

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Prediction-powered generalization of causal inferences

Proceedings of the 41st International Conference on Machine Learning

作者： Ilker Demirel Ahmed Alaa Anthony Philippakis David Sontag MIT CSAIL and Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Department of Computational Precision Health UC Berkeley and UCSF Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard MIT CSAIL

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When exposure affects subgroup membership: Framing relevant causal questions in perinatal epidemiology and beyond

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arXiv 2024年

作者： Gupta, Shalika Balzer, Laura B. Kamya, Moses R. Havlir, Diane V. Petersen, Maya L. Division of Epidemiology University of California Berkeley United States Division of Biostatistics University of California Berkeley United States Department of Medicine Makerere University College of Health Sciences Uganda Division of HIV Infectious Diseases and Global Medicine Department of Medicine University of California San Francisco United States UCSF-UC Berkeley Program in Computational Precision Health United States

Perinatal epidemiology often aims to evaluate exposures on infant outcomes. When the exposure affects the composition of people who give birth to live infants (e.g., by affecting fertility, behavior, or birth outcomes), this "live birth process" mediates the exposure effect on infant outcomes. Causal estimands previously proposed for this setting include the total exposure effect on composite birth and infant outcomes, controlled direct effects (e.g., enforcing birth), and principal stratum direct effects. Using perinatal HIV transmission in the SEARCH Study as a motivating example, we present two alternative causal estimands: 1) conditional total effects;and 2) conditional stochastic direct effects, formulated under a hypothetical intervention to draw mediator values from some distribution (possibly conditional on covariates). The proposed conditional total effect includes impacts of an intervention that operate by changing the types of people who have a live birth and the timing of births. The proposed conditional stochastic direct effects isolate the effect of an exposure on infant outcomes excluding any impacts through this live birth process. In SEARCH, this approach quantifies the impact of a universal testing and treatment intervention on infant HIV-free survival absent any effect of the intervention on the live birth process, within a clearly defined target population of women of reproductive age with HIV at study baseline. Our approach has implications for the evaluation of intervention effects in perinatal epidemiology broadly, and whenever causal effects within a subgroup are of interest and exposure affects membership in the subgroup. © 2024, CC BY.

关键词： Epidemiology

Grade Inflation in Generative Models

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arXiv 2024年

作者： Nguyen, Phuc Li, Miao Morgan, Alexandra Arnaout, Rima Arnaout, Ramy BostonMA02215 United States The Department of Medicine The Bakar Computational Health Sciences Institute The UCSF UC Berkeley Joint Program for Computational Precision Health The University of California San Francisco San FranciscoCA94143 United States The Department of Pathology The Division of Clinical Informatics Department of Medicine BIDMC Harvard Medical School BostonMA02215 United States

Generative models hold great potential, but only if one can trust the evaluation of the data they generate. We show that many commonly used quality scores for comparing two-dimensional distributions of synthetic vs. ground-truth data give better results than they should, a phenomenon we call the "grade inflation problem." We show that the correlation score, Jaccard score, earth-mover's score, and Kullback-Leibler (relative-entropy) score all suffer grade inflation. We propose that any score that values all datapoints equally, as these do, will also exhibit grade inflation;we refer to such scores as "equipoint" scores. We introduce the concept of "equidensity" scores, and present the Eden score, to our knowledge the first example of such a score. We find that Eden avoids grade inflation and agrees better with human perception of goodness-of-fit than the equipoint scores above. We propose that any reasonable equidensity score will avoid grade inflation. We identify a connection between equidensity scores and Rényi entropy of negative order. We conclude that equidensity scores are likely to outperform equipoint scores for generative models, and for comparing low-dimensional distributions more generally. © 2024, CC BY-NC-SA.

关键词： Inflation

Are foundation models efficient for medical image segmentation?

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arXiv 2023年

作者： Ferreira, Danielle L. Arnaout, Rima Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute University of California San Francisco 521 Parnassus Avenue San FranciscoCA94143 United States Department of Medicine Division of Cardiology Bakar Computational Health Sciences Institute UCSF-UC Berkeley Joint Program in Computational Precision Health Department of Radiology Center for Intelligent Imaging University of California San Francisco 521 Parnassus Ave Box 0124 San FranciscoCA94143 United States

Foundation models are experiencing a surge in popularity. The Segment Anything model (SAM) asserts an ability to segment a wide spectrum of objects but required supervised training at unprecedented scale. We compared SAM’s performance (against clinical ground truth) and resources (labeling time, compute) to a modality-specific, label-free self-supervised learning (SSL) method on 25 measurements for 100 cardiac ultrasounds. SAM performed poorly and required significantly more labeling and computing resources, demonstrating worse efficiency than SSL. © 2023, CC BY-NC-ND.

关键词： Image segmentation