BRAINX COMMUNITY LIVE !

November 2019 session.

Dr.Aziz Nazha,medical director, center for clinical artificial intelligence at Cleveland Clinic discussed the current state of machine learning and artificial intelligence in healthcare.He showed how significant progress has been made in both the scientific field of AI and machine learning over last many years and its growing impact on healthcare.Narrating some of the challenges we face in AI's application he also showcased a roadmap and some of the projects undertaken by him and the center for clinical artificial intelligence at Cleveland Clinic.

His showcased results of his research focused on leukemic disorders and application of ML to help improve diagnosis, management and prognosis.Dr.Nazha demonstrated translation of this research into web application for personalized prediction models.

Genomic Biomarkers to Predict Resistance to Hypomethylating Agents in Patients With Myelodysplastic Syndromes Using Artificial Intelligence

Furthermore, Dr.Nazha, demonstrated how Cleveland Clinic was undertaking many projects including those on decreasing readmissions, large database analysis and many other specialty specific ones through collaborations both internally and with external partners.


BRAINX COMMUNITY LIVE !

September 2019 session.

Artificial Intelligence is presenting healthcare with unprecedented solutions for better diagnosis, treatment options and prognosis.Dr Pallavi Tiwari , Assistant Professor of Biomedical Engineering ,director of Brain Image Computing laboratory at Case Western Reserve University. and an associate member of the Case Comprehensive Cancer Center presented her lab’s work focused on developing radiomic (extracting computerized sub-visual features from radiologic imaging), radiogenomic (identifying radiologic features associated with molecular phenotypes), and radiopathomic (radiologic features associated with pathologic phenotypes) techniques to capture insights into the underlying tumor biology as observed on non-invasive routine imaging.

She discussed clinical applications of this work for predicting disease outcome, recurrence, progression and response to therapy specifically in the context of brain tumors. She also discussed current efforts in developing new radiomic features for post-treatment evaluation and predicting response to chemo-radiation treatment.

She also presented her work which includes machine learning solutions to differentiate between tumors and non-tumor areas, developing new features to characterize tumors better and novel multimodal approaches to strengthen the accuracy of diagnosis and prognosis.

These new innovative techniques are now presenting our clinicians and patients with less invasive, more accurate and cheaper treatment options.From avoidance of brain biopsy to radiomics guided biopsies, better decision support for clinicians is likely to result in better treatment outcomes.

She concluded with a discussion on her lab’s recent findings in AI + experts, in the context of a clinically challenging problem of distinguishing benign radiation effects from tumor recurrence on routine MRI scans.

BIO:

Dr. Tiwari got her PhD from Rutgers University in 2012.In 2016, she founded the Brain Image Computing lab at Case Western Reserve University. Her research interests lie in pattern recognition, data mining, and image analysis for automated personalized medicine solutions using radiological imaging in brain tumors and neurological disorders. 

Over the last 13 years, her research has been focused on developing novel image analysis methods for diagnosis, prognosis, and evaluating treatment response of different types of cancers (prostate, breast, lung) and neuro-imaging applications including brain tumors, epilepsy, and cancer pain. Her research has so far evolved into over 30 peer-reviewed publications, 35 peer-reviewed abstracts, and 7 patents (2 issued, 5 pending). 

In 2017, her work was recognized by Case Western as one of the most notable research works in the university. Her research has also been covered by various news outlets including Crains Cleveland, NSF Science Now, and Science Daily. She has received certification of commendation from the General Assembly of the State of Ohio and from Ohio Secretary of State for her work in brain tumors.  She has been a recipient of several scientific awards, most notably being named as one of 100 women achievers by Government of India for making a positive impact in the field of Science and Innovation.  In 2018, she was selected as one of Crain’s Business Cleveland Forty under 40.

Dr. Tiwari is currently leading a team of researchers on multiple projects using novel radiomics and image informatics for prognosis and treatment evaluation in adult and pediatric brain tumors, autism, as well as other neurological disorders. She has been an invited and plenary speaker on topics relating to radiomics and radiogenomics at forums including MICCAI, Society of Neuro-Oncology, SPIE, and radiology workshops throughout the country.  Her research is funded through the Department of Defense, Dana Foundation, state of Ohio, and Case Comprehensive Cancer Center. She also serves on various leadership capacities (i.e. associate editor, committee member, scientific reviewer) in the medical image informatics community.

 

BRAINX COMMUNITY LIVE !

August 2019 session.

 

Steve Roesing, CEO, ASMGi, presented valuable insights on operationalizing artificial intelligence and machine learning applications in healthcare using, "Practical Innovation".

While many of us worry about the accuracy of our machine learning models or clinical application,one of the biggest challenges is having a strategy to deploy and sustain a machine learning model in an active health care environment.According to the current research around 85% of the machine learning models are never used in healthcare.

He shared his strategy of Practical Innovation and the "crawl-walk-run" model for implmentation of these machine learning models.The "crawl" phase is focused on answering the basic questions at a small scale whether the model is possible and of good quality.During the ,"walk" phase, it's tested as a pilot and during the "run" phase the model is deployed into the active healthcare IT environment with active support.

For all involved in developing commercial level sustainable and scalable models it is important to engage teams from healthcare IT to build a strategy for deployment from the very beginning to avoid failures.

 

BRAINX COMMUNITY LIVE !

June 2019 session.

An engaging discussion held on challenges in application of artificial intelligence in healthcare.Various perspectives including challenges with data quality, machine learning modeling and most importantly ethical and legal challenges were highlighted.

Watch the full video: https://www.youtube.com/watch?v=hGySfW9zIUE&feature=youtu.be

Many thanks to the team of the Center of Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University for hosting and co-organizing this event with BrainX Community.

Abstracts from our 3 eminent speakers are available below.

Dr.Ashish Khanna

Title: Solving healthcare challenges with machine learning and artificial intelligence-clinical  challenges.

Big data analytics fit well into the many thousands of numbers generated by patient monitors in the ICU and the general care floor. As anesthesiologists and perioperative physicians, we have been tasked with taking care of patients as they transition from the critical care unit to the general care floor. It is essential that we are able to accurately determine and predict the nature, scope and extent of cardiopulmonary deterioration in all of these patient care environments. Dr.Khanna’s talk was centered on clinical questions, research methodology and the dearth of granular data to drive predictive analytics models as they exist today. He gave examples of the intensive care unit where at least one blood pressure reading (verified and entered in the EMR) is at best available for a data pull, whereas continuous streaming chunks of unverified data stored in monitoring systems are purged out every 3 days or so.

Hypotension in the postoperative critically ill patient is of critical importance. The conventional blood pressure to defend in the ICU has always been stated to be at least 65mmHg. This is based on RCT verified data, and other observational work. Our recent work across 9,000 ICU patients in a 110 ICUs in the United States, showed that in fact higher pressures may be needed in the critically ill patient. Herein, we showed that the earliest sign of harm begins at a MAP of 85mmHg, for myocardial injury, acute kidney injury & mortality and for AKI and mortality increases progressively down to a MAP of 55mmHg. We subsequently examined a 3,000-patient cohort of postoperative critically ill patients admitted to the surgical ICU from the operating room. We identified a strong non-linear association with the lowest MAP on any given day in the ICU and a composite of myocardial injury and or mortality and a secondary outcome of acute kidney injury. These associations were identified at blood pressure thresholds that were previously considered normal. 

Similarly, current data on the general care floor is that entered via conventional spot checks based monitoring protocols, that limit us to a once in 4-6 hours’ time interval of vital signs. We know that postoperative cardiorespiratory events are preceded by 6-12 hours of vital signs abnormalities; and some may yet be largely unpredictable. Knowing the extent of the problem and the lack of predictability of the same, the only logical solution may be continuous, automated, multi-parameter monitoring of cardiorespiratory parameters on all patients on the regular nursing floor. It remains to be seen whether this practice will make a difference in the early detection of patient decline and clinical outcomes along with the utilization of emergency response teams in hospital systems. Keeping in mind that alarm fatigue secondary to the many false alarms that may be generated from these systems may be a real problem, an initial scientific question should also integrate the investigation of the extent of nursing responses to continuous monitoring systems on the ward. However, the amount of big data generated from continuous monitoring will need to be curated and fitted into AI platforms that can create a strong efferent limb of provider response based on mitigation of false alarms and creation of monitoring stations that allow continuous proactive surveillance of deterioration.

Reference:Automated continuous noninvasive ward monitoring: future directions and challenges.Khanna AK, et al. Crit Care. 2019.

 

Rakesh Shiradkar,PhD.

Title: Machine Learning Challenges in Medical Image Analysis

 

Machine Learning is increasingly gaining prominence in the Medical Imaging community due its potential benefits in numerous clinical tasks. There are, however, challenges at a number of levels which machine learning scientists in medical image analysis face, that need to be addressed. 

This talk brings out some of the commonly faced challenges including those with respect to data acquisition, pre-processing, choice of learning strategies, validation, communicating with clinicians and interpretation of results. Potential strategies for addressing some of these are also touched upon.

Professor Sharona Hoffman

Title:Artificial Intelligence and Ethics

 

Artificial intelligence (AI) is generating significant excitement and holds promise to improve treatment outcomes, but it also has important ethical and legal implications.  First, are privacy and discrimination concerns.  Data generated by AI will be incorporated into electronic health records and thus may be vulnerable to privacy breaches or be obtained by third parties through disclosure authorizations.  Employers, insurers, and others may use AI data for discriminatory purposes and make adverse decisions regarding data subjects.

  Second, AI may exacerbate health disparities because resource-poor facilities may not have access to it.  Use of racial or ethnic identification as a variable in AI analysis may also lead to stigmatization of certain groups as more diseased or biologically inferior to others. Third, clinicians should be wary of causing psychological harms by disclosing predictions about patients’ long-term health outlook (e.g. future cognitive decline).  The potential for erroneous predictions resulting from training data that are of poor-quality, biased, or otherwise flawed augments this concern.  Finally, physicians worry about the impact AI will have on the physician-patient relationship and about its liability ramifications.  This talk analyzed all of these concerns and outlined strategies to address them.

Reference:“What Genetic Testing Teaches about Predictive Health Analytics” to be published in the North Carolina Law Review in late 2019. 

 

BRAINX COMMUNITY LIVE !

May 2019 session.

At the first anniversary event of BrainX Community Live, Dr.Michael Burns made a presentation on “Practical Applications of Machine Learning and Natural Language Processing in Perioperative Medicine”. He is currently an anesthesiologist at the University of Michigan and clinical analytics researcher within the Multicenter Perioperative Outcomes Research (MPOG) group. He discussed current research and applications of Artificial Intelligence in perioperative patient care areas. These include applications of predictive analytics with neural networks and deep learning to guide diagnosis and treatment, especially useful in critically ill patients.

He also discussed his current work within MPOG with perioperative applications of machine learning models including classification models for Current Procedure Terminology (CPT) codes in medical procedural billing applications. Michael demonstrated that significant improvement can be achieved in classification of CPT codes using natural language processing and feature engineering. His work can be very impactful in increasing the speed, accuracy, and revenue capture compared to existing healthcare billing systems.

Links to his latest publications/presentations are below:

Mathur, P. and M. L. Burns (2019). "Artificial Intelligence in Critical Care." Int. Anesthesiology Clinics 57(2): 89-102. PMID: 30864993

"ProcedureView (ProView): Analyzing and Presenting Anesthesiology Case Data for Providers using Machine Learning" - Society for Technology in Anesthesia (2019)

"Clustering Anesthesiology Case Data for Machine Learning" - Machine Learning for Healthcare (2018)

 

 

BRAINX COMMUNITY LIVE !

April 2019 session.

Dr.Anant Madabhushi, was the presenter for the April 2019,BrainX Community live event.He is currently, Director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD) and the F. Alex Nason Professor II in the Departments of Biomedical Engineering, Pathology, Radiology, Radiation Oncology, Urology, General Medical Sciences, and Electrical Engineering and Computer Science at Case Western Reserve University.

His presentation was on innovative use of machine learning techniques to aid with diagnosis, treatment and prognostication of cancer.He and his team have developed unique cost-effective solutions and pathbreaking techniques to provide less invasive, personalized and precise care to  cancer patients.They are currently researching application of these techniques to healthcare areas other than cancer including cardiology.

The key aspect of his presentation was demonstration of feature engineering as a key ML technique in the field of pathomics and radiomics to generate new and actionable knowledge.This new knowledge is enhancing  decision making for management of patients and supporting clinicians by providing them with actionable and precise information.

It is truly remarkable to see how Dr.Madabhushi is changing the management of cancer patients by providing actionable information in near real time and with significant accuracy.His orientation to patient care and clinician engagement is an important reason for this collaborative success.

He also discussed challenges associated with applications of machine learning in healthcare including access to data,quality of data,reproducibility of results,legal and ethical dilemmas.

Abstract to his presentation, links to his center and publications are available below.

 

 

Title - "Artificial Intelligence in Radiology and Pathology: Implications for Precision Medicine"

Abstract - Traditional biology generally looks at only a few aspects of an organism at a time and attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and microenvironment, as seen in its histological image features, its 3 dimensional tissue architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission Tomography (PET), result in emergence of definable phenotypes. At the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University, we have been developing computerized knowledge alignment, representation, and fusion tools for integrating and correlating heterogeneous biological data spanning different spatial and temporal scales, modalities, and functionalities. These tools include computerized feature analysis methods for extracting subvisual attributes for characterizing disease appearance and behavior on radiographic (radiomics) and digitized pathology images (pathomics). In this talk I will discuss the development work in CCIPD on new radiomic and pathomic approaches for capturing intra-tumoral heterogeneity and modeling tumor appearance. I will also focus my talk on how these radiomic and pathomic approaches can be applied to predicting disease outcome, recurrence, progression and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers. Additionally I will also discuss some recent work on looking at use of pathomics in the context of racial health disparity and creation of more precise and tailored prognostic and response prediction models.

Learn more about CCIPD:

http://engineering.case.edu/centers/ccipd/

CCIPD publications:

http://engineering.case.edu/centers/ccipd/publications

 

 

BRAINX COMMUNITY LIVE !

March 2019 session.

This session titled, “Hands on Tech:Learning AI”,focused on giving a primer on how to get started with machine learning in healthcare.

A step by step approach on training  in machine learning with a healthcare focus was provided by 

Piyush Mathur MD, FCCM , Founder of BrainX Community,Anesthesiologist/Intensivist, Quality Improvement Officer,Anesthesiology Institute, Cleveland Clinic.

Anirban Bhattacharyya MD, MPH , Associate Staff Physician,Respiratory Institute,Cleveland Clinic

Ghaith Habboub MD , Spine Fellow, Neurology Institute,Cleveland Clinic

Whether it is to write the codes or just to get familiar this approach is helpful in becoming bilingual and to foster collaborations between clinicians and machine learning experts.

A copy of the presentation is below:

BrainX Community Live! March 2019

 

 

 

 

BRAINX COMMUNITY LIVE !

January 2019 session.

A review of 2018 publications on ML applications in healthcare was presented by Piyush Mathur MD, FCCM.

Review and a copy of the presentation are available below:

PDF of 2018 Year in Review:Machine Learning in Healthcare.

BrainX Community Live January 2019 event

 

BRAINX COMMUNITY LIVE !

December 2018 session.

Presented by Feras Hatib, PhD, Director R&D, Edwards Lifesciences and Bart Geerts MD PhD MSc MBA, Consultant anaesthetist and researcher at AMC-Academic Medical Center,Netherland.They discussed  their research on prediction of hypotension through arterial waveform analysis using machine learning algorithm.All the way from selection of waveform variables to  2 million plus feature selection and engineering.Now paving the path for clinical trials and adoption.

Their research was recently published in the Anesthesiology journal’s October edition focused on Artificial Intelligence.Link and details of their publication are below:

http://anesthesiology.pubs.asahq.org/article.aspx?articleid=2685008

BRAINX COMMUNITY LIVE !

November 2018 session.

Dr.Ghaith Habboub,Spine fellow,Cleveland Clinic, discussed challenges with scaling and generalizing artificial intelligence(AI) applications in healthcare.

He pointed out that most of the effort currently goes into pre-processing and processing of data.

Machine learning applications are being created but integration into current workflow to make them actionable remains another challenge. Dr.Habboub presented some solutions to integrate both applications developed within EHR(electronic health record) and from outside of EHR.

Containerized API(application programming interface) appear to have certain advantages for embedding and scaling AI applications.

Presentation slides available via link below:

Embedding and Scaling AI Models in Healthcare Applications.11_3_2018

Follow his work on GitHb repository via link below:

https://github.com/rocketheat/Kubeadm_Rocketheat

Journal club on “Scalable and accurate deep learning with electronic health records”,generated interesting discussion on processing of the data using FHIR(Fast Healthcare Interoperability Resource).Use of multimodal data and automated feature engineering seems to be a strength of this study.

https://www.nature.com/articles/s41746-018-0029-1

For more information,contact Dr.Habboub directly at habboug@ccf.org

 

BRAINX COMMUNITY LIVE !

October 2018 session.

Dr. Aziz Nazha, Assistant Professor, Taussig Cancer Center, Cleveland Clinic started the October, BrainX Community live event by making a case for artificial intelligence applications  in medicine.He then went on to showcase some of the prognostic models in cancer that have been developed by his team and applications there of in personalized medicine.Newer models  using traditional recommender systems can be developed which use genomic biomarkers to predict response to therapy in a much more accurate manner than traditional methods.He also showed how neural networks can help in drug development and to target therapies.Integrating the work of pathomics and radiomics into cancer management along with electronic medical records data will hopefully build better treatment care paths in the future for more effective personalized care.

The Complexity of Interpreting Genomic Data in Patients with Primary and Secondary Acute Myeloid Leukemia (AML)
Aziz Nazha, Ahmad Zarzour, Tomas Radivoyevitch, Hetty E. Carraway, Jennifer S. Carew, Cassandra M Hirsch, Kassy E Kneen, Bartlomiej Przychodzen, Bhumika J. Patel, Michael Clemente, Srinivasa R. Sanikommu, Matt Kalaycio, Jaroslaw P. Maciejewski and Mikkael A. Sekeres
The Revised International Prognostic Scoring System "Molecular" (IPSS-Rm), a Validated and Dynamic Model in Treated Patients with Myelodysplastic Syndromes (MDS)
Aziz Nazha, Mayur Subhash Narkhede, Tomas Radivoyevitch, Matt Kalaycio, Bhumika J. Patel, Aaron T. Gerds, Sudipto Mukherjee, Michael Clemente, Cassandra M Hirsch, Anjali S. Advani, Bartlomiej Przychodzen, Hetty E. Carraway, Jaroslaw P. Maciejewski and Mikkael A. Sekeres
The efficacy of current prognostic models in predicting outcome of patients with myelodysplastic syndromes at the time of hypomethylating agent failure
Aziz Nazha, Rami S. Komrokji, Guillermo Garcia-Manero, John Barnard, Gail J. Roboz,David P. Steensma, Amy E. DeZern, Katrina Zell,Cassie Zimmerman, Najla Al Ali, Elias Jabbour,Molly D. Greenberg, Hagop M. Kantarjian, Jaroslaw P. Maciejewski, Alan F. List, and Mikkael A. Sekeres, On behalf of the MDS Clinical Research Consortium
Haematologica. 2016 Jun; 101(6): e224–e227.

BRAINX COMMUNITY LIVE !

September 2018 session.

Dr.Maheshwari, Director, Center for Perioperative Intelligence, shared his work on integrating data  intelligently into real time decision making to change perioperative outcomes thereby creating value.

He highlighted management of  important variables such as blood pressure in real time intra-operatively using workflow integrated advanced decision support.Features that have not been considered have the potential to be developed using machine learning and deep learning moving beyond simple models such as measurement of mean arterial pressure or  volume status alone.

Some of the references he cited are available below:

Relationship between Intraoperative Hypotension, Defined by Either Reduction from Baseline or Absolute Thresholds, and Acute Kidney and Myocardial Injury after Noncardiac Surgery: A Retrospective Cohort Analysis

Vafi Salmasi, M.D.; Kamal Maheshwari, M.D., M.P.H. ; Donsheng Yang, m.A.; Edward J. Mascha, Ph.D.;Asha Singh, M.D.; et al

http://anesthesiology.pubs.asahq.org/article.aspx?articleid=2579833

Association of Postoperative High-Sensitivity Troponin Levels With Myocardial Injury and 30-Day Mortality Among Patients Undergoing Noncardiac Surgery

Writing Committee for the VISION Study Investigators

https://jamanetwork.com/journals/jama/fullarticle/2620089

The association of hypotension during non-cardiac surgery, before and after skin incision, with postoperative acute kidney injury: a retrospective cohort analysis.

Maheshwari K, Turan A, Mao G, Yang D, Niazi AK, Agarwal D, Sessler DI, Kurz A.

https://onlinelibrary.wiley.com/doi/abs/10.1111/anae.14416

A Randomized Trial of Continuous Noninvasive Blood Pressure Monitoring During Noncardiac Surgery.

Maheshwari K, Khanna S, Bajracharya GR, Makarova N, Riter Q, Raza S, Cywinski JB, Argalious M, Kurz A, Sessler DI.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072385/

The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients.

Maheshwari K, Nathanson BH, Munson SH, Khangulov V, Stevens M, Badani H, Khanna AK, Sessler DI.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013508/


BRAINX
 COMMUNITY LIVE !

August 2018 session.

Our August session featured Pengtao Xie, PhD, who has recently completed his PhD from the Machine Learning Department at Carnegie Mellon University. Director of Data Services and Solutions and Research Scientist at Petuum,Inc.He presented his team’s work titled, “Deep Learning for Medical Information Extraction”.

We all learnt from the complexity of machine learning models and how they can be used to solve some of the real world electronic health record data problems with a high degree of accuracy.

Summary of the discussion from the 2 papers(in press) he discussed is below:

 

1) Named Entity Recognition (NER). 

Title: “Effective use of bidirectional language modeling for biomedical named entity recognition”

 

Background: There is an increased need for text mining in the biomedical field due to the rapid increase in the number of publications, scientific articles, reports, medical records, etc. that are available and readily accessible in electronic format. To transform unstructured collections of medical text into structured information and link them, information extraction systems must accurately identify different biomedical entities such as chemical ingredients, genes, proteins, medications, diseases, symptoms, etc. The task of identification and tagging of such entities in text as members of predefined categories such as diseases, chemicals, genes, etc. is referred to as NER. Designing an NER system with high precision and recall for the biomedical domain is a very challenging task due to the limited availability of high-quality labeled data and the linguistic variation of that data that includes ambiguous abbreviations, non-standardized descriptions, and lengthened names of entities. An NER system can be devised as a supervised ML task in which the training data consists of labels for each token in a text.

 

2) Relation Extraction (RE).

Title: “Relation Extraction of medical entities and attributes on Electronic Medical Records”.

 

This paper presents the findings of the development and testing of a deep learning model developed for medical relation extraction in clinical notes. The novel and effective deep learning approach automatically extracts relation for medical entities from EHRs. Specifically, a CNN-based model is used, which captures both salient syntactic feature and latent semantic feature from the text descriptions, despite their differences in language style. The model was evaluated on a real-patient dataset and achieved better performance than existing baselines on the tasks of extracting relations and deciding negations. It also shows significant potential in helping doctors in downstream tasks. 

Petuum has been named as a 2018 Technology Pioneer by the World Economic Forum with a significant focus and leadership in healthcare.

Link to Petuum's healthcare research and publications:

https://www.petuum.com/healthcare.html

 

BRAINX COMMUNITY LIVE !

July 2018 session.

July event featured Dr.Art Papier,who presented his 18 years experience in the field of diagnosis accuracy and use of visual diagnosis with the aid of machine learning to improve outcomes and decrease cost.

He showcased existing technology that his team has built which can be integrated into various workflows to aid decision making.

The talk was followed by a great discussion amongst engaged community members on various topics such as data access, patient privacy,regulatory/legal  issues,technological challenges and clinician adoption amongst others.

Link to Dr. Art Papier's July 2018 BrainX Community session video is attached below.

Note:It opens with Internet Explorer browser only.

http://webcast1.ccf.org/viewerportal/ccfe/video.vp?programId=esc_program:107577

A commentary by Dr. Papier recently published in the American Journal of Medicine July issue.

https://www.amjmed.com/article/S0002-9343(18)30094-9/pdf

Link to 1971 Larry Weed Internal Medicine Grand Rounds at Emory as discussed by Dr.Papier's during his presentation.

"It's incredible to see what Dr. Weed visioned 47 years ago, so much is relevant today……"

https://www.visualdx.com/company/larry-weed-1971-grand-rounds-at-emory-video

Learn more about CoreML, VisualDx.

http://www.businessinsider.com/visualdx-machine-learning-app-for-skin-diagnosis-ceo-interview-2017-11

Learn more about the Visual Dx through the link below:

https://www.visualdx.com

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BRAINX COMMUNITY LIVE !

June 2018 Session

June Session featured Satish Viswanath,PhD. who gave a presentation on Machine Learning applications in Oncology and GastroIntestinal diseases.

His presentation focused on his teams application of combining Imaging data with pathology features to augment understanding of malignancy and it's treatment effect.

Literature review available via link below:

https://www.brainxai.org/learn/

Learn more about Dr.Viswanath and his teams work through link below:

http://engineering.case.edu/centers/ccipd/

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BRAINX COMMUNITY LIVE !

Innaugral May 2018 session.

Inaugural session of BrainX Community's first live session held at Cleveland Clinic in May,2018.

Theme:Can AI transform healthcare?

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