Leading publications and more for machine learning in healthcare, all resource linked here.


Multimodal Machine Learning for Automated ICD Coding General

Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur MD, Frank Papay MD, Ashish K. Khanna MD, Jacek B. Cywinski MD, Kamal Maheshwari MD, Pengtao Xie, Eric Xing
Innovative multimodal machine learning modal with high accuracy for automated generation of diagnosis and associated ICD-10 codes.

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis  General

Xiaoxuan Liu, MBChB, Livia Faes, MD, Aditya U Kale, MBChB , Siegfried K Wagner, BMBCh , Dun Jack Fu, PhD, Alice Bruynseels, MBChB, et al.
A systematic analysis of studies comparing deep learning models against human performance for diagnosis.

Alex Zhavoronkov,et al.

Drug development using machine learning-a real life example


Evaluation of  of AutoML for medical image classification models.


An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction Cardiology

Zachi I Attia, MS,Peter A Noseworthy, MD,et al.

Identification of patients with episodes of  Atrial Fibrillation during normal sinus rhythm using convolutional neural network.

A clinically applicable approach to continuous prediction of future acute kidney injury Nephrology

Nenad Tomašev,Xavier Glorot

An artificial Intelligence algorithm to identify patients at risk for acute kidney injury as early as 48 hours before onset.


Artificial Intelligence in Critical Care  Critical care

Mathur, Piyush, MD, FCCM; Burns, Michael L., MD, PhD

A primer on AI and review of AI research/applications in critical care.

Artificial Intelligence for Clinical Trial Design Research

Stefan Harrer,Pratik Shah,Bhavna Antony,Jianying Hu

Comprehensive review on use of machine learning for clinical research and trials.

An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction  Cancer

Bin Lou, PhD ,Semihcan Doken, BA ,Tingliang Zhuang, PhD ,Danielle Wingerter, BE ,Mishka Gidwani, BS ,Nilesh Mistry, PhD, Lance Ladic, PhD ,Ali Kamen, PhD,Mohamed E Abazeed, MD

Personalized radiotherapy dose prediction using CT scan images.


Deep Learning to Assess Long-term Mortality From Chest Radiographs Imaging/Radiology

Michael T. Lu, MD, MPH; Alexander Ivanov, BS; Thomas Mayrhofer, PhD; et al

Use of Deep Learning for prognosis using Chest X-Rays.

Machine learning as a supportive tool to recognize cardiac arrest in emergency calls Emergency Medicine

Fredrik Folke,Annette Kjær Ersbøll,Helle Collatz Christensen,et al.

Use of machine learning to recognize out of hospital cardiac arrests from dispatcher calls.

Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports  Cancer

Kenneth L. Kehl, MD, MPH; Haitham Elmarakeby, PhD; Mizuki Nishino, MD, MPH; et al

Use of natural language processing to predict outcomes more efficiently in cancer patients.

Outcome Prediction in Postanoxic Coma With Deep Learning Critical care

Tjepkema-Cloostermans, Marleen C., PhD; da Silva Lourenço, Catarina, BSc; Ruijter, Barry J., MD, PhD; Tromp, Selma C., MD, PhD; Drost, Gea, MD, PhD; Kornips, Francois H. M., MD; Beishuizen, Albertus, MD, PhD; Bosch, Frank H., MD, PhD; Hofmeijer, Jeannette, MD, PhD; van Putten, Michel J. A. M., MD, PhD

Outcome prediction using EEG waveform derived deep learning model in patients who had cardiac arrest.

Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems  Critical care

Interesting article combining aspects of Telemedicine and machine learning.

Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature General

Triantafyllidis AK, Tsanas A

An exhaustive review of literature on real life interventions using machine learning applications.


Multitask Learning and Benchmarking with Clinical Time Series Data  General

Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg, Aram Galstyan

Interesting use of multimodal architecture and feature engineering for developing prediction models using MIMIC data.


Radiology:Artificial Intelligence Imaging/Radiology

Radiology Society of North America's journal on Artificial Intelligence.Key Imaging publications on AI published here periodically.


The Lancet Digital Health  General

Lancet's digital health focused Journal with new publications posted periodically.

Machine Learning in Medicine  General

Alvin Rajkomar, M.D.,Jeffrey Dean, Ph.D., Isaac Kohane, M.D., Ph.D.

A basic but thorough review of machine learning modeling for healthcare and its challenges.

Applications of machine learning in drug discovery and development  General

Jessica Vamathevan,Dominic Clark,Paul Czodrowski,Ian Dunham,Edgardo Ferran,George Lee,Bin Li,Anant Madabhushi,Parantu Shah,Michaela Spitzer & Shanrong Zhao 

An overview of machine learning applications and challenges in drug discovery.

Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs  Imaging/Radiology

Eui Jin Hwang, MD; Sunggyun Park, MS; Kwang-Nam Jin, MD; et al

Moving from development to validation and generalizability of deep learning algorithms for Chest X-Ray interpretation.

Artificial intelligence in healthcare: past, present and future  General

Fei Jiang, Yong Jiang, Hui Zhi,  Yi Dong,  Hao Li,  Sufeng Ma,  Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang

Review of ML in healthcare.A good primer and interesting data on ML applications.

An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care  Critical care

Joon Lee, Roger G Mark

Prediction of hypotension in ICU using waveform and other data.

Ease of adoption of clinical natural language processing software: An evaluation of five systems  General

KaiZheng V.G. , Vinod Vydiswaran, Yang Liu, Yue Wang, Amber Stubbs, Özlem Uzuner, Anupama E. Gururaj, Samuel Bayer, John Aberdeen, Anna Rumshisky,Serguei Pakhomov, Hongfang Liu, Hua Xu.

Excellent evaluation of NLP in healthcare and its challenges.

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions  General

Wei-Hsuan Lo-Ciganic, PhD ; James L. Huang, PhD ; Hao H. Zhang, PhD ; et al

Machine learning models for prediction of at risk population for opioid overdose.

Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data  Neurology

Nikhil Bhagwat ,Joseph D. Viviano,Aristotle N. Voineskos,M. Mallar Chakravarty 

Modeling of symptoms to predict  trajectory of Alzheimer's disease.


Fast and accurate view classification of echocardiograms using deep learning  Cardiology

Ali Madani,Ramy Arnaout,Mohammad Mofrad Rima Arnaout 

Accurate echocardiogram view classification using deep learning

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification  Neurology

Igor O. Korolev, Laura L. Symonds, Andrea C. Bozoki

Using multimodal technique to predict progression of Alzheimer's dementia.


Machine learning in healthcare: Nature biomedical engineering  General

Continuously updated Nature journal's collection of  machine learning in healthcare articles.


KenSci publications  General

A comprehensive list of publications of research articles from experts at KenSci.Ranging from predictive analytic applications in healthcare to various frameworks for value based healthcare using machine learning.


Also watch webinar on : "Explainable Machine Learning models for Healthcare AI."

High-performance medicine: the convergence of human and artificial intelligence  General

Eric J.Topol

Excellent article from Dr.Topol,describing advances made in AI/ML application amongst various medical specialities.

BrainX Community's  2018 AI/ML Year in Review is a great supplement to this article.


The reproducibility crisis in the age of digital medicine  General

Aaron Stupple,David Singerman,Leo Anthony Celi 

Reproducibility and validation issues with AI/ML in healthcare addressed in this article.

Machine learning and medical education  General

Vijaya B. Kolachalama,Priya S. Garg 

Perspective on integrating ML training into medical curriculum.

An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets  Neurology

Hyunkwang Lee,Sehyo Yune,Mohammad Mansouri,Myeongchan Kim,Shahein H. Tajmir,Claude E. Guerrier,Sarah A. Ebert,Stuart R. Pomerantz,Javier M. Romero,Shahmir Kamalian,Ramon G. Gonzalez,Michael H. Lev & Synho Do 

Deep learning for ICH detection, classification with explainability.

Publications from Laboratory of Quantitative Imaging and Artificial Intelligence (QIAI)  General/Imaging

Excellent and exhaustive research  from Daniel L. Rubin, MD, MS lab at Stanford University, especially focused on Imaging.

Article collection on ML/AI with focus on Imaging/Radiology  Imaging

Good collection of ML articles posted by Canadian Association of Radiologists.Some really good as primers.

Image reconstruction by domain-transform manifold learning  Imaging

Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen & Matthew S. Rosen

AUTOMAP:Innovative image reconstruction process using machine learning.

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU  Critical Care

Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG.

AISE(Artificial Intelligence Sepsis Expert) for Sepsis prediction.

Machine learning for real-time prediction of complications in critical care: a retrospective study  Critical Care

Alexander Meyer, Dina Zverinski, Boris Pfahringer, Jörg Kempfert, Titus Kuehne, Simon H Sündermann, Christof Stamm, Thomas Hofmann, Volkmar Falk, Carsten Eickhoff

Machine learning model for prediction of complications in critically ill patients.

The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care  Critical Care

Matthieu Komorowski,Leo A. Celi,Omar Badawi,Anthony C. Gordon & A. Aldo Faisal 

Machine learning to optimize management of sepsis.

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery   Anesthesiology/Surgery

Maheshwari K, Cywinski J, Mathur P, Cummings KC 3rd, Avitsian R, Crone T, Liska D, Campion FX, Ruetzler K, Kurz A.
Machine learning used to identify  variations in clinical practice.

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression.  Behavioral sciences

Nikolaos Koutsouleris, MD; Lana Kambeitz-Ilankovic, PhD; Stephan Ruhrmann, MD; et al

Machine learning predictive model using multimodal data for prediction of functional outcomes in patients with psychiatric disorders.

Informatics, Data Science, and Artificial Intelligence  General

Lisha Zhu, PhD ; W. Jim Zheng, PhD

Viewpoint on relationship of health informatics, data and AI.

Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response  Cancer/Imaging

Nathaniel Braman, Prateek Prasanna, Mehdi Alilou, Niha Beig, Anant Madabhushi

Radiomic biomarkers for oncologic diagnosis and treatment management.

Feature Driven Local Cell Graph (FeDeG): Predicting Overall Survival in Early Stage Lung Cancer  Cancer/Imaging

Cheng Lu, Xiangxue Wang, Prateek Prasanna, German Corredor, Geoffrey Sedor, Kaustav Bera, Vamsidhar Velcheti, Anant Madabhushi

New feature based histopathological approach to predict survival in Lung cancer.

Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer  Cancer/Imaging

Germán Corredor, Xiangxue Wang, Yu Zhou, Cheng Lu, Pingfu Fu, Konstantinos N Syrigos, David L. Rimm, Michael Yang, Eduardo Romero, Kurt A. Schalper, Vamsidhar Velcheti and Anant Madabhushi

More and better features being utilized for oncologic disease management.


Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning  Cancer/Pathology

Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L. Moreira, Narges Razavian & Aristotelis Tsirigos 

Use of convolutional neural networks for classification in Pathology.

Anesthesiology, October 2018 edition focused on Artificial Intelligence.Articles with links below:

Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark  Anesthesiology

Michael R. Mathis, M.D.; Sachin Kheterpal, M.D., M.B.A.; Kayvan Najarian, Ph.D.

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality  Anesthesiology

Christine K. Lee, M.S., Ph.D.; Ira Hofer, M.D.; Eilon Gabel, M.D.; Pierre Baldi, Ph.D.; Maxime Cannesson, M.D., Ph.D.

Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis  Anesthesiology

Feras Hatib, Ph.D.; Zhongping Jian, Ph.D.; Sai Buddi, Ph.D.; Christine Lee, M.S.; Jos Settels, M.S.; et al

Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension  Anesthesiology

Samir Kendale, M.D.; Prathamesh Kulkarni, Ph.D.; Andrew D. Rosenberg, M.D.; Jing Wang, M.D., Ph.D.

AIMed Magazine #04 – Cloud Computing & Big Data  General

AIMed magazine edition focused on healthcare data.

  • Data is the new fuel: a note by Dr Anthony Chang, MD, MBA, MPH, MS
  • The challenge of data protection diversity for AI research by Leo Anthony Celi, MD
  • Great expectations for machine learning in EHRs by Piyush Mathur, MD, FCCM,Ashish Khanna,MD,FCCM
  • Empathy, Values + AI by Jules Sherman, MFA
  • Building a strong future: Diversity & Inclusion in AIMed by Crystal Valentine, PhD; Fran Ayalasomayajula, MPH, PMP; Maxine Mackintosh, PhD; Claire Novorol, PhD


Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data  General

Nrupen A. Bhavsar, PhD; Aijing GaO, MS, Matthew Phelan, MS; et al

While most studies show the importance of socioeconomic determinants this one demonstrates no value over EHR data.

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges  Cancer/Imaging

Hesham Elhalawami et al.

Lessons learnt from challenge based research in head and neck oncology machine learning applications.

The eICU Collaborative Research Database, a freely available multi-center database for critical care research   General

Tom J. Pollard, Alistair E. W. Johnson, Jesse D. Raffa, Leo A. Celi, Roger G. Mark & Omar Badawi

Article from Nature Journal on description of the EICU Collaborative Research database.

Workshop on Artificial Intelligence in Medical Imaging to foster innovative collaborations in applications for diagnostic medical imaging held by National Institute of Biomedical Imaging and Bioengineering (NIBIB).  General

Watch the videos from the workshop via links below for day 1 and day 2..

Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices  Ophthalmology

Michael D. Abràmoff  Philip T. Lavin, Michele Birch, Nilay Shah and James C. Folk

From retrospective research to pivotal trial leading to FDA approval.Landmark article for use of AI in healthcare, especially using imaging modalities.


Scalable and accurate deep learning with electronic health records  General

Alvin Rajkomar, et al.

Novel methods to use electronic health record(EHR) data for predictive analytics.A must read for anyone using EHR data.

Deep Lesion  Imaging

NIH release of a annotated 32,000 CT scan image dataset from 4400 unique patients for research and development.

DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

Ke Yan; Xiaosong Wang; Le Lu; Ronald M. Summers

Deep Learning: A Primer for Radiologists  Imaging

Detailed basic review on application of machine learning in Radiology.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI  Cancer

Nathaniel M. Braman, Maryam Etesami , Prateek Prasanna , Christina Dubchuk , Hannah Gilmore , Pallavi Tiwari , Donna Pletcha and Anant Madabhushi

Radiomics using DCE-MRI for  prediction of chemotherapy response.

Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings  Cancer/Imaging

Soumya Ghose, Rakesh Shiradkar, Mirabela Rusu , Jhimli Mitra, Rajat Thawani , Michael Feldman, Amar C. Gupta, Andrei S. Purysko, Lee Ponsky & Anant Madabhushi

Using MRI to look at Prostate shapes and predict recurrence of Prostate Cancer.

Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma  Cancer/Imaging

Niha Beig, Jay Patel, Prateek Prasanna, Virginia Hill, Amit Gupta, Ramon Correa, Kaustav Bera, Salendra Singh, Sasan Partovi, Vinay Varadan, Manmeet Ahluwalia, Anant Madabhushi & Pallavi Tiwari

Use of radiomic features on MRI to determine prognosis amongst Glioblastoma patients.

Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging  Cancer/Imaging/Pathology

Jacob Antunes, PhD , Satish Viswanath PhD , Justin T. Brady MD , Benjamin Creshaw MD , Pablo Ros MD , Scott Steele MD , Conor P. Delaney MD , Raj Paspulati MD , Joseph Willis MD , AnantMadabhushi, PhD

Artificial Intelligence in Cardiology  Cardiology

Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg, Khader Shameer, Riccardo Miotto, Mohsin Ali, Euan Ashley and Joel T. Dudley

Detailed discussion of various machine learning techniques and applications in context of cardiovascular disease.Very informative for clinicians who are learning the basics of machine learning and its application in healthcare.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs  Ophthalmology

Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD; Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB;Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD

A landmark article demonstrating high accuracy of image recognition technology to arrive at a diagnosis.

Machine learning in cardiovascular medicine: are we there yet?  Cardiology

Shameer K,Johnson KW,Glickberg BS,Dudley JT,Sengupta PP

New publications coming everyday exploring the opportunities for application of machine learning in cardiovascular medicine.Opportunities and challenges discussed in this article.

Improving Palliative Care with Deep Learning  General

This article discusses use of electronic health record data to predict mortality and thereby identifying patients who would benefit from palliative care.

What This Computer Needs Is a Physician. Humanism and Artificial Intelligence  General

Abraham Verghese, MD; Nigam H.Shah,MBBS,PhD;Robert A. Harrington, MD

View point in JAMA,discussing the need for improving Electronic Health Records exploring  possibilities using Machine learning and Artificial Intelligence while also addressing their pitfalls.

Towards Automated ICD Coding Using Deep Learning  General

Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, Eric P. Xing

Deep learning techniques applied on electronic health record data to generate ICD-10 diagnosis with high grade of accuracy.

Artificial intelligence applications in the intensive care unit

Critical Care

C. William Hanson III, MD, FCCM; Bryan E. Marshall, MD, FRCP, FRCA

Review from 2001 of AI applications in data rich extremely complex environment of Intensive Care Unit.Opportunities to apply advanced technologies in healthcare environment were identified many years ago.