Well File:

Therapeutic Area


Using AI in the fight against cancer - Digital Journal

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Artificial intelligence, in the form of queried databases, is helping to tackle certain cancers. Algorithms have been developed to cross-reference a patient's medical records, habits and genetic information to spot any early signs of cancer. With cancer, the key problem is about the late diagnosis of cancer and the argument of using artificial intelligence is to identify those members of the population who are at greatest risk and to then bring them in earlier for screening. A pilot has been developed in the U.K., where the focus is particularly with prostate, lung and bowel cancer, and then you can undertake procedures like surgery or administer treatment sooner in order to increase survival rates. Similarly, a U.S. study, published in 2022, found that a machine learning algorithm trained to predict cancer outcomes zeroed in to finds the prostate on male patients and successfully outlines any cancer-suspicious areas without any human supervision.


What Is the Future Of Work? Technology or People?

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Over the past two years, there has been a heated debate on the future of work and its effect on businesses with exponential advancements in technology and artificial intelligence (AI). Many companies shut down as governments enforced the WHO protocols, lockdowns, cessation of movements and curfews. The Covid-19 pandemic has been detrimental to the financial economy. As the economy starts to open up again, the biggest question many are pondering is, "how do we navigate from this point?" The majority of people are now required to work from home due to the pandemic.



Waveform Segmentation Using Deep Learning - MATLAB & Simulink

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The electrical activity in the human heart can be measured as a sequence of amplitudes away from a baseline signal. The segmentation of these regions of ECG waveforms can provide the basis for measurements useful for assessing the overall health of the human heart and the presence of abnormalities [2]. Manually annotating each region of the ECG signal can be a tedious and time-consuming task. Signal processing and deep learning methods potentially can help streamline and automate region-of-interest annotation. This example uses ECG signals from the publicly available QT Database [3] [4].


Artificial intelligence is breaking patent law

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In 2020, a machine-learning algorithm helped researchers to develop a potent antibiotic that works against many pathogens (see Nature https://doi.org/ggm2p4; Artificial intelligence (AI) is also being used to aid vaccine development, drug design, materials discovery, space technology and ship design. Within a few years, numerous inventions could involve AI. This is creating one of the biggest threats patent systems have faced. Patent law is based on the assumption that inventors are human; it currently struggles to deal with an inventor that is a machine.


Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists' Intelligence?

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Background: Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the explosion of the amount of data; medicine is not an exception. Rather than replacing clinicians, AI is augmenting the intelligence of clinicians in diagnosis, prognosis, and treatment decisions. Summary: Kidney disease is a substantial medical and public health burden globally, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality as well as a huge economic burden. Even though the existing research and applied works have made certain contributions to more accurate prediction and better understanding of histologic pathology, there is a lot more work to be done and problems to solve. Key Messages: AI applications of diagnostics and prognostics for high-prevalence and high-morbidity types of nephropathy in medical-resource-inadequate areas need special attention; high-volume and high-quality data need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built. Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines; medicine is not an exception.


Construct a biomedical knowledge graph with NLP

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I have already demonstrated how to create a knowledge graph out of a Wikipedia page. However, since the post got a lot of attention, I've decided to explore other domains where using NLP techniques to construct a knowledge graph makes sense. In my opinion, the biomedical field is a prime example where representing the data as a graph makes sense as you are often analyzing interactions and relations between genes, diseases, drugs, proteins, and more. In the above visualization, we have ascorbic acid, also known as vitamin C, and some of its relations to other concepts. For example, it shows that vitamin C could be used to treat chronic gastritis.


AI and Machine Learning in the Management of Liver Disease

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Oral Abstract 74. Machine learning model outperforms noninvasive tests to detect fibrotic nonalcoholic steatohepatitis in patients with nonalcoholic …


Patients recognize role of artificial intelligence in diagnosis, treatment

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With artificial intelligence making its way into daily life, healthcare, including ophthalmology, is no exception. Ophthalmology, with its heavy reliance on imaging, is an innovator in the field of AI in medicine. Although the opportunities for patients and health care professionals are great, hurdles to fully integrating AI remain, including economic, ethical, and data-privacy issues. "AI is impacting health care at every level, from the provider to the payer to pharma," according to Dan Riskin, MD, CEO and founder of Verantos, a health care data company in Palo Alto, California, that uses AI to sort through real world evidence. The question remains, just how to patients feel about the use of AI in the diagnosis and treatment of their illnesses? In a patient survey conducted in December 2019, 66% of respondents said AI plays a large role in their diagnosis and treatment and thought it was important.


Building a learning culture with AI

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Building your organization's digital acumen is a critical priority for CIOs today. You won't be future-ready by simply hiring people with the most cutting-edge skills – you also need to create a culture where everyone is continually working on growing and evolving their skills together. I often tell my IT organization that the half-life of an IT professional is about 18 months because technology is constantly changing; thus, it is critical we are all continual learners. That's true not only at Johnson & Johnson; I've observed this across other companies and industries too. To support building our IT organization's digital acumen, we implemented a program that uses artificial intelligence to assess our skills.