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These Algorithms Look at X-Rays--and Somehow Detect Your Race

WIRED

Millions of dollars are being spent to develop artificial intelligence software that reads x-rays and other medical scans in hopes it can spot things doctors look for but sometimes miss, such as lung cancers. A new study reports that these algorithms can also see something doctors don't look for on such scans: a patient's race. The study authors and other medical AI experts say the results make it more crucial than ever to check that health algorithms perform fairly on people with different racial identities. Complicating that task: The authors themselves aren't sure what cues the algorithms they created use to predict a person's race. Evidence that algorithms can read race from a person's medical scans emerged from tests on five types of imagery used in radiology research, including chest and hand x-rays and mammograms.


Researchers Utilize GPS and AI in Cars to Identify Potential Alzheimer Cases

#artificialintelligence

Recently, researchers have been able to combine GPS with AI to detect early-onset Alzheimer's in drivers which a high degree of accuracy. Why is detecting Alzheimer's early important, what did the researchers achieve, and how does it demonstrate the importance of AI in medical diagnosis? There are very few individuals who enjoy arranging doctor appointments, having blood taken, and waiting for results. Despite the unpleasantness of such experiences, getting diagnosed as early as possible for diseases provides the best chance for treatment and survival. For example, many millions around the world still die as a result of perfectly treatable conditions such as prostate and breast cancer, and this is due to a lack of awareness and desire to get tested. Alzheimer's is a particularly nasty disease for a multitude of reasons.


Nicolas Babin disruptive week about Artificial Intelligence - August 2nd 2021 - Babin Business Consulting

#artificialintelligence

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. By examining Mayo Clinic data, nference used artificial intelligence to discover a correlation between anemia and long-term symptoms of COVID-19.


Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning – Digital Health and Patient Safety Platform

#artificialintelligence

Endoscopic resection is recommended for gastric neoplasms confined to mucosa or superficial submucosa. The determination of invasion depth is based on gross morphology assessed in endoscopic images, or on endoscopic ultrasound. These methods have limited accuracy and pose an inter-observer variability. Several studies developed deep-learning (DL) algorithms classifying invasion depth of gastric cancers. Nevertheless, these algorithms are intended to be used after definite diagnosis of gastric cancers, which is not always feasible in various gastric neoplasms.



ARTIFICIAL INTELLIGENCE, A TRANSFORMATIONAL FORCE FOR THE HEALTHCARE INDUSTRY

#artificialintelligence

Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.


Artificial Intelligence, a Transformational Force for the Healthcare Industry

#artificialintelligence

Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.


Top Advantages and Disadvantages of Artificial Intelligence

#artificialintelligence

One of the biggest advantages of AI is that it can significantly reduce errors and increase accuracy and precision. The decisions taken by AI in every step is decided by information previously gathered and a certain set of algorithms. When programmed properly, these errors can be reduced to null. Another big advantage of AI is that humans can overcome many risks by letting AI robots do them for us. Whether it be defusing a bomb, going to space, exploring the deepest parts of oceans, machines with metal bodies are resistant in nature and can survive unfriendly atmospheres.


Is AI the Future of Clinical Trials? - Digital Health Central

#artificialintelligence

Clinical Trials are the mandatory path for developing and bringing a new drug or vaccine to the market. Unfortunately, according to a study conducted by MIT, 86 percent of the drugs will fail during this process. This very high failure rate not only has consequences on the Pharmaceutical companies' bottom line, but it precludes potentially safe and efficacious drugs from reaching patients that could benefit from them. Recruitment is one of the main bottlenecks, is time-consuming, and very expensive. According to Chunhua Weng from Columbia University (New York), "Recruitment is the number one barrier to clinical research."


A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision

#artificialintelligence

Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revision. Here, we present a novel concept called artificial intelligence assisted contour revision (AIACR) and demonstrate its feasibility. The proposed clinical workflow of AIACR is as follows given an initial contour that requires a clinicians revision, the clinician indicates where a large revision is needed, and a trained deep learning (DL) model takes this input to update the contour. This process repeats until a clinically acceptable contour is achieved.