Monthly Archives: January 2022

Novant Health's integration of Aidoc's AI solutions, amid the latest wave of the Omicron variant, expands upon its existing slate of innovative technologies designed to improve delivery of patient care and outcomes

NEW YORK, Jan. 24, 2022 /PRNewswire/ -- Aidoc, the leading provider of enterprise-grade AI solutions for medical imaging, announces a partnership with Novant Health, a health network of over 1,800 physicians with 15 medical centers across three states. By incorporating Aidoc's AI platform, which includes seven FDA-cleared solutions for triage and notification of patients with acute medical conditions, Novant Health is taking proactive steps to improve patient outcomes and reduce emergency department (ED) length of stay amid resource constraints inflicted by the Omicron variant.

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With a dedication to digital transformation for improving workflow efficiencies and patient outcomes, Novant Health is one of the first health networks in North Carolina to adopt Aidoc's AI platform. Novant Health has integrated multiple technologies and has been recognized by the College of Healthcare Information Management Executives' (CHIME) "Digital Health Most Wired" program five years in a row for effectively applying "core and advanced technologies into their clinical and business programs to improve health and care in their communities."

"When diagnosing and treating critical pathologies like pulmonary emboli and hemorrhagic strokes, every second counts," said Dr. Eric Eskioglu, Executive Vice President Chief Medical and Scientific Officer, Novant Health. "We are thrilled to partner with Aidoc to bring yet another leading-edge AI-technology to Novant Health. For years, we've been committed to harnessing innovative technologies to improve patient safety and outcomes through the Novant Health Institute of Innovation and Artificial Intelligence. With Aidoc's technology, our physicians will be able to more quickly identify and prioritize these patients and provide rapid life-saving treatments."

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From Aidoc's AI platform, Novant Health will be utilizing the intracranial hemorrhage (brain bleeds), pulmonary embolism (lung blood clots), incidental pulmonary embolism, c-spine fracture, and abdominal free air AI solutions. In one example, a study conducted by the Yale New-Haven Health System found that Aidoc's intracranial hemorrhage AI solution was able to reduce ED length of stay by approximately one hour.

"With rapidly rising numbers of people infected with the highly contagious Omicron variant, we can see the hard impact on hospital emergency room capacities and resources across the U.S.," says Elad Walach, CEO and co-founder of Aidoc. "We're proud to partner with a leading, innovative hospital network like Novant Health, which serves a large portion of the population in the three states its facilities are located in. Together, through our AI solutions and their state-of-the-art facilities, we will enable radiologists and related hospital providers to expedite care for tens of thousands of patients, contributing toward a mitigation of the current emergency room situations and setting an example for integrating innovation during turbulent and non-turbulent periods."

About Aidoc

Aidoc delivers the most comprehensive and widely-used portfolio of AI solutions, supporting providers by flagging patients with suspected acute conditions in real-time, expediting patient treatment and improving quality of care. Aidoc's healthcare AI platform is currently used by thousands of physicians in hospitals and radiology groups worldwide and across multiple care coordination service lines, having analyzed over 10.3 million scans in the past year. For more information, visit http://www.aidoc.com.

About Novant Health

Novant Health is an integrated network of physician clinics, outpatient facilities and hospitals that delivers a seamless and convenient healthcare experience to communities in North Carolina, South Carolina, and Georgia. The Novant Health network consists of more than 1,800 physicians and over 35,000 employees who provide care at nearly 800 locations, including 15 hospitals and hundreds of outpatient facilities and physician clinics. In 2021, Novant Health was the highest-ranking healthcare system in North Carolina to be included on Forbes' Best Employers for Diversity list. Diversity MBA Magazine ranked Novant Health first in the nation on its 2021 list of "Best Places for Women & Diverse Managers to Work." In 2020, Novant Health provided more than $1.02 billion in community benefit, including financial assistance and services.

For more information, please visit our website at NovantHealth.org. You can also follow us on Twitter and Facebook.

Ariella ShohamVP Marketing ariella@aidoc.com

Cision

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Aidoc partners with Novant Health, providing imaging AI to expedite treatment for patients in the emergency department - Yahoo Finance

Perception systems can be defined as a machine or Edge device, which has embedded advanced intelligence, which can perceive its surroundings, taking meaningful abstractions out of it and allow itself to take some decisions in real-time, said Pradeep Sukumaran, VP, AI&Cloud at Ignitarium, at the Machine Learning Developers Summit (MLDS) in his talk titled Hardware Accelerators in the World of Perception AI.

The key components of perception system AI include sensing systems like camera, Lidar, Radar, and microphone.

Pradeep says, Looking at the cost and power parameters, and now with the advent of Deep Learning, which is a subset of ML, and availability of some very interesting hardware options, I think this has opened up the use of Deep Learning. In some cases, completely replacing the traditional signal processing algorithms going way beyond what was done earlier, in terms of the amount of data it can process and also in some cases, there is a combination of traditional signal processing with Deep Learning.

Perception AI: Use cases

Automotive and Robotics

Sensors guide a truck from source to destination on dedicated lanes in the trucking industry. There are also lower end-use cases like robotics which can be used for services or delivery where the robots use sensors to understand their surroundings to find their way around.

Predictive maintenance

Companies use vibration sensors attached to motors to understand specific signatures. And these are typically done using ESP pattern recognition, but now they are being replaced by ML and Deep Learning which can be used by low power hardware.

Surveillance

Surveillance is done with a combination of Deep Learning and specialises hardware in the multimodal use cases. Now, there are multiple sensors with audio and video combined, trying to get information from the surroundings. The 2D cameras with 3D LiDARS can be used in traffic junctions to monitor vehicles and pedestrian movement. Sometimes, the 2D cameras miss out on many images due to excessive light or rain or environmental conditions obstructing standard cameras. 3D LiDAR can actually detect objects in such conditions and use a combination of these two to get the traffic pattern for a more intelligent traffic management system.

Medical equipment

The medical field is also using Deep Learning and FPGAs specifically for smart surgery, smart surgical equipment etc.

Edge AI

General Purpose Hardware like the CPU, DSP , GPU is strapped to DNN Engine.

Deep learning models require specific hardware to run it efficiently. These are called DNN engines. So they are strapping these to the CPUs and DSPs and the GPUs, basically allowing the CPUs to offload some of the work to these engines that are tightly coupled to the same chip. The general-purpose hardware is now getting variance and is tuned for AI.

FPGAs are programmable devices, and the companies providing FPGAs want to enable AI in their key applications across industries. They want to get high performance with low power where you can write the code, burn it in the FPGA, and design it on the field. The trade off is the lack of software developer friendliness. The developers have to use hardware to implement neural nets. However, companies are building tools and SDKs that make it easier, but still its a long way to go.

ASICs are basically application specific integrated circuits specifically designed for AI workloads.

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Hardware accelerators in the world of Perception AI - Analytics India Magazine

Data scientist Darshana Govind believes that STEM and data science especially artificial intelligence are great fields for female researchers.

Its challenging because you dont see a lot of women in the field, says Govind, who recently earned her doctorate in computational cell biology, anatomy and pathology from the Jacobs School of Medicine and Biomedical Sciences at UB. Id like to see more women join STEM (science, technology, engineering and math) and data science. Its a great field to be in. Its hard to be one female in a room full of men, so I encourage more women to join AI teams.

Realizing the potential of AI to make a difference in peoples lives by transforming health care is what really drew me to the field, she explains. Plus, its exciting to be a part of groundbreaking research, especially when youre surrounded by brilliant researchers from whom you get to learn every day. Ive been able to learn a lot of new science and engineering by being part of a field with a rapid pace of development that is multidisciplinary.

While at UB, Govind conducted her research in the lab of her mentor, Pinaki Sarder, associate professor of pathology and anatomical sciences. Sarder is a big supporter of her work.

One of my goals at UB is not only to do research, but also to develop a workforce, and thats very important, Sarder says. Darshana has done excellent, very difficult work for her PhD and has been published in a top journal. He notes that while the situation is improving, there still arent many women working in artificial intelligence right now.

Govind, who now works as a data scientist at Janssen Pharmaceuticals, a division of Johnson & Johnson, is a strong proponent of women in data science and AI.

Data science and AI have enabled us to leverage petabytes of data to extract meaningful information in a variety of different fields. In health care, we are now able to mine volumes of medical data to optimize patient diagnosis and treatment response. Its a game-changer, and we need more data scientists, says Govind, whose doctoral degree is to be conferred next month. Unfortunately, there is currently a major gender gap in this field, with less than one-third of data scientists being women. Its important to have women play an equal role in this industry and incorporate our voices and perspectives while developing major impactful technologies.

Additionally, this field is fueled by creativity and innovation, and we need as many diverse minds as possible to come up with novel solutions to critical problems, she adds.

Allison Brashear, vice president for health sciences and dean of the Jacobs School, notes that its no secret that men tend to outnumber women majoring in the STEM fields in college. Part of the problem is that gender stereotypes and a shortage of diverse role models perpetuate gender STEM gaps, Brashear says. In higher education, its of utmost importance that we increase opportunities in STEM for women. Although some progress has been made in recruiting women to some fields, like biological sciences and computer science, we still have a long way to go toward narrowing the gender pay gap in STEM careers and ensuring a more diverse body of STEM researchers in higher education.

I commend Dr. Govind for actively encouraging more women to enter STEM fields. Now more than ever, women at the start of their educational journeys need support and access to fields where they are underrepresented, she adds.

Govind says notable women like Joy Buolamwini, whose TED Talk on algorithmic bias has more than 1 million views, and Fei-Fei Li, co-director of Stanford Universitys Human-Centered AI Institute, are at the forefront of AI and have played a major role in encouraging more inclusion and diversity in the field. In addition, organizations like Women in Data Science and Women in AI have enabled the formation of large communities that support women and minorities in the field.

That being said, we are still vastly underrepresented in this field, Govind says, and I believe all of us have a role to play in encouraging and empowering women to close this gender gap.

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UB-educated data scientist urges more women to work in AI - UB Now: News and views for UB faculty and staff - University at Buffalo Reporter

Monday 24 January 2022 9:00 am

MDOTM, a London-based fintech providing AI-driven investment strategies, has acquired the team of Mercurius Betting Intelligence, a company specialized in betting models and sports prediction with artificial intelligence.

Following the 6.2m (5.1m) Series-B round raised in September 2021 which brought MDOTMs total funding to 8.2m the agreement will incorporate Mercurius skills in AI applied to game theory, rare events forecasting and tail risk management into MDOTM expertise. The acquisition also makes the firm one of Europe and the UKs largest investment-focused AI teams with more than 50 data scientists and finance experts split between London and Milan.

Founded by a group of mathematicians in 2017, Mercurius Betting Intelligence specialises in AI technology to control, trade and capitalise on sports betting markets.

The company uses proprietary deep learning algorithms to systematically analyse millions of data points per match, in order to find and exploit inefficient odds in the betting markets.

The unique know-how brought by Mercurius will keep bringing our clients the best technology for investment decision-making, Tommaso Migliore, CEO and founder of MDOTM, said.

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AI-driven fintech MDOTM bets on game theory - City A.M.

IBM is to sell off the data assets of its AI-powered Watson Health operation to private equity firm Francisco Partners, in move which pretty much ends the story for the beleaguered healthcare unit.

Announced on Friday, exact details of the deal were undisclosed, but media reports suggested a sale of more than $1bn.

The sell-off marks an end to IBMs healthcare ambitions, as the Armonk, NY based giant concentrates on its hybrid cloud business. Where reports earlier in the month had suggested IBM was looking to sell off the entirety of the poorly performing unit, last weeks deal is a sale of Watson Healths data and analytics assets.

IBM had previously built out Watson Health through a series of acquisitions totalling more than $4bn. The division facilitated medical research and solution making with a proprietary tool powered by artificial intelligence (AI) and the Watson supercomputer.

Todays agreement with Francisco Partners is a clear next step as IBM becomes even more focused on our platform-based hybrid cloud and AI strategy, said Tom Rosamilia, Senior Vice President, IBM Software in a statement.

IBM remains committed to Watson, our broader AI business, and to the clients and partners we support in healthcare IT.

Trouble first hit Watson Health when a 2013 venture with MD Anderson Cancer Center to eradicate cancer promised more than it delivered. The project closed in 2017 after a total spend of $62m.

Other healthcare providers pulled away from the service, implying a lack of efficacy. 2018 saw an undisclosed number of layoffs at Watson Health, with its chief stepping down in the same year.

As of 2021, the unit recorded $1bn in annual revenue and no profit.

The move echoes McDonalds recent AI sell-off due to disappointing results, and rumours of an AI winter a period of diminished interest and investment in AI, as has happened more than once already have been circulating in recent times. But its unlikely Big Blue will move completely away from Watson or AI in the immediate future.

IBM, which has been pivoting away from healthcare ventures following the appointment of new CEO Arvind Krishna, is instead refocusing its AI tools to different sectors.

The companys latest AI products show a pivot towards customer engagement, worker productivity and technician aids.

AI in healthcare meanwhile remains a lucrative field:Microsoft acquired the UKs Nuance Communications for $19.7bn last April. The companys primary offering is an AI tool that transcribes doctors notes, along with customer service calls and voicemails to healthcare providers.

GlobalDataforecasts in a recent report on AI in healthcare that the market for AI platforms for the entire health industry will reach $4.3bn by 2024.

IBM Watson Health should be a cautionary tale for all AI vendors, says GlobalData research director Ed Thomas. It demonstrates the potential consequences of overpromising and underdelivering.

The irony is that IBM is getting out of healthcare technology just when others are making big bets on the sector. Oracle bought Cerner for $28 billion in December, while healthcare expertise is a significant factor behind Microsofts $20 billion move for Nuance.

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IBM sells off Watson AI healthcare unit - Verdict

If you wish to sharpen your skills in AI and data science, you should apply for these hackathons in 2022

With the growing popularity of data science and AI, more people are interested in learning about this sector and there is no better way to learn something through more and more practice and updating the skills. Hackathons are events where people from different corners can come together under the name of competition sharpen their skills and learn more about their competitors. Here are the top AI and data science hackathons for you to apply in 2022.

HackerEarth is a good place for Beginners. It is a place where programmers from all over the world come together to solve problems in a wide range of Computer Science domains such as algorithms, machine learning, or artificial intelligence, as well as to practice different programming paradigms like functional programming.

MachineHack is an online platform for Machine Learning competitions. It is a growing platform with a mission to support the ever-growing data science community and help young aspirants learn and improve their skills in the field of analytics. They host tough business problems that can only find solutions in Machine Learning & Data Science.

Data science hackathons on DataHack enable you to compete with leading data scientists and machine learning experts in the world. This is your chance to work on real-life data science problems, improve your skillset, learn from expert data science and machine learning professionals, and hack your way to the top of the hackathon leaderboard! You also stand a chance to win prizes and get a job at your dream data science company.

The WiDS Datathon aims to inspire women worldwide to learn more about data science, and to create a supportive environment for women to connect with others in their community who share their interests. This years WiDS Datathon, organized by the WiDS Worldwide team, Harvard University IACS, and the WiDS Datathon Committee, will tackle a key way to mitigate the effects of climate change with a focus on energy efficiency. The WiDS Datathon Committee is partnering with experts from many disciplines at Climate Change AI, Lawrence Berkeley National Laboratory (Berkeley Lab), US Environmental Protection Agency (EPA), and MIT Critical Data. WiDS Datathon participants will analyze regional differences in building energy efficiency, creating models to predict building energy consumption.

For this years Helmholtz GPU Hackathon the organizers have partnered with the Helmholtz Information & Data Science Academy and the AI Campus in Berlin as well as NVIDIA and OpenACC.org, to provide a unique opportunity to all scientists and industry partners to accelerate their AI research and/or HPC codes. All teams will receive expert mentorship from academia and industry leaders to work in a collaborative environment. The Helmholtz GPU Hackathon is a multi-day event designed to help teams of three to six developers accelerate their own codes on GPUs using a programming model or machine learning framework of their choice. Each team is assigned mentors for the duration of the event.

Smart India Hackathon 2022 is a nationwide initiative to provide students a platform to solve some of the pressing problems we face in our daily lives, and thus inculcate a culture of product innovation and a mindset of problem-solving. The themes of this hackathon vary from smart automation to heritage and culture and fitness and many more.

This is an AI-based hackathon that is centered in Canada. The competition is sprint styled and it aims to enable students from all kinds of backgrounds to come together and innovate something unique. It is presented by McMaster artificial intelligence society. The Hackathon will take place from January 21st to 23rd.

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Top AI and Data Science Hackathons to Apply for in 2022 - Analytics Insight

Analysing big data to optimise assets and leverage predictive algorithms and artificial intelligence (AI) to boost efficiency and enhance capacity will be essential skills for the tech-driven future.

Its time for recruiting teams to leverage the power of AI software to help them source, filter, and hire the best applications. The winners of this category at the HR Vendors of the Year 2021 provide the best tools that are designed to simplify and make mainstream the process of recruitment, providing HR professionals a game-changing way to hire top talent from any field.

Gold: SHLhttps://www.shl.com/This email address is being protected from spambots. You need JavaScript enabled to view it.

Silver: The Talent Gameshttps://thetalentgames.com/This email address is being protected from spambots. You need JavaScript enabled to view it.

Bronze: TALENTCLOUD AIhttps://www.talentcloud.ai/ This email address is being protected from spambots. You need JavaScript enabled to view it.

Gold: SHLhttps://www.shl.com/

Silver: PeopleStronghttps://www.peoplestrong.com/sg/This email address is being protected from spambots. You need JavaScript enabled to view it.

Bronze: The Talent Gameshttps://thetalentgames.com/This email address is being protected from spambots. You need JavaScript enabled to view it.

To find out more about the awards or to participate in the 2022 edition:

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Follow us onTelegramand onInstagram@humanresourcesonlinefor all the latest HR and manpower news from around the region

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Best AI recruitment tools in Malaysia and Singapore - Human Resources Online

Autonomous driving technology company Pony.ai has introduced its 6th generation autonomous driving (AD) system, with leading-edge sensors, NVIDIA DRIVE computing platform solutions, and styling and design features for L4 automotive-grade mass production fleets. The first model equipped with this system, Toyota S-AM, a seven-seat hybrid electric platform for autonomous mobility (earlier post), will start road testing in China this year and be deployed within Pony.ais public-facing robotaxi operations in the first half of 2023.

Pony.ai next-generation autonomous driving system on Toyota Sienna Autono-MaaS (S-AM) platform. (

Pony.ai started to develop the self-driving capabilities for the S-AM China model together with Toyota as early as 2019. Pony.ai believes this highly customized S-AMwith its dual redundancy systemwill provide better functionality and control performance for Level 4 AD development. In addition, as an MPV model, the S-AM has a flexible riding configuration to meet numerous family and professional usage scenarios.

The sensor solution, comprising 23 sensors, includes four solid-state LiDARs on the roof, covering a 360 field of view; three near-range LiDARs on the body of the vehicle, covering the blind spots of the roof LiDARs; four millimeter-wave radars located at the corners of the roof; one long-range forward-facing millimeter-wave radar, and 11 cameras deployed around the roof and body of the vehicle (in a combination of wide angle, super wide angle, middle and long-range, and traffic light detection cameras).

The central mechanical LiDAR has been replaced with solid-state LiDARs. The self-developed traffic light camera has a resolution rate 1.5 times that of the previous generation. Coupled with its in-house Sensor Fusion technology, Pony.ai will significantly reduce the cost associated with the solution by utilizing mass-produced, automotive-grade sensors.

Pony.ai is also introducing its next-generation mass-production autonomous computing unit, using the NVIDIA DRIVE Orin. This builds on Pony.ai and NVIDIAs relationship, which dates back to 2017 when the company first adopted the NVIDIA DRIVE platform.

The Pony.ai next-generation autonomous computing platform, built on NVIDIA DRIVE Orin. NVIDIA DRIVE Orin achieves 254 TOPS (trillion operations per second) of performance, and includes comprehensive CUDA and NVIDIA deep learning accelerator (NVDLA) toolchain support.

Pony.ais autonomous computing unit features low latency, high performance, and high reliability. The company is one of the first in the autonomous vehicle industry to create a product portfolio featuring multiple configurations with one or more DRIVE Orin processors and automotive-grade NVIDIA Ampere architecture GPUs. This enables scalable deployment across self-driving trucks and robotaxis, and accelerates Pony.ais future of a robust, mass-production platform for autonomous vehicles.

Compared with the previous generation computing platform, the new generation is expected to have greater than a 30% increase in computing power, at least 30% less weight, and more than a 30% reduction in cost.

The new generation system has complete redundancy in place, which maximizes safety and enables the vehicle to pull over or stop safely in case of an emergency.

In the S-AM model, Pony.ai is also unveiling a concept design for the new AD system, making the sensor suite more integrated and aesthetically pleasing while also more effective for mass production.

Among other design features, this concept design includes rooftop signaling units which have a horizontal lighting unit on the front and three vertically placed lighting stripes on the rear, to give the vehicle an elegant futuristic touch. By using different colors and lighting combinations, the rooftop signaling units can provide external communication and interaction, demonstrating the robotaxis status and intentions.

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Pony.ai introduces 6th generation autonomous driving system design; road-testing in Toyota S-AM this year - Green Car Congress

For the first time ever, scientists have shown that artificial intelligence (AI) could lead to better ways to predict the onset and course of cardiovascular disease.

A new AI-based system developed by scientists at University of Utah Health (Salt Lake City, Utah, USA) mines Electronic Health Records (EHRs) and assesses the combined effects of various risk factors to better predict the onset and outcomes of heart disease. The researchers developed unique computational tools to precisely measure the synergistic effects of existing medical conditions on the heart and blood vessels.

The researchers say this comprehensive approach could help physicians foresee, prevent, or treat serious heart problems, perhaps even before a patient is aware of the underlying condition. Although the study only focused on cardiovascular disease, the researchers believe it could have far broader implications. In fact, they suggest that these findings could eventually lead to a new era of personalized, preventive medicine. Doctors would proactively contact patients to alert them to potential ailments and what can be done to alleviate the problem.

Current methods for calculating the combined effects of various risk factors - such as demographics and medical history - on cardiovascular disease are often imprecise and subjective. As a result, these methods fail to identify certain interactions that could have profound effects on the health of the heart and blood vessels. To more accurately measure how these interactions, also known as comorbidities, influence health, the researchers used machine learning software to sort through more than 1.6 million EHRs after names and other identifying information were deleted.

These electronic records, which document everything that happens to a patient, including lab tests, diagnoses, medication usage, and medical procedures, helped the researchers identify the comorbidities most likely to aggravate a particular medical condition such as cardiovascular disease. In their current study, the researchers used a form of AI called probabilistic graphical networks (PGM) to calculate how any combination of these comorbidities could influence the risks associated with heart transplants, congenital heart disease, or sinoatrial node dysfunction (SND, a disruption or failure of the hearts natural pacemaker).

Among adults, the researchers found that individuals who had a prior diagnosis of cardiomyopathy (disease of the heart muscle) were at 86 times higher risk of needing a heart transplant than those who didnt. They also found that those who had viral myocarditis had about a 60 times higher risk of requiring a heart transplant. In addition the usage of milrinone, a vasodilating drug used to treat heart failure, pushed the transplant risk 175 times. This was the strongest individual predictor of heart transplant. In some instances, the combined risk was even greater. For instance, among patients who had cardiomyopathy and were taking milrinone, the risk of needing a heart transplant was 405 times higher than it was for those whose hearts were healthier.

Comorbidities had a significantly different influence on the transplant risk among children, according to the researchers. Overall, the risk of pediatric heart transplant ranged from 17 to 102 times higher than children who didnt have pre-existing heart conditions, depending on the underlying diagnosis. The researchers also examined influences that a mothers health during pregnancy had on her children. Women who had high blood pressure during pregnancy were about twice as likely to give birth to infants who had congenital heart and circulatory problems. Children with Down syndrome had about three times greater risk of having a heart anomaly. Infants who had Fontan surgery, a procedure that corrects a congenital blood flow defect in the heart, were about 20 times more likely to develop SND heart rate dysfunction than those who didnt need the surgery. The researchers also detected important demographic differences. For instance, a Hispanic patient with atrial fibrillation (rapid heartbeat) had twice the risk of SND compared with Blacks and Whites, who had similar medical histories.

We can turn to AI to help refine the risk for virtually every medical diagnosis, said Martin Tristani-Firouzi, M.D. the studys corresponding author and a pediatric cardiologist at U of U Health. The risk of cancer, the risk of thyroid surgery, the risk of diabetesany medical term you can imagine.

This novel technology demonstrates that we can estimate the risk for medical complications with precision and can even determine medicines that are better for individual patients, said Josh Bonkowsky, M.D. Ph.D., Director of the Primary Childrens Center for Personalized Medicine, who believes this research could lead to development of a practical clinical tool for patient care.

Related Links:University of Utah Health

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AI Predicts Cardiovascular Disease Before Patient Becomes Aware of Underlying Condition - HospiMedica

Humans have been obsessed with immortality since time immemorial. While immortality continues to be a distant dream, science has taken strides in getting humans close to immortality--by increasing the life expectancy and making sure people feel younger, longer. But the secret to immortality lies in understanding the genomics of an individual, observing not just how to treat them for a disease but preventing the disease from happening at the first place.

Precision medicine is an area that's focused on getting you custom-made medicine based on not just your medical history but also your family background, work history, living situation etc. This ensures that for the same problem two different individuals with widely different backgrounds are not given the same one-size-fits-all medicine--ensuring you respond to medication better and there's no guess work in getting you the right treatment.

This is where artificial intelligence comes to the rescue. AI's use in bespoke medicines can truly revolutionize the healthcare industry and that's why in today's lead story, we delve into this topic and explain the role AI really plays in the future of medicine.

Do share your thoughts.

RegardsVarun AggarwalEditor, ETCIOvarun.aggarwal@timesinternet.in

Would you taste these bespoke medicines made with the help of AI?

Researchers and experts have been using Artificial Intelligence in precision medicine to study and better understand genomics of an individual. This study and understanding helps to predict if you are going to have cancer or diabetes in the later years, basis which the right treatment can be given to prevent the disease.

Precision medicine goes deep in the DNA to look for the driver of a disease, what caused it, what drugs will the DNA respond to. This helps healthcare providers alter and tailor the treatments they offer.Read more..

AI at par with specialists in diagnosing prostate cancer: Study

Fifteen of the algorithms were selected to have their performance measured against diagnoses made by specialist uropathologists and general pathologists.Read More

IBM sells its Watson healthcare assets to Francisco Partners

Watson was one of IBM's highest-profile initiatives in recent years and a big bet on the growing healthcare sector. IBM currently has a market value of $108 billion, way behind its Cloud-computing rivals like Amazon and Microsoft. IBM Watson was one of the "strategic imperatives" under former CEO Ginni Rometty. Read more

Google AI tools bring back women in science to the fore

Google has developed new machine learning (ML) tools for use by curators at the Smithsonian in the US to help uncover and highlight many roles women have played in science over more than 174 years of history. Read more

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AI is bringing science fiction to life - ETCIO.com