Medical Machine Learning

C O N T E N T S:

KEY TOPICS

  • At least when it comes to machine learning, it’s likely that useful and widespread applications will develop first in narrow use-cases for example, a machine learning healthcare application that detects the percentage growth or shrinkage of a tumor over time based on image data from dozens or hundreds of X-ray images from various angles.(More…)
  • A good technical read on foundations of AI is ” Neural Networks And Learning Machines ” by Simon Haykin.(More…)
  • Few data science concepts have brought as much excitement and anticipation to healthcare as artificial intelligence and machine learning.(More…)
  • Pure Storage solutions drive the machine learning and AI tools that are redefining healthcare IT and economics.(More…)

POSSIBLY USEFUL

  • According to McKinsey, there are many other ML applications for helping increase clinical trial efficiency, including finding best sample sizes for increased efficiency; addressing and adapting to differences in sites for patient recruitment; and using electronic medical records to reduce data errors (duplicate entry, for example).(More…)

RANKED SELECTED SOURCES

Medical Machine Learning
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link: http://venturebeat.com/2018/05/23/gv-invests-in-medical-machine-learning-startup-owkin/
author: venturebeat.com
description: GV invests in medical machine learning startup Owkin | VentureBeat

KEY TOPICS

At least when it comes to machine learning, it’s likely that useful and widespread applications will develop first in narrow use-cases for example, a machine learning healthcare application that detects the percentage growth or shrinkage of a tumor over time based on image data from dozens or hundreds of X-ray images from various angles. [1] The present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals. [2] The first step in applying machine learning is to identify data that one can use to train a neural network. [3] Deep learning is a type of machine learning based on data representations rather than task-specific algorithms. [3] A patient who is being told that he/she must undergo chemotherapy is unlikely to accept the answer, “The machine learning algorithm said so, based on previous case data and your current condition.” [1]

When a hospital brings on a new machine learning healthcare diagnostic device, who pays for it? Would patients pay a premium to be treated at hospitals with such devices? Would hospitals cover the cost in order to brag about better diagnostic tools and attract more patients? Would insurance cover the cost in some way? Doctors might like such a device if it improved diagnostic accuracy, but some patients may resent or not accept being treated by a machine. [1] Some patients might rally for more machine learning diagnostic tools, but doctors or nurses in fear of their jobs might rally against their widespread adoption. [1]

While much of the healthcare industry is a morass of laws and criss-crossing incentives of various stakeholders (hospital CEOs, doctors, nurses, patients, insurance companies, etc), drug discovery stands out as a relatively straightforward economic value for machine learning healthcare application creators. [1] Until that day comes, Google’s DeepMind Health is working with University College London Hospital (UCLH) to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments. [2] The MIT Clinical Machine Learning Group is spearheading the development of next-generation intelligent electronic health records, which will incorporate built-in ML/AI to help with things like diagnostics, clinical decisions, and personalized treatment suggestions. [2] While machine learning might help with “suggestions” in a diagnostic situation, a doctor’s judgement would be needed in order to factor for the specific context of the patient. [1] At the institutional level, executives and caregivers have been on the lookout for solutions to many of these problems for years, and there is broad hope that machine learning may help develop those solutions. [3] Anyone working in deep learning – the machine learning concept behind many recent AI advances – use GPUs. [3] A “black box” won’t do: Machine learning and deep learning (unlike stodgier AI approaches like expert systems) are unable to express why they achieved the result that they did. [1] Consider this example of how the NVIDIA AI technology works: First there is a clinical challenge, such as identifying breast cancer cells on a pathology slide with machine learning. [3] In the future, machine learning could be used to combine visual data and motor patterns within devices such as the da Vinci in order to allow machines to master surgeries. [1] The use of machine learning in preliminary (early-stage) drug discovery has the potential for various uses, from initial screening of drug compounds to predicted success rate based on biological factors. [2] While not all robotic surgery procedures involve machine learning, some systems use computer vision (aided by machine learning) to identify distances, or a specific body part (such as identifying hair follicles for transplantation on the head, in the case of hair transplantation surgery ). [1] Many of the machine learning (ML) industry’s hottest young startups are knuckling down significant portions of their efforts to healthcare, including Nervanasys (recently acquired by Intel), Ayasdi (raised $94MM as of 02/16), Sentient.ai (raised $144MM as of 02/16), Digital Reasoning Systems (raised $36MM as of 02/16) among others. [1] Google has also jumped into the drug discovery fray and joins a host of companies already raising and making money by working on drug discovery with the help of machine learning. [1] TechEmergence conducts direct interviews and consensus analysis with leading experts in machine learning and artificial intelligence. [2] “Owkin has assembled a talented team of experts in applied machine learning with clinical data experience,” noted GV general partner Adam Ghobarah. [4]

With the assistance of Artificial Intelligence (AI), we are living in an era where machines are learning to program themselves! With heavy investments in this field, machine learning has been transforming the way medical devices provide healthcare assistance and come to conclude how the technology shows tremendous promise for future applications as well. [5] To successfully incorporate machine learning in medicine, doctors and medical specialists have to take a leading role. [6] Computer scientists, engineers and developers representing technology companies interested in developing machine learning based products for the medical imaging market. [7] This is a proposal for a new Market Report on the global market for Machine Learning in Medical Imaging, to be published in June 2018. [8]

A good technical read on foundations of AI is ” Neural Networks And Learning Machines ” by Simon Haykin. [9] Deep learning will probably play a more and more important role in diagnostic applications as deep learning becomes more accessible, and as more data sources (including rich and varied forms of medical imagery) become part of the AI diagnostic process. [1] Among others, one potential solution being worked on is “transfer learning”, to learn in a different domain and transfer the knowledge into the medical domain. [9] To get an overview, ” A survey on deep learning in medical image analysis ” by Litjens and others is an up-to-date article. [9]

If such a machine made an error (potentially a fatal one), at what point would we say that this was the responsibility of the machine manufacturer, and at what point would we say it was the fault of the doctors for not using it correctly? This is just the tip of the iceberg of the lattice of stakeholders in the medical domain, and it’s one of many reasons why innovation and change are sometimes difficult in the medical field. [1]

Few data science concepts have brought as much excitement and anticipation to healthcare as artificial intelligence and machine learning. [10] A machine learning algorithm can accurately identify risk factors for mental health issues, including high stress, by analyzing data collected from wearable devices, according to a study published in JMIR. Recent advancements in mobile and wearable. [10] Senior author of the piece and director of the Stanford Center for Biomedical Ethics David Magnus Ph.D. ?89 divided the major ethical concerns into three levels: the design of the algorithm, the data set being utilized and the societal implementation of machine learning. [11] The recommended courses of action that machine learning algorithms propose are dependent upon existing data sets that may contain inherent biases. [11]

According to medicine associate professor and paper co-author Nigam Shah postdoc ?07, machine learning has potential applications in diagnosis, prognosis and suggested courses of treatment in a clinical setting. [11] Both drug discovery and computational medicine will be improved with machine learning, and treatment regimes, too, will find themselves transformed by intelligence and automation. [12] Precision medicine driven by machine learning offers the promise of tailored, individualized treatment, improved patient outcomes, and better overall population health. [10] We invite full papers, as well as work-in-progress on the application of machine learning for precision medicine and healthcare informatics. [13] As machine learning, deep learning, and other aspects of AI start to mature, they bring nearly endless possibilities to supplement, streamline, and enhance the way. [10] The healthcare industry has innumerable opportunities to leverage artificial intelligence and machine learning in pursuit of more accurate, proactive, and comprehensive patient care. [10] It’s no exaggeration to say that machine learning can transform the healthcare industry entirely. [12]

Machine learning and AI will back experience-based improvement, with medical analysis and assurance boosting in efficacy and accuracy with each ensuing diagnosis. [14]

In an article published in Chronicle PharmaBiz, Sindhu Ramachandran, Principal Architect, QuEST Global shares her views on how machine leaning is revolutionizing the medical devices industry. [5] Startup community working on innovative solutions in medical imaging informatics utilizing machine intelligence. [7]

Pure Storage solutions drive the machine learning and AI tools that are redefining healthcare IT and economics. [15] Listen in as Mark Michalski, MD, Executive Director at the MGH & BWH Center for Clinical Data Science (CCDS), details the infrastructure enabling efforts at applying machine learning to healthcare, and the potential for machine learning to become a powerful tool in the hands of clinical professionals in the years to come. [15] There might be a lot of hype around what machine learning and artificial intelligence can do for healthcare, but a new study could showcase the real-life potential of the technology for early disease detection. [16] Artificial Intelligence (AI) and machine learning have taken the healthcare industry by storm. [14] Here we present some of the important ways AI and machine learning is already helping, or will help the future healthcare industry. [14] On May 24, FDA gave the green light to market Imagen OsteoDetect, an AI algorithm that uses machine learning techniques to analyze wrist radiographs (X-ray images) to assist clinicians in locating areas of distal radius fracturing. [17] In this track, we will explore key machine learning and drug development insights for utilizing AI in clinical development. [18] Integrated care requires high-performance workflows, clinical apps, and advanced data analytics – all powered by machine learning. [15] Machine learning applies not just to your data, but to your all-flash array itself. [15] Machine learning has shown the potential for helping with fast-rising radiology workloads through machine-assisted image analysis and rapid, broad image access. [15] By continuously scanning call-home telemetry from Pure’s installed base, our Pure1 cloud-based management and support uses machine learning predictive analytics to help resolve potential issues and optimize your workloads. [15] The clinically relevant and increasingly common diagnosis of sarcopenia is at the confluence of three tectonic shifts in medicine: opportunistic imaging, precision medicine, and machine learning. [19] Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning. – PubMed – NCBI Warning: The NCBI web site requires JavaScript to function. more. [19]

“These automated tools, based on the principles of machine learning and implemented via complex neural networks, are now equaling, if not surpassing, human performance in many cases,” he said. [20]

By using machine learning, AI and the blockchain, this company plans to empower the users with their own medical data, which can lead to a simplification process of taking care of your health. [21] By leveraging a powerful form of AI, called causal machine learning, we transform massive and diverse data streams to precisely match therapeutics, procedures, and care management interventions to individuals. [22] The application of machine learning in healthcare has the potential to uncover clinically-relevant patterns and meaning in our healthcare data; helping us better understand complex diseases like cancer. [23] Eliot Siegel, M.D., associate vice chair of diagnostic radiology and nuclear medicine, vice chair of information systems, University of Maryland, and chief of radiology, VA Maryland Healthcare System, discusses how machine learning (aka artificial intelligence) is impacting radiology today and its role in the future. [24] Biodesix’s proprietary machine learning platform builds on recent advances in the artificial intelligence field to address the unique needs of clinical diagnostics. [23] At Biodesix, we use the Diagnostic Cortex platform, which is based on modern machine learning techniques to design tests that are reproducible, robust, and answer critical clinical questions for our partners. [23] Combining radiologists’ knowledge with modern machine learning enables an entirely new level of diagnostics we can use to detect cancer and improve patient outcomes. [25] At Biodesix, we are transforming the healthcare industry by using machine learning to design molecular diagnostic tests that answer meaningful clinical questions. [23] Machine learning is a subset of artificial intelligence, where the machine has the ability to acquire its own knowledge by extracting patterns from data 2. [23] It was an easy decision coming here – I wanted to gain expertise in applying advanced statistical modeling and machine learning to analyze real-world data. [22] Colin Hill is a leading voice in healthcare technology and precision medicine and brings impressive leadership experience in commercializing machine learning technologies in the biopharmaceutical and managed care industries. [22] As a pioneer in artificial intelligence and causal machine learning – GNS has been discovering scientific evidence to improve health outcomes and speed drug discovery and development since 2000. [22] Ben brings 25 years of executive and senior information technology experience to GNS. In his role at GNS as Chief Information Officer, Ben is responsible for scaling and supporting the delivery of GNS technology, including the GNS REFS? (Reverse Engineering and Forward Simulation) causal machine learning and simulation platform and other end user tools. [22] When I heard GNS was working on personalized medicine via causal machine learning, I asked to join them. [22] A: I work with the R&D team to firstly learn about causal machine learning in REFS, then to write automated scripts to assess and confirm its quality, and finally prototype additional solutions for future updates. [22] Before turning to systems biology, Boris’s training focused on computer science in the context of machine learning and natural language processing. [22]

POSSIBLY USEFUL

According to McKinsey, there are many other ML applications for helping increase clinical trial efficiency, including finding best sample sizes for increased efficiency; addressing and adapting to differences in sites for patient recruitment; and using electronic medical records to reduce data errors (duplicate entry, for example). [2] “We can feed the GPU information derived from medical records, like clinical notes, medical imaging data, pathology slides – just about everything we can learn from our records, we try to leverage.” [3] “One of the primary reasons we are building our own data center with dedicated GPUs is that we rely on medical data such as MRIs and CTs, which can be a lot of data, and is sensitive from a privacy perspective,” Michalski explained. [3]

The beauty of it is, again, that the AI will not be making any diagnostic decision but just making the existing medical wisdom accessible, the wisdom that is currently fallow under terabytes of digital dust. [9] If we abandon the classification-oriented AI (making yes/no decisions), which aims at eliminating the diagnostic role of the pathologist, then we are left with mining-oriented AI that identifies and extracts similar patterns from large archives of medical images. [9]

Elsewhere, in July Google announced a new venture fund called Gradient Ventures aimed specifically at early-stage AI startups, which recently made its first medical sciences investment when it plowed money into BenchSci to help speed up biomedical discoveries. [4] Not engineers, not computer scientists, it will be the pathologists that would have the medical knowledge to be in charge of exploiting the AI capabilities. [9]

The promise of personalized medicine is a world in which everyone’s health recommendations and disease treatments are tailored based on their medical history, genetic lineage, past conditions, diet, stress levels, and more. [1] Predicting outbreak severity is particularly pressing in third-world countries, which often lack medical infrastructure, educational avenues, and access to treatments. [2]

Like Instagram, you might only need a dozen engineers and the right idea at the right time; however, it’s unlikely that a dozen engineers even if they raised many tens of millions of dollars would have the requisite industry connections and legal understandings to penetrate the deep layers of stakeholders in order to become a de-facto medical standard. [1] An article by myself and Dr. Pantanowitz (University of Pittsburgh Medical Center) titled “Artificial Intelligence in digital Pathology – Challenges and Opportunities” will soon appear in the Journal of Pathology Informatics. [9] In an October 2016 interview with Stat News, Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, stated: “In 20 years, radiologists won?t exist in anywhere near their current form. [2] The manifestation of medical diagnosis difficulty is clearly visible in the so-called “inter-observer variability”; doctors cannot agree on a diagnosis or measurement when given the same case. [9] Michalski is a radiologist by training, and in his field, there are many instances where one has to look at medical images and find abnormalities within images, characterize and measure those abnormalities, and describe a diagnosis based on those findings. [3] Looking at its potentials for radiogenomics, auto-captioning of medical images, recognition of highly non-linear patterns in large datasets, and quantification and visualization of extremely complex image content, are just some examples. [9] Most works were focused on conventional computer vision which focused, and still does, on “handcrafted” features, techniques that were the results of manual design to extract useful and differentiating information from medical images. [9] Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms. [9] A final problem worth mentioning is the so-called “adversarial attacks” when someone with knowledge of the system, or exploiting the presence of artefacts and noise, could eventually fool a deep network into a wrong decision, an effect that is extraordinarily important in medical imaging; we cannot allow algorithms to be fooled when we are dealing with people’s lives. [9] The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. [9] The investigations of applications of these powerful AI methods in medical imaging has started in the past 3-4 years and is in its infancy but promising results have been reported here and there. [9]

Artificial neural networks continued to fall short of expectations not just in medical imaging, but in computer vision in general. [9]

Support vector machines and artificial neural networks have been used, for example, to predict malaria outbreaks, taking into account data such as temperature, average monthly rainfall, total number of positive cases, and other data points. [2] Document classification (sorting patient queries via email, for example) using support vector machines, and optical character recognition (transforming cursive or other sketched handwriting into digitized characters), are both essential ML-based technologies in helping advance the collection and digitization of electronic health information. [2] At Kimia Lab, we have been working on a multitude of techniques, from deep networks to support vector machines, from local binary patterns to Radon transform, and from deep autoencoders to dimensionality reduction. [9] K-means (an old clustering method), support vector machines (SVM), probabilistic schemes, and decisions trees and their extended version ‘random forests’ were among successful approaches. [9] This kind of “black box problem” is all the more challenging in healthcare, where doctors won’t want to make life-and-death decisions without a firm understanding of how the machine arrived at it’s recommendation (even if those recommendations have proven to be correct in the past). [1] Doctors are assessing streams of information that machines today are either incapable of assessing or are incapable of integrating into a “doctor-replicator” robot. [1] If a machine can be trained to replicate the legendary creative capacity of Van Gough or Picaso, we might imagine that with enough training, such a machine could “drink in” enough hip replacement surgeries to eventually perform the procedure on anyone, better than any living team of doctors. [1] This is one more reason that most doctors should not be shaking in their boots about getting replaced by machines in the next decade. [1]

Imagine a machine that could adjust a patient’s dose of pain killers or antibiotics by tracking data about their blood, diet, sleep, and stress. [1] Diagnosis is a very complicated process, and involves at least for now a myriad of factors (everything from the color of whites of a patient’s eyes to the food they have for breakfast) of which machines cannot presently collate and make sense; however, there’s little doubt that a machine might aid in helping physicians make the right considerations in diagnosis and treatment, simply by serving as an extension of scientific knowledge. [1] “It turns our brain’s work in a similar way to GPUs, which is perhaps why GPU chips are so effective as tools for machine learning.” [3]

GPUs and deep learning have big potential for how healthcare can manage and interpret imaging and other clinical data. [3] That’s why I think a lot of us in the field believe AI and deep learning are going to be very important to the future of healthcare. [3] One application of AI, deep learning, is coming into its own. [3] Deep learning applications are known be limited in their explanatory capacity. [1]

The domain is presently ruled by supervised learning, which allows physicians to select from more limited sets of diagnoses, for example, or estimate patient risk based on symptoms and genetic information. [2] Image credit: Circulation – A: Matrix representation of the supervised and unsupervised learning problem B: Decision trees map features to outcome. [2] Deep learning is just one of them, but it is the one with the most success in recognizing image content in recent years. [9] One can find many works with artificial neural networks, the backbone of deep learning. [9] For readers who aren’t familiar with deep learning but would like an informed, simplified explanation, I recommend listening to our interview with Google DeepMind’s Nando de Freitas. [1] Today, Massachusetts General and Brigham and Women’s Hospitals are using deep learning to automate the things that humans do well, but don’t want to do or don’t have the time to do. [3] Deep learning faces multiple challenges in digital pathology. [9] Another major challenge for deep learning in digital pathology is the dimensionality of the problem. [9]

HIPAA (Health Insurance Portability and Accountability Act, passed by Congress in 1996) laws exist among other reasons to enforce Federal standards on any transmission of patient medical information. [1] Please note that medical information found on this website is designed to support, not to replace the relationship between patient and physician/doctor and the medical advice they may provide. [9]

Dr. Hamid R. Tizhoosh is a Professor in the Faculty of Engineering at University of Waterloo since 2001 where he leads the KIMIA Lab (Laboratory for Knowledge Inference in Medical Image Analysis). [9]

Dr. Gurpreet Dhaliwal is a professor of medicine at the University of California, San Francisco and a staff physician at the San Francisco VA Medical Center. [26] Over the recent years, the decreasing cost of data acquisition and ready availability of data sources such as Electronic Health records (EHR), claims, administrative data and patient-generated health data (PGHD), as well as unstructured data, have led to an increased focus on data-driven and ML methods for medical and healthcare domain. [13] The American Medical Association (AMA) has released a policy statement that takes a favorable view towards artificial intelligence in healthcare – as long as the emerging category of tools and solutions can be carefully designed, user-friendly,. [10]

Salary level 57 (code 1352/pay framework 24.1) at present NOK 490 900 gross p.a., with a degree in Medicine or Dentistry level 59 (code 1352/pay framework 24.3) at present NOK 508 800 gross p.a., with a medical specialization level 61 (code 1352/pay framework 24.5) at present NOK 527 500 gross p.a. on the government salary scale. [6] This project will be conducted at Mirada Medical Ltd, in collaboration with the BUBBL group at the University of Oxford. [27]

The hype around artificial intelligence (AI) in medical imaging has led to plenty of discussions of its impact in clinical and academic spaces. [7] Founded by Stanford doctoral candidate Enhao Gong and Stanford professor Greg Zaharchuk, an MD/PhD focused on stroke and neurological disorders, Subtle Medical is focused on improving the quality and speed of medical imaging exams by enhancing the ability to use lower-quality scans obviating the need for repeat imaging procedures. [12]

A deep learning approach that incorporates big data from electronic health records (EHRs) was able to predict inpatient mortality, unexpected readmissions, and long length of stay more accurately than traditional predictive models, according. [10] A deep learning tool identified melanoma in dermoscopic images with more accuracy than dermatologists, according to a study published in the Annals of Oncology. [10]

Enlitic, a startup, is using deep learning to detect lung cancer nodules in CT images, and their algorithm is 50% more precise than a team of expert thoracic radiologists. [14] AI is powered by massively parallel technologies, like deep learning and GPUs. [15] Pure Storage all-flash solutions provide a high-performance foundation for modern analytics and deep learning. [15]

“Artificial intelligence, particularly efforts to use machine learning. holds enormous promise for the future of medicine, and we?re actively developing regulatory framework to promote innovation and the use of AI-based technologies,” FDA Commissioner Scott Gottlieb said April 26 at the Health Datapalooza in Washington, D.C. [17] Or, to analogize another current tech behemoth, think of it as the health version of Netflix – the more the app (or machine) records what a viewer is watching, the better the likelihood that the viewer will have a positive experience (better health) in the future. [20]

AI-enabled robots can improve and steer the accuracy of the surgical instrument by combining real-time operating metrics, information from surgical experiences, and data from pre-op medical records. [14] Most of this interaction data is already collected in an effort to assess better ways to design a website, but it can be put to medical use as well. [16] By all assessments, AI has a bright future in medicine, in big-picture endeavors such as the Cancer Moonshot and the Precision Medicine Initiative, as well as in everyday uses that might prove to be the most common manifestations for AI. In the Healthcare IT News survey, in fact, respondents said they expected the most common use of medical AI would be in “pop health.” [17] The company’s current and future arsenal of AI algorithms are expected to be bundled into a $1 per scan offering to hospitals, as Zebra Medical aims to provide scalable, transparent and affordable AI assistance to provider organizations. [28]

Artificial intelligence is being used by life science organizations to accelerate drug development and spur medical device innovation by through genomic profiling, recognizing a need for immediate clinical intervention, and by monitoring medication adherence. [18] “These interactions can also be leveraged to reveal the underlying medical conditions of users, based on statistics collected from millions of customers,” the report notes. [16] Zebra Medical Vision is focusing its efforts on the growing market of AI-based tools for radiology. [28]

The approval is FDA’s third to date that uses AI within the medical field, following approval in April of a device called IDx-DR that uses AI to help detect an eye disease known as diabetic retinopathy in adults with diabetes. [17] AI has established an impressive track record in the medical field, in areas such as diagnosing lung cancer and heart disease, creating a bloodless blood test, or predicting the next pandemic. [17]

Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. [29] The papers are being presented at the Conference on Computer Vision and Pattern Recognition (CVPR), held this week, and at the Medical Image Computing and Computer Assisted Interventions Conference (MICCAI), held in September. [29]

RANKED SELECTED SOURCES(29 source documents arranged by frequency of occurrence in the above report)

1. (24) Machine Learning Healthcare Applications – 2018 and Beyond

2. (23) Artificial Intelligence and Machine Learning in Medical Imaging

3. (13) Mass General, Brigham and Women’s to apply deep learning to medical records and images | Healthcare IT News

4. (12) 7 Applications of Machine Learning in Pharma and Medicine

5. (8) Machine Learning News and Resources for Healthcare – HealthITAnalytics

6. (8) Enterprise Medical Imaging Solutions: Data Storage for Healthcare | Pure Storage

7. (8) Home | GNS HealthCare

8. (5) How Healthcare Industry Using AI | Wearable Technologies

9. (5) AI in Medicine Gets Closer to Making Regular Rounds

10. (5) Machine Learning – Biodesix

11. (3) Research examines ethics of machine learning in medicine The Stanford Daily

12. (3) Bessemer launches a seed fund for startups applying machine learning to health TechCrunch

13. (3) Machine Learning Could Help Detect Diseases Earlier, New Study Finds – HealthTech

14. (3) 2018 Conference on Machine Intelligence in Medical Imaging – Society for Imaging Informatics in Medicine

15. (2) Machine Learning for Medicine and Healthcare

16. (2) AI | AI Innovation Summit Philadelphia

17. (2) Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning. – PubMed – NCBI

18. (2) Dr. Gatti’s e-book explores MATLAB, machine learning and the future of personalized medicine « News

19. (2) Zebra Medical Vision raises $30M in latest venture round | Health Data Management

20. (2) Faster analysis of medical images | MIT News

21. (2) GV invests in medical machine learning startup Owkin | VentureBeat

22. (2) Chronicle PharmaBiz | Machine learning revolutionizing the field of medical devices – QuEST Global

23. (2) Postdoctoral Fellow in medical imaging and machine learning job with UNIVERSITY OF BERGEN | 82615

24. (1) Health FX ICO (HFX Token): Blockchain AI Machine Learning Medical Care?

25. (1) VIDEO: Machine Learning and the Future of Radiology | Imaging Technology News

26. (1) Kheiron Medical

27. (1) Three Difficult Questions to Ask About Using AI in Medicine – The Experts – WSJ

28. (1) Ph.D. in Interactive Machine Learning for Medical Image Segmentation | Mirada

29. (1) Machine Learning in Medical Imaging – World – 2018 – Signify Research