Nasajte atmosféru Red Button EDU. Podívejte se na kompletní ukázku vysílání tak, jak je dostupné registrovaným uživatelům.
Na toto vysílání navazuje praktická diskuse, během které se můžete doptat na věci, které zazněl ve vysílání a prodiskutovat s ostatními vaše vypracované výzvy.
AI in health. Haha. If we had a dollar for every time we heard this phrase, we’d have enough money to produce Red Button EDU programmes for free for the next 250 years. ‘AI’ is a sexy term, and is deployed in a truly ‘Deus Ex Machina’ way to often quickly dismiss any questions which are pouring in about ‘what the hell can we do to make sure COVID goes away, our healthcare system is resilient, and people don’t die’. But more often than not, these promises are empty, and even when they’re not, we come crashing against a barrier of lack of expertise, mistrust of technologies, bureaucracy, regulations, and most important of all – lack of data. Or lack of digitalised, quality, well-labelled and connected data, to be exact.
Why should I watch the episode?
Do you want to change the world? No, but seriously. Why wouldn’t you be able to? You have unprecedented technology at your fingertips. Nobody’s going to do it for you. Do you want to radically transform the area you’re most passionate about, be it education, health, ballet, travel, climate, looking after elderly dogs? There literally isn’t an area where some form of AI wouldn’t be able to help. Open your mind, educate yourself about latest technologies, and listen to world experts share their stories of changing status quo with the help of technology. It doesn’t have to be a Deus Ex Machina – the power to change the world can come from you and you alone.
What will be my take-home value?
- Appreciation of multidisciplinarity
- What AI is and isn’t
- How widely AI can be applied and how it can radically change the world we live in
- Status quo isn’t given
- Chaos is good – it’s an opportunity
Who are the guests?
Tomáš Šebek is a co-founder of virtual hospital and unique telemedicine project uLekare.cz which provides a virtual medical consultations, appointments and treatment related services to Czech and Slovak patients by the team of 40 General Practitioners, over 200 Medical Specialists and almost all cooperating healthcare facilities in the country. Tomaš’s mission is to utilize his medical and digital skills, knowledge & experience in order to develop both Health and e-Health systems that push the boundaries of conservative view and improve daily lives & health of people & patients. Working also in an international humanitarian environment helps him to be more open-minded, see whole complexity and be proud member of wide network of all around the globe experts.
Ondřej Vaněk is a co-founder and CEO of Blindspot Solutions, a company helping others to harness the power of artificial intelligence by designing and implementing tailored software solutions. Ondrej is fascinated with new technologies, their impact on business and society, and he enjoys working with his team to digitize the world. Outside of working hours, Ondrej struggles to keep his running pace on a reasonable level, reads at least some of the most interesting books out there, and discovers the world with his wife Barbara and two daughters Julie and Anna.
Start studying AI
Time needed: 2 hours
(but can be less or more depending on how much in detail you decide to go)
The challenge here is to get started on Chapter 1 of the Elements of AI course. We’ll be releasing the Czech version with prg.ai during march but in the meantime, you can get started on the English version. It’s super-important to understand what AI is and isn’t in order to be able to understand how it can and can’t change our lives, especially in such crucial areas such as healthcare.
Why should you accept the challenge?
- You’ll start on a course by the end of which you’ll have a good rounded basic understanding of artificial intelligence (and a certificate!) and be the first pioneering wave of the Czech public to get involved in this initiative
- You’ll be able to spot ‘PR fake-AI’ from real AI techniques, plough through media-BS and better deploy critical thinking in relation to news on how innovation can change society
- You’ll have new opportunities open up, whether educational or professional thanks to your increased AI knowledge. You won’t become a pro overnight, but lifelong learning is always worth it and this is a neat, elegant way to get started with a bunch of like-minded people.
What should I do?
- Go to the website: https://course.elementsofai.com/
- Do first chapter – What is AI? (all three subsections)
- After you finished the chapter, reflect whether you can:
- Explain autonomy and adaptivity as key concepts for explaining AI
- Distinguish between realistic and unrealistic AI (science fiction vs. real life)
- Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
Reach out to email@example.com if you want to chat further or find out more, and follow prg.ai for additional content on Elements of AI
- Artificial Intelligence, Applications in Healthcare Delivery
- Summary: The re-discovery of the potential of Artificial Intelligence (AI) to improve healthcare delivery and patient outcomes has led to increasing application of AI techniques such as Deep Learning, Computer Vision, Natural Language Processing and Robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in Medicine. These trends will mean an expanded role for AI in provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare. This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration and delivery. Readers, especially healthcare professionals and managers, will find the book useful to understand the different types of AI and how they are relevant to Healthcare Delivery. The book provides examples of AI being applied in medicine, population health, genomics, healthcare administration and delivery and how they can commence applying AI in their health services. Others like researchers and technology professionals will find the book useful to note current trends in the application of AI in healthcare and initiate their own projects to enable the application of AI in healthcare/medical domains.
- Deep Medicine : How Artificial Intelligence Can Make Healthcare Human Again
- Summary: A visit to a physician these days is cold: physicians spend most of their time typing at computers, making minimal eye contact. Appointments generally last only a few minutes, with scarce time for the doctor to connect to a patient’s story, or explain how and why different procedures and treatments might be undertaken. As a result, errors abound: indeed, misdiagnosis is the fourth-leading cause of death in the United States, trailing only heart disease, cancer, and stroke. This is because, despite having access to more resources than ever, doctors are vulnerable not just to the economic demand to see more patients, but to distraction, burnout, data overload, and their own intrinsic biases. Physicians are simply overmatched.
- Introducing Medical Anthropology: A Discipline in Action
- This new text provides students with a first exposure to the growing field of medical anthropology. As such, it is guided by three unifying themes. First, medical anthropology is actively engaged in helping to address pressing health problems around the globe through research, intervention, and policy-related initiatives. Second, illness and disease cannot be fully understood or effectively addressed by treating them solely as biological in nature; rather, health problems involve complex biosocial processes and resolving them requires attention to range of factors including systems of belief, structures of social relationship, and environmental conditions. Third, through an examination of health inequalities on the one hand, and environmental degradation and environment-related illness on the other, the authors emphasize the need for a comprehensive medical anthropology that integrates biological, cultural, and social factors, in order to understand the origin of ill health and to contribute to more effective and equitable health care systems.
- Reader in Medical Anthropology
- Summary: A Reader in Medical Anthropology: Theoretical Trajectories, Emergent Realities brings together articles from the key theoretical approaches in the field of medical anthropology as well as related science and technology studies. The editors‘ comprehensive introductions evaluate the historical lineages of these approaches and their value in addressing critical problems associated with contemporary forms of illness experience and health care.
- Madness and Civilization: A History of Insanity in the Age of Reason
- Summary: Michel Foucault examines the archeology of madness in the West from 1500 to 1800 – from the late Middle Ages, when insanity was still considered part of everyday life and fools and lunatics walked the streets freely, to the time when such people began to be considered a threat, asylums were first built, and walls were erected between the “insane” and the rest of humanity.
- The Birth of the Clinic: An Archaeology of Medical Perception
- Summary: In the eighteenth century, medicine underwent a mutation. For the first time, medical knowledge took on a precision that had formerly belonged only to mathematics. The body became something that could be mapped. Disease became subject to new rules of classification. And doctors begin to describe phenomena that for centuries had remained below the threshold of the visible and expressible.
- Medicine, Rationality and Experience: An Anthropological Perspective
- Summary: Medicine supposedly offers a scientific account of the human body and of illness, and it follows that scientific medicine treats all forms of folk medicine as little more than superstitious practices.
- The Creativity Code
- Summary: Will a computer ever compose a symphony, write a prize-winning novel, or paint a masterpiece? And if so, would we be able to tell the difference? As humans, we have an extraordinary ability to create works of art that elevate, expand and transform what it means to be alive. Yet in many other areas, new developments in AI are shaking up the status quo, as we find out how many of the tasks humans engage in can be done equally well, if not better, by machines. But can machines be creative? Will they soon be able to learn from the art that moves us, and understand what distinguishes it from the mundane?
- Na hrane chaosu: AI vs. Healthcare
- The state of artificial intelligence in medicine
- AI in Healthcare: Top A.I. Algorithms In Healthcare – The Medical Futurist
- AI in Medicine | Medical Imaging Classification (TensorFlow Tutorial)
- AI in Radiology at Stanford: Rise of the Machines
- AIMI Symposium 2020
- Artificial Intelligence in Medicine
- Summary: Artificial intelligence in medicine and healthcare has been a particularly hot topic in recent years. While there is a sense of great potential in the application of AI in medicine, there are also concerns around the loss of the ‘human touch’ in such an essential and people-focused profession.
- Artificial Intelligence in Medicine: Applications, implications, and limitations
- Summary: The future of ‘standard’ medical practice might be here sooner than anticipated, where a patient could see a computer before seeing a doctor. Through advances in artificial intelligence (AI), it appears possible for the days of misdiagnosis and treating disease symptoms rather than their root cause to move behind us. Think about how many years of blood pressure measurements you have, or how much storage you would need to delete to fit a full 3D image of an organ on your laptop?
- AI for Medicine Specialization
- AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
- Artificial Intelligence in Medicine AI for Diagnostics, Drug Development, Treatment Personalisation and Gene Editing
- Summary: Machine Learning has made great advances in pharma and biotech efficiency. This post summarizes the top 4 applications of AI in medicine today
- Wikipedia entry – good general overview
- Overview of artificial intelligence in medicine
- Summary: Artificial intelligence (AI) is the term used to describe the use of computers and technology to simulate intelligent behavior and critical thinking comparable to a human being. John McCarthy first described the term AI in 1956 as the science and engineering of making intelligent machines.
- The Future of Artificial Intelligence in Medicine and Imaging
- Summary: Artificial intelligence’s remarkable ability to ingest huge amounts of data, make sense of images, and spot patterns that escape even the most-skilled human eye has inspired hope that the technology will transform medicine. Realizing the full potential of this opportunity will require the combined efforts of experts in computer science, medicine, policy, mathematics, ethics and more.
- Artificial intelligence in medicine: current trends and future possibilities
- Summary: Artificial intelligence (AI) research within medicine is growing rapidly. In 2016, healthcare AI projects attracted more investment than AI projects within any other sector of the global economy.1 However, among the excitement, there is equal scepticism, with some urging caution at inflated expectations.2 This article takes a close look at current trends in medical AI and the future possibilities for general practice.
- Application of neural networks in medicine – a review
- Summary: The main aim of research in medical diagnostics is to develop more exact, cost-effective and easy-to-use systems, procedures and methods for supporting clinicians. In this paper the authors introduce a new method that recently came into the focus referred to as computer generated neural networks. Based on the literature of the past 5-6 years they give a brief review – highlighting the most important articles – showing the idea behind neural networks and where they are used in the medical field. The definition, structure and operation of neural networks are discussed. In the application section they discuss examples in order to give an insight into neural network application research. It is emphasized that in the near future completely new diagnostic equipment can be developed based on this new technology in the field of ECG, EEG and macroscopic and microscopic image analysis systems.
- Medical Decision Making
- Summary: Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical decision-making applications. It includes detailed discussion of the issues particularly relevant to medical data as well as wider issues relevant to any neural net application. The article is restricted to back-propagation learning in multilayer perceptrons, as this is the neural net model most widely used in medical applications. Key words: neural networks; medical decision making; pattern recognition; nonlinearity; error back-propagation; multilayer perceptron.
Might Be Interesting
3. 3. 2021 | 62 min
9. 2. 2021 | 49 min