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Papers & Reports

Topics in this section

AI and the medical profession, Bayesian modeling, bioinformatics, big data, bias, cardiology, clinical research, commentary, computer vision, cybersecurity, decision support systems, drug response, dynamical systems, editorial, fairness, implementation, infectious diseases, medical education, modeling, modelling, natural language processing, oncology, performance evaluation, perspective, policy, privacy, probabilistic ML, public health, regulation, reinforcement learning, reproducibility, research, review, software package descriptor, surgical practice, systematic review, tutorial, unsupervised learning

Papers & Reports

Reproducibility standards for machine learning in the life sciences, Heil BJ, Hoffman MM, Markowetz F, Lee S-I, Greene CS, Hicks SC
Nat Methods 2021 Oct; 18(10):1132–5 (Bias & fairness)

Keeping checks on machine learning 
Nat Methods 2021 Oct; 18(10):1119 (Commentary, Policy, Regulation)

Transparency and reproducibility in AI, Haibe-Kains B, Adam GA, Hosny A, Khodakarami F, MAQC Society Board, Waldron L, Wang B, McIntosh C, Goldenberg A, Kundaje A, Greene CS, Broderick T, Hoffman MM, Leek JT, Korthauer K, Huber W, Brazma A, Pineau J, Tibshirani R, Hastie T, Ioannidis JPA, Quackenbush J, Aerts HJWL.  
Nature 2020 Oct 14; 586:E14–16 (Policy, Regulation)

Statistical inference, learning and models in big data, Franke B, Plante JF, Roscher R, Lee A, Smyth C, Hatefi A, Chen F, Gil E, Schwing A, Selvitella A, Hoffman MM, Grosse R, Hendricks D, Reid N.
Int Stat Rev 2016. 84:371–89. (Research)

How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, We et al.  
Nature Medicine, 2021 (Commentary, Policy, Regulation)

A guide to deep learning in healthcare, Esteva et al.    
Nature Medicine, 2021 (Review)

AI-facilitated health care requires education of clinicians, Keane and Topol    
The Lancet, 2021 (Commentary, Medical education)

An algorithmic approach to reducing unexplained pain disparities in underserved populations, Pierson et al.
Nature Medicine, 2021 (Research, Bias & fairness)

Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty, Harish, Morgado et al.
Academic Medicine, 2021 (Commentary, Medical education, Ethics)

Reproducibility in machine learning for health research: Still a ways to go, McDermott et al.    
Science Translational Medicine, 2021 (Commentary, Reproducibility)

Ethical machine learning in healthcare, Chen et al.    
Annual Reviews in Biomedical Data Science, 2021 (Review, Ethics)

Artificial intelligence for clinical oncology, Kann et al.     
Cancer Cell, 2021 (Perspective, Clinical research, Oncology)

Machine learning for single-cell genomics data analysis,
Raimundo et al.    
Bioarxiv, 2021 (Review, Bioinformatics)

Gov't Canada: Health and Biosciences Sector Regulatory Review    
Gov. Canada, 2021 (Report, Policy)

Gov't Canada: Responsible use of artificial intelligence (AI)    
Gov. Canada, 2021 (Report, Policy)

Royal College of Physicians and Surgeons of Canada: Task Force Report on Artificial Intelligence and Emerging Digital Technologies, Royal College of Physicians and Surgeons of Canada        
2021, (Report, AI and the Medical Profession)

Machine learning for deciphering cell heterogeneity and gene regulation, Scherer et al.    
Nature Computational Science, 2021 (Review, Bioinformatics)

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review, Quer et al.
JACC, 2021 (Review, Cardiology)

Probabilistic Machine Learning for Healthcare, Chen et al.    
Annual Reviews in Biomedical Data Science, 2021 (Review, Probabilistic ML)

The intersection of genomics and big data with public health: Opportunities for precision public health, Khoury et al.    
PLoS Medicine, 2021 (Review, Public health, Big data)

International evaluation of an AI system for breast cancer screening, McKinney et al.
Nature, 2020 (Research, Clinical research)

Transparency and reproducibility in artificial intelligence, Haibe-Kains et al.  
Nature, 2020 (Commentary, Reproducibility)

Challenges to the Reproducibility of Machine Learning Models in Healthcare, Beam et al.    
JAMA, 2020 (Commentary, Reproducibility)

What do medical students actually need to know about artificial intelligence?, McCoy et al.    
npj Digital Medicine, 2020 (Commentary, Medical education)

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies, Nagendra et al.    
BMJ, 2020 (Systematic review, Performance evaluation)

Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data, Andrews et al.    
Nature Protocols, 2020 (Tutorial, Bioinformatics)

Machine learning approaches to drug response prediction: challenges and recent progress, Adam et al.    
npj Precision Oncology, 2020 (Review, Bioinformatics, Drug response)

Prediction of gestational diabetes based on nationwide electronic health records, Artzi et al.
Nature Medicine, 2020 (Research, Clinical research)

Human-computer collaboration for skin cancer recognition, Tschandl et al.    
Nature Medicine, 2020 (Research, Computer vision)

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal, Wynants et al.    
BMJ, 2020 (Systematic review, Performance, Reproducibility)

Treating health disparities with artificial intelligence, Chen et al.    
Nature Medicine, 2020 (Commentary, Bias & fairness)

A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies, Degasperi et al.    
Nature Cancer, 2020 (Software package descriptor, Bioinformatics, Oncology)

Computational Methods for Single-Cell RNA Sequencing, Hie et al.    
Annual Reviews in Biomedical Data Science, 2020 (Review, Bioinformatics)

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist, Norgeot et al.
Nature Medicine, 2020 (Commentary, Reproducibility)

Video-based AI for beat-to-beat assessment of cardiac function, Ouyang et al.
Nature, 2020 (Research, Computer vision)

Effect of a Machine Learning–Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery - The HYPE Randomized Clinical Trial, Wijnberge et al.    
JAMA, 2020 (Research, Clinical trial)

Infectious Disease Research in the Era of Big Data, Kasson    
Annual Reviews in Biomedical Data Science, 2020 (Review, Public health, Big data, Infectious diseases)

Mining Social Media Data for Biomedical Signals and Health-Related Behavior, Correia et al.    
Annual Reviews in Biomedical Data Science, 2020 (Review, Big data, Social media)

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database, Benjamens et al.    
npj Digital Medicine, 2020 (Research, Implementation and Policy)

FDA: AI/ML-Based Software as a Medical Device (SaMD) Action Plan    
FDA, 2020 (Report, Policy)

Big data in psychiatry: multiomics, neuroimaging, computational modeling, and digital phenotyping, Ressler et al.
Neuropsychopharmacology, 2020 (Editorial)

How to Read Articles That Use Machine Learning Users’ Guides to the Medical Literature, Liu et al.    
JAMA, 2019 (Critical appraisal)

Machine Learning in Medicine, Rajkomar et al.    
NEJM, 2019 (Review)

High-performance medicine: the convergence of human and artificial intelligence, Topol    
Nature Medicine, 2019 (Review)

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, Christodoulou et al.    
Journal of Clinical Epidemiology, 2019 (Systematic review, Performance evaluation)

Dissecting racial bias in an algorithm used to manage the health of populations, Obermeyer et al.    
Science, 2019 (Research, Bias & fairness)

Artificial intelligence in global health: defining a collective path forward    
USAID, 2019 (White Paper)

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis, Liu et al.    
Lancet Digital Health, 2019 (Systematic review, Performance evaluation)

Humanizing Artificial Intelligence, Israni and Verghese    
JAMA, 2019 (Commentary, AI and the Medical Profession)

Ethical Dimensions of Using Artifiical Intelligence in Health Care, Rigby    
AMA Journal of Ethics, 2019 (Editorial, Ethics)

Potential Liability for Physicians Using Artificial Intelligence, Price et al.    
JAMA, 2019 (Commentary, Ethics, Regulation)

How Should Clinicians Communicate With Patients About the Roles of Artificially Intelligent Team Members?, Schiff and Borenstien    
AMA Journal of Ethics, 2019 (Commentary, Ethics)

Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence, Liang et al.
Nature Medicine, 2019 (Research, Natural language processing)

Do no harm: a roadmap for responsible machine learning in healthcare, Wiens et al.    
Nature Medicine, 2019 (Commentary, Implementation)

A clinically applicable approach to continuous prediction of future acute kidney injury, Tomasev et al.
Nature, 2019 (Research, Clinical research)

Guidelines for reinforcement learning in healthcare, Gottesman et al.    
Nature Medicine, 2019 (Commentary, Reinforcement learning)

Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise, Feldman et al.    
JAMIA, 2019 (Research, Natural language processing, Infectious diseases, Public health) 

Adversarial attacks on medical machine learning, Finlayson et al.    
Science, 2019 (Perspective, Cybersecurity)

Identification of Anonymous MRI Research Participants with Face-Recognition Software, Schwarz et al.
NEJM, 2019 (Research, Cybersecurity, Privacy)

Exploring single-cell data with deep multitasking neural networks, Amodio et al.    
Nature Methods, 2019 (Software package descriptor, Bioinformatics)

Deep learning for cellular image analysis, Moen et al.    
Nature Methods, 2019 (Review, Bioinformatics)

How precision medicine and screening with big data could increase overdiagnosis, Vogt et al.    
BMJ, 2019 (Commentary, Performance evaluation, Regulation)

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis, Seymour et al.    
JAMA, 2019 (Research, Clinical research, Unsupervised learning)

Longitudinal multi-omics of host–microbe dynamics in prediabetes, Zhou et al.    
Nature, 2019 (Research, Bioinformatics)

Current best practices in single‐cell RNA‐seq analysis: a tutorial, Luecken et al.    
Molecular Systems Biology, 2019 (Review, Bioinformatics)

Childhood cerebellar tumours mirror conserved fetal transcriptional programs, Vladoiu et al.    
Nature, 2019 (Research, Bioinformatics, Oncology)

Determining cell type abundance and expression from bulk tissues with digital cytometry, Newman et al.
Nature Biotechnology, 2019 (Software package descriptor, Bioinformatics)

Comprehensive Integration of Single-Cell Data, Stuart et al.    
Cell, 2019 (Software package descriptor, Bioinformatics)

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk, Zhou et al.
Nature Genetics, 2019 (Research)

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap, Reimand et al.    
Nature Protocols, 2019 (Tutorial, Bioinformatics)

A field guide for the compositional analysis of any-omics data, Quinn et al.    
Gigascience, 2019 (Tutorial, Bioinformatics)

Digital epidemiology: what is it, and where is it going?, Salathe et al.    
Life Sciences, Society, and Policy, 2018 (Review, Public health, Infectious diseases)

Social media interventions for precision public health: promises and risks, Dunn et al.    
npj Digital Medicine, 2018 (Review, Social media, Public health)

Precision” Public Health — Between Novelty and Hype, Chowkwanyun et al.
NEJM, 2018 (Perspective, Public health, Big data)

Machine learning in medicine: Addressing ethical challenges, Vayena et al.  
PLoS Medicine, 2018 (Review, Ethics)

What This Computer Needs Is a Physician - Humanism and Artificial Intelligence, Verghese et al.    
JAMA, 2018 (Commentary, AI and the Medical Profession)

The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care, Komorowski et al.    
Nature Medicine, 2018 (Research, Reinforcement learning)

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery, Lundberg et al.
Nature Biomedical Engineering, 2018 (Research, Clinical research)

Artificial intelligence in surgery, Hashimoto et al.    
Annals of Surgery, 2018 (Review, Surgical Practice)

Clinical decision support in the era of artificial intelligence, Shortliffe et al.    
JAMA, 2018 (Commentary, Decision Support Systems)

Next-Generation Machine Learning for Biological Networks, Camacho et al.
Cell, 2018 (Review, Bioinformatics)

Deep generative modeling for single-cell transcriptomics, Lopez et al.    
Nature Methods, 2018 (Software package descriptor, Bioinformatics)

Dermatologist-level classification of skin cancer with deep neural networks, Esteva et al.
Nature, 2017 (Research, Computer vision)

Computational nosology and precision psychiatry, Friston et al.    
Computational Psychiatry, 2017 (Research, Dynamical systems, Bayesian modeling)

Machine learning and the profession of medicine, Darcy et al.  
JAMA, 2016 (Commentary, AI and the Medical Profession)

Adapting to AI - Radiologists and Pathologists as Information Specialists, Jha and Topol
JAMA, 2016 (Commentary, AI and the Medical Profession)

Machine Learning in Medicine, Deo
Circulation, 2015 (Review)

Robust enumeration of cell subsets from tissue expression profiles, Newman et al.    
Nature Methods, 2015 (Software package descriptor, Bioinformatics)

A targeted real-time early warning score (TREWScore) for septic shock, Henry et al.    
Science Translational Medicine, 2015 (Research, Clinical research, Probabilistic ML)

Similarity network fusion for aggregating data types on a genomic scale, Wang et al.    
Nature Methods, 2014 (Software package descriptor, Bioinformatics)