Building AI Models for Healthcare (ML Tech Talks)

In this session of Machine Learning Tech Talks, Product Manager Lily Peng will discuss the three common myths in building AI models for healthcare.

Chapters:

  • 0:00 - Introduction
  • 1:48 - Myth #1: More data is all you need for a better model
  • 6:58 - Myth #2: An accurate model is all you need for a useful product
  • 9:15 - Myth #3: A good product is sufficient for clinical impact
  • 12:19 - Conversation with Kira Whitehouse, Software Engineer
  • 34:48 - Conversation with Scott McKinney, Software Engineer

Resources:
Deep Learning for Detection of Diabetic Eye Disease: Gulshan et al, Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 2016 → https://goo.gle/3gVhTxs

A major milestone for the treatment of eye disease De Fauw et al, Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine September 2018 → https://goo.gle/35Sfs9C

Assessing Cardiovascular Risk Factors with Computer Vision. Poplin et al, Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. March 2018 → https://goo.gle/3qkg01I

Improving the Effectiveness of Diabetic Retinopathy Models: Krause et al, Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. Ophthalmology August 2018 → https://goo.gle/3gR8d8n

Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. Raumviboonsuk et al. NPJ Digital Medicine. April 2019 → https://goo.gle/2SmyXUO

Healthcare AI systems that put people at the center: Beede et al, A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. CHI '20 April 2020 → https://goo.gle/3ja6TyP

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. MScPH, Yuchen Xie, Quang D. Nguyen BEng, Haslina Hamzah BSc, Gilbert Lim, Valentina Bellemo MSc, Dinesh V. Gunasekeran MBBS, Michelle Y. Yip, et al. The Lancet → https://goo.gle/3zVec3q

#tensorflow #ai #artificial-intelligence

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Building AI Models for Healthcare (ML Tech Talks)
Alayna  Rippin

Alayna Rippin

1597622400

The Trending Healthcare App Features for 2020

The recent technological trend in the healthcare industry has brought a virtual doctor into many of our pockets. Be it a serious health condition or a need to track our fitness level, there are thousands of mHealth apps for most of the healthcare use cases.

In 2020, the health-tech industry offers many mHealth apps ranging from heart rate monitoring to nutrition and fitness apps. Undoubtedly, there will be variation in the app functionality according to the target market, customer base and the purpose.

Use-Case specific app features

There are thousands of healthcare mobile apps available in the market and depending on the use-cases, they will incorporate specific features that help them serve the purpose. Following are some of the most trending app features.

Digital Prescription and Reports

Almost every hospital or pharmacy-related healthcare app should have the functionality to handle digital prescriptions and reporting. Misplacing or losing medical prescriptions and reports is very common for the patients. It even becomes a hassle for the healthcare institutions or doctors to manage and access the patient files. This is where the digital prescription feature comes handy for both the patients and doctors.

This feature should also incorporate the functionality to download the reports and prescription information in a documented format. It allows the patients to manage their lifetime medical history in one place. This feature is very crucial to speed up the medical treatment process.

Wearable Connectivity

Wearables are the most trending discussion in the health tech space. Up until now, this feature is most commonly used by fitness tracking apps. But as the healthcare industry has now paced up the technology adoption, healthcare providers and medical practitioners have started to trust the wearable technologies to monitor their patient health continuously.

In 2020, wearables do not just mean some gadgets like Fitbits. There are many clinical-grade IoMT (Internet of Medical Things) devices used by the healthcare industry that are used in a form of belts, chest straps etc.

The wearable connectivity feature allows the healthcare apps to record the user’s data, which can be shared with the doctors. The doctors can provide a better consultancy if they have access to their patient’s all-time health status.

On-Demand Medicine

Similar to amazon for x apps, this on-demand app feature is focused on making the medicines accessible anytime, anywhere. Using this feature, the patients will be able to refill their stock of medicine without having to visit the pharmacy.

The feature would require the functionality to allow admin to list out the pharmacies in the locality, so that the users can order from the nearest one. To make this feature more effective, you also would need to add an online payment feature within the app.

#healthcare #health-tech #health-tech-and-cyber-security #healthcare-apps #healthcare-application #healthcare-mobile-apps #healthcare-trends-in-2020 #top-healthcare-trends

Ken  Mueller

Ken Mueller

1591112700

Importance of AI in Healthcare Sector

AI and related advancements are progressively playing the role of a disruptor in business and society. The application of AI is also increasing in the healthcare domain. These advances can possibly change numerous parts of patient care, just as regulatory procedures inside supplier, patient experience, and pathology labs.

There are as of now various researches recommending that AI can proceed just as or better than people at key human services, for example, diagnosing the ailment. Today, algorithms are beating radiologists at spotting harmful tumors. They are directing specialists on how to build companions for expensive clinical preliminaries.

Nonetheless, for an assortment of reasons, we accept that it will be numerous prior years AI replaces people for wide clinical procedure areas. In this article, we portray both the potential that AI offers to mechanize parts of care and a portion of the hindrances to the fast execution of AI in social insurance.

#artificial intelligence tutorials #ai applications in healthcare #ai in healthcare #applications of ai in healthcare #artificial intelligence and healthcare

Hertha  Walsh

Hertha Walsh

1602709200

Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Shradha Singh

1609159431

AI Models Are Making the World a Better Place

Artificial Intelligence (AI) is not a future trend; it is very much a part of our present and is steering our everyday lives. From the posts we see on our social media profiles to the movies we are recommended by Netflix and products Amazon suggests to us, we actively use AI technology.

Further on, with big companies and makers like NVIDIA, Intel, Qualcomm, and others, innovating the underlying technology (semiconductors), AI models are becoming smarter and better. Here we explore a few ways in which AI is changing our world and making it more advanced and simpler.

AI and the Human World: 4 Big Futuristic Changes
AI Will Improve Remote Learning

Distance learning has existed for many years. But its sudden introduction came as a shocker to the parents as well as teachers as it forced them to learn to teach and learn through the screen.

Artificial Intelligence professionals can help education leaders reduce costs and make education more effective by delivering successful online lessons. It will allow teachers to delegate mundane tasks and take up creative assessments. Planning, assessment, scheduling, and even teaching of facts can be taken care of by the AI.

The tech will allow teachers to focus on building students’ curiosity levels, critical thinking abilities, and creativity. China is leading the way in this with AI solutions in e-Learning with its 9 EdTech unicorns.

It can deliver a learning experience that is customized to a child’s needs.

**AI Will Introduce Physical Interfaces Between Humans and Machines **
Platforms and machines today are better at interacting with us due to AI. However, it is yet to go beyond the software styled interface. In the years to come, AI will go beyond real-world interfaces through which we will talk and interact with AI machines.

Autonomous vehicles are one such example.

Currently, such automation can only be seen in closed doors of factories and warehouses. Plus, these machines are narrow in their activities and rigid. AI-driven automated interfaces and machines will be more sensitive to our needs and intelligence. Artificial Intelligence professionals working in this arena will be high in-demand in future economies.

Latest developments in machine learning and AI models can successfully beat humans through reinforcement learning in games such as Go and DOTA, where an infinite amount of data is generated. This raises hopes for intelligent real-world AI becoming a reality provided enough data and simulations are provided.

#ai models #machine learning #ai #ai machines #ai solutions #futuristic