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Artificial Intelligence in ophthalmology

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Telemedicine and artificial intelligence (AI) provide solutions to the challenges faced by ophthalmologists and healthcare professionals around the world. Diseases such as diabetic retinopathy (DR), retinopathy of prematurity (ROP), age-related macular degeneration (AMD), glaucoma and other anterior segment disorders could be more easily predicted and detected with the help of these new technologies. New digital tools and the development of fifth-generation (5G) wireless networks, artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) and the Internet of Things (IoT), or blockchain, have created new opportunities for the healthcare sector that offer great scenarios for improving diagnoses and making patient care more comfortable. Moreover, the pandemic that we have unfortunately had to live with for more than a year now is prompting us to speed up the process of widespread telemedicine. Particularly in less industrialised countries where hospitals are often very far from villages.


Not using AI in healthcare will soon be malpractice

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Central and Eastern Europe is well positioned to take a leading role in the development of AI in healthcare, but the creation of a marketplace for data is crucial. Just how important a role will artificial intelligence (AI) have in medicine over the coming years? That it will revolutionise healthcare is now beyond doubt, particularly in early diagnosis. Even so, its importance – and the need to speed up its implementation – cannot be overstated. Ligia Kornowska, the managing director of the Polish Hospital Federation, and a leader of the AI Coalition in Healthcare, is clear: "not to make use of AI," she says, "will soon be viewed as medical malpractice."


How AI in healthcare is transforming medical imaging

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Medical imaging is amongst the most promising clinical applications of AI, and its ability to detect and qualify a wide arrange of medical conditions. Medical imaging is fundamental in clinical diagnosis, patient treatment and medical research. Leveraging computer-aided diagnostics can drastically improve accuracy and specificity for the detection of even the smallest radiographic abnormalities. Medical imaging produces huge datasets, which would traditionally be analysed in real-time by radiologists. However, in the light of a global pandemic, demand is mounting, and backlogs are growing.


How To Ace ML Interview Questions

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Suppose you get a call from the recruiter of your dream company where you have applied for the ML Engineer role. You have set a date and started preparation with an ML study guide like this one or similar. On the day of the interview, you are able to answer all the questions and are confident that you will move onto the onsite stage. However, you get a call from the recruiter saying that they have decided not to go forward. It is not enough to answer the question, because the interviewer wants to see that you have a deep understanding of the topic/question.


Remote Patient Monitoring -- RPM

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Americans will generate more clinical grade biological data like daily vital signs in the next 5 years than has previously been recorded in the past 20 years. The data will be more accurate since it won't be one snapshot in time, but many snapshots in someone's daily life. While most clinical grade vital signs are collected and recorded in a healthcare setting like a clinic, hospital, or ER, there are a number of factors changing that quickly. The combination of AI based software & medical devices that have cleared the FDA, payor reimbursement, clinical adoption, and patient adoption are all coming together to bring RPM mainstream. This is impactful for a number of reasons.


Using AI ethically to tackle covid-19

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Taking a principled approach is crucial to the successful use of AI in pandemic management, say Stephen Cave and colleagues In a crisis such as the covid-19 pandemic, governments and health services must act quickly and decisively to stop the spread of the disease. Artificial intelligence (AI), which in this context largely means increasingly powerful data driven algorithms, can be an important part of that action—for example, by helping to track the progress of a virus or to prioritise scarce resources.1 To save lives it might be tempting to deploy these technologies at speed and scale. Deployment of AI can affect a wide range of fundamental values, however, such as autonomy, privacy, and fairness. AI is much more likely to be beneficial, even in urgent situations, if those commissioning, designing, and deploying it take a systematically ethical approach from the start. Ethics is about considering the potential harms and benefits of an action in a principled way. For a widely deployed technology, this will lay a foundation of trustworthiness on which to build. Ethical deployment requires consulting widely and openly; thinking deeply and broadly about potential impacts; and being transparent about goals being pursued, trade-offs being made, and values guiding these decisions. In a pandemic, such processes should be accelerated, but not abandoned. Otherwise, two main dangers arise: firstly, the benefits of the technology could be outweighed by harmful side effects, and secondly, public trust could be lost.2 The first danger is that the potential benefits increase the incentive to deploy AI systems rapidly and at scale, but also increase the importance of an ethical approach. The speed of development limits the time available to test and assess a new technology, while the scale of deployment increases any negative consequences. Without forethought, this can lead to problems, such …


Applications of IoT for Healthcare

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Over the past few centuries, healthcare technology has come a long way--from the invention of the stethoscope in 1816 to robots performing surgery in 2020. As computers became more common starting in the 1960s and 1970s, researchers began to explore how they might enhance healthcare, and the first electronic health record (EHR) systems appeared by 1965 in the U.S. But it wasn't until the 1980s and 1990s that clinicians began to rely on computers for data management. Internet connectivity paved the way for much better data management, and EHRs became far more common in the 2000s. On the clinical side, healthcare technology improved greatly between the 1950s and the turn of the twenty-first century.


Machine learning is the new key to healthcare

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As healthcare professionals are facing massive pressure not only to ensure the quality of care, but also to come up with new solutions, cures and treatments, they are becoming increasingly dependent on advanced technologies like artificial intelligence (AI) and machine learning (ML). But it is hardly a smooth partnership. The issues of skills shortages at the entry-level and of "messy data" in leveraging patient records at the high end are merely book-ends for a range of challenges that span these fields. Last week's annual Amazon Web Services Re:Invent conference, one of the largest cloud-focused events in the world, saw the launch or demonstration of a range of new cloud-based tools that are ideal for health research and treatment. ML, defined as computer algorithms that improve automatically through experience, was at the heart of these.


Decoding the Booming Adoption of AI in Healthcare Market

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Owing to the COVID 19 pandemic, healthcare institutes are readily adopting artificial intelligence-based solutions for an effective outcome. It may seem that COVID 19 has initiated this adoption, but over the past few years, healthcare institutes have taken cognizance of this nascent technology. Due to its ability to analyse and process large datasets, healthcare institutes are deploying AI-models to make data-driven decisions. Big techs like IBM and Google are leveraging their respective products towards proactive data-driven healthcare advancements. For example, IBM Watson Health is a comprehensive product created to solve major health challenges using data, analytics and AI.


Google Launches Healthcare Natural Language API and AutoML Entity Extraction for Healthcare

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In a recent blog post, Google announced the public preview of two new fully-managed AI tools: Healthcare Natural Language API and AutoML Entity Extraction for Healthcare. Both tools can assist healthcare professionals in reviewing and analyzing medical documents in a repeatable, scalable way. By delivering these new tools, the public cloud vendor hopes to reduce workforce burnout and increase healthcare productivity, both in the back-office and in clinical practice. The new Healthcare Natural Language API uses Artificial Intelligence (AI) to help medical staff like doctors to extract the most pertinent information they need to know about their patients they treat from the stacks of medical records related to each individual. The API has been trained on thousands of medical documents to extract the information medical staff needs to know - using machine learning to classify clinically important attributes based on the surrounding context in medical records.