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Algorithm Fairness in AI for Medicine and Healthcare

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arXiv 2021年

作者： Chen, Richard J. Chen, Tiffany Y. Lipkova, Jana Wang, Judy J. Williamson, Drew F.K. Lu, Ming Y. Sahai, Sharifa Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School 60 Fenwood Road Hale Building for Transformative Medicine BostonMA02445 United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Boston University School of Medicine BostonMA United States CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Harvard Medical School BostonMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs. In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g.-image acquisition, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. Lastly, we also review emerging technology for mitigating bias via federated learning, disentanglement, and model explainability, and their role in AI-SaMD development. Copyright © 2021, The Authors. All rights reserved.

关键词： Health care

SYNTHETIC data IN MACHINE LEARNING FOR PATHOLOGY

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Journal of Pathology informatics 2022年 13卷 100044-100044页

作者： Chen, Richard J. Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Cancer Data Science Program Dana-Farber Cancer Institute Boston MA USA

AI-BASED PATHOLOGY PREDICTS ORIGINS FOR CANCERS OF UNKNOWN PRIMARY

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Journal of Pathology informatics 2022年 13卷 100045-100045页

作者： Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Zhao, Melissa Shady, Maha Lipkova, Jana Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Cancer Data Science Program Dana-Farber Cancer Institute Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA

Consciousness in Artificial Intelligence: Insights from the science of Consciousness

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arXiv 2023年

作者： Butlin, Patrick Long, Robert Elmoznino, Eric Bengio, Yoshua Birch, Jonathan Constant, Axel Deane, George Fleming, Stephen M. Frith, Chris Ji, Xu Kanai, Ryota Klein, Colin Lindsay, Grace Michel, Matthias Mudrik, Liad Peters, Megan A.K. Schwitzgebel, Eric Simon, Jonathan VanRullen, Rufin Future of Humanity Institute University of Oxford United Kingdom Center for AI Safety United States University of Montreal MILA - Quebec AI Institute Canada Centre for Philosophy of Natural and Social Science London School of Economics and Political Science United Kingdom School of Engineering and Informatics The University of Sussex Centre de Recherche en Éthique University of Montreal United Kingdom Department of Philosophy University of Montreal Canada Department of Experimental Psychology Wellcome Centre for Human Neuroimaging University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London Institute of Philosophy University of London United Kingdom Araya Inc. School of Philosophy The Australian National University Australia Department of Psychology Center for Data Science New York University United States Center for Mind Brain and Consciousness New York University United States School of Psychological Sciences and Sagol School of Neuroscience Tel-Aviv University CIFAR Program in Brain Mind and Consciousness Israel Department of Cognitive Sciences University of California Irvine and CIFAR Program in Brain Mind and Consciousness United States Department of Philosophy University of California Riverside United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.1 © 2023, CC BY-NC-SA.

关键词： Neurophysiology

Representation Learning to Advance Multi-institutional Studies with Electronic Health Record data

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arXiv 2025年

作者： Zhou, Doudou Tong, Han Wang, Linshanshan Liu, Suqi Xiong, Xin Gan, Ziming Griffier, Romain Hejblum, Boris Liu, Yun-Chung Hong, Chuan Bonzel, Clara-Lea Cai, Tianrun Pan, Kevin Ho, Yuk-Lam Costa, Lauren Panickan, Vidul A. Gaziano, J. Michael Mandl, Kenneth Jouhet, Vianney Thiebaut, Rodolphe Xia, Zongqi Cho, Kelly Liao, Katherine Cai, Tianxi Department of Statistics and Data Science National University of Singapore Singapore Department of Statistics Columbia University NY United States Harvard T.H. Chan School of Public Health MA United States Harvard Medical School MA United States Department of Statistics University of Chicago ChicagoIL United States Univ. Bordeaux INSERM Bordeaux Population Health Research Center Bordeaux France CHU de Bordeaux Service d’Information Médicale Bordeaux France Inria SISTM Team Talence France Duke University DurhamNC United States VA Boston Healthcare System BostonMA United States Brigham and Women’s Hospital BostonMA United States Brown University ProvidenceRI United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States Department of Neurology University of Pittsburgh PittsburghPA United States

The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively conducting multi-institutional EHR studies is the data heterogeneity across systems with numerous codes that either do not exist or represent different clinical concepts across institutions. The need for data privacy further limits the feasibility of including multi-institutional patient-level data required to study similarities and differences across patient subgroups. To address these challenges, we developed the GAME algorithm. Tested and validated across 7 institutions and 2 languages, GAME integrates data in several levels: (1) at the institutional level with knowledge graphs to establish relationships between codes and existing knowledge sources, providing the medical context for standard codes and their relationship to each other;(2) between institutions, leveraging language models to determine the relationships between institution-specific codes with established standard codes;and (3) quantifying the strength of the relationships between codes using a graph attention network. Jointly trained embeddings are created using transfer and federated learning to preserve data privacy. In this study, we demonstrate the applicability of GAME in selecting relevant features as inputs for AI-driven algorithms in a range of conditions, e.g., heart failure, rheumatoid arthritis. We then highlight the application of GAME harmonized multi-institutional EHR data in a study of Alzheimer’s disease outcomes and suicide risk among patients with mental health disorders, without sharing patient-level data outside individual institutions. In summary, the GAME algorithm advances the feasibility of multi-institution EHR studies providing a method for code translation and harmonization at the scale with the adaptability needed for high-dimensional data-driven algorithms in clinical research and care. Moreover, we demonstrate that the valuab

关键词： Electronic health record

An Open-Source Knowledge Graph Ecosystem for the Life sciences

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

PAN-CANCER INTEGRATIVE HISTOLOGY-GENOMIC ANALYSIS VIA INTERPRETABLE MULTIMODAL DEEP LEARNING

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Journal of Pathology informatics 2022年 13卷 100057-100057页

作者： Chen, Richard J. Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Lipkova, Jana Shaban, Muhammad Shady, Maha Williams, Mane Joo, Bumjin Noor, Zahra Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Cancer Data Science Program Dana-Farber Cancer Institute Boston MA USA Department of Computer Science Harvard University Cambridge MA USA

Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

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SSRN

SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing