Goto

Collaborating Authors

health & medicine


AIhub monthly digest: February 2021

AIHub

Welcome to the second of our monthly digests, designed to keep you up-to-date with the happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. You may be aware that we are running a focus series on the UN sustainable development goals (SDG). Each month we tackle a different SDG and cover some of the AI research linked to that particular goal. In February it was the turn of climate action.


4 Reasons Why Workers Should Welcome Artificial Intelligence In the Workplace

#artificialintelligence

In recent months, concerns about the economic impact of the pandemic have been closely tied with a spate of panicked automation headlines like, "Will Robots Take Our Jobs In A Socially Distanced Era??". Already we have seen that incorporating new technologies has led to a dramatic shift in the way industries operate worldwide. We are also witnessing a significant rise in interest for robotic process automation (RPA), intelligent automation and artificial intelligence among business leaders who realize that intelligent automation demonstrates strong transformative potential across all industries. Business leaders are accelerating the adoption of technologies they view as crucial to digital transformation efforts – like intelligent and robotic process automation – to help them thrive in this tumultuous business environment and beyond. Businesses are constantly met with new restrictions and 63% of business decision makers feel they are struggling to meet customer demands.


Potential early diagnostic biomarkers of sepsis

#artificialintelligence

Objective: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. Methods: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression.


Clinical Risk Grouping Solutions Market - Global Forecast to 2024

#artificialintelligence

Rising consumer awareness regarding risk management and implementation of big data solutions are driving the market for clinical risk grouping software. Scorecard & visualization tools, dashboard analytics, and risk reporting are the three product types of clinical risk grouping solutions. Scorecard & visualization tools segment dominated the market with the largest share due to its ability to predict payment processes accurately and project per-patient risk. The rising need to reduce healthcare costs through these two channels is expected to augment the growth of the segment during the forecast period. Hospitals, payers, ambulatory care centers, and long-term care centers, among others are the end-users of clinical risk grouping solutions, of which hospitals accounted for the largest market share in 2018.


Budget 2021: Reactions From The Tech Industry

#artificialintelligence

Marking a significant shift in India's digital journey, the Union Finance Minister, Nirmala Sitharaman, has presented the first-ever digital budget for the upcoming fiscal year starting April 2021. Her budget speech touched upon "proliferation of technologies, especially analytics, machine learning, robotics, bioinformatics, and artificial intelligence." The budget has come at a time when the country is still struggling with the massive economic slowdown precipitated by COVID pandemic. However, despite this downturn, businesses have seen a significant push towards digitisation, including acknowledging the importance of artificial intelligence across industries. As a matter of fact, India is considered one of the fastest-growing digital markets globally.


Are Psychologists The Next Target For AI & Machine Learning?

#artificialintelligence

According to a WHO prediction, by 2020, roughly 20% of India will suffer from some mental illness and 450 million people currently suffer from a mental illness, worldwide. These numbers are a wake-up call that psychology as an issue and psychologists as a profession must be taken seriously. Such helping professions are often considered as human channels. Unlike manual workers whose job responsibilities are being taken over by machines and AI bots, psychiatrists and counselors see no threat to their professions with the advancements of machine learning and artificial intelligence. According to an influential survey of the future of employment by Carl Benedikt Frey and Micheal Osborne who are Oxford economists, the probability that psychology could be automated in the future is only 0.43%.


Smile for the camera: dark side of China's emotion-recognition tech

The Guardian

"Ordinary people here in China aren't happy about this technology but they have no choice. If the police say there have to be cameras in a community, people will just have to live with it. So says Chen Wei at Taigusys, a company specialising in emotion recognition technology, the latest evolution in the broader world of surveillance systems that play a part in nearly every aspect of Chinese society. Emotion-recognition technologies – in which facial expressions of anger, sadness, happiness and boredom, as well as other biometric data are tracked – are supposedly able to infer a person's feelings based on traits such as facial muscle movements, vocal tone, body movements and other biometric signals. It goes beyond facial-recognition technologies, which simply compare faces to determine a match. But similar to facial recognition, it involves the mass collection of sensitive personal data to track, monitor and profile people and uses machine learning to analyse expressions and other clues. The industry is booming in China, where since at least 2012, figures including President Xi Jinping have emphasised the creation of "positive energy" as part of an ideological campaign to encourage certain kinds of expression and limit others. Critics say the technology is based on a pseudo-science of stereotypes, and an increasing number of researchers, lawyers and rights activists believe it has serious implications for human rights, privacy and freedom of expression. With the global industry forecast to be worth nearly $36bn by 2023, growing at nearly 30% a year, rights groups say action needs to be taken now. The main office of Taigusys is tucked behind a few low-rise office buildings in Shenzhen. Visitors are greeted at the doorway by a series of cameras capturing their images on a big screen that displays body temperature, along with age estimates, and other statistics. Chen, a general manager at the company, says the system in the doorway is the company's bestseller at the moment because of high demand during the coronavirus pandemic. Chen hails emotion recognition as a way to predict dangerous behaviour by prisoners, detect potential criminals at police checkpoints, problem pupils in schools and elderly people experiencing dementia in care homes. Taigusys systems are installed in about 300 prisons, detention centres and remand facilities around China, connecting 60,000 cameras. "Violence and suicide are very common in detention centres," says Chen. "Even if police nowadays don't beat prisoners, they often try to wear them down by not allowing them to fall asleep.


Deep learning isn't hard anymore

#artificialintelligence

This had the effect of bottlenecking deep learning, limiting it to the few projects that met those conditions. Over the last couple years, however, things have changed. The driver behind this growth is transfer learning. Transfer learning, broadly, is the idea that the knowledge accumulated in a model trained for a specific task--say, identifying flowers in a photo--can be transferred to another model to assist in making predictions for a different, related task--like identifying melanomas on someone's skin. Note: If you want a more technical dive into transfer learning, Sebastian Ruder has written a fantastic primer.


Top Automation Trends to Watch in 2021

#artificialintelligence

Yes, automation has made its presence felt but with the pandemic that shook the entire world, organizations are now forced to rethink as to how will they proceed further. Even though the vaccine rollout promises returning back to normalcy, it definitely doesn't mean that we will return to business as usual. All this has made organizations look forward to exploring options in digital transformation, automation and artificial intelligence like never before. Some areas can surely see these being implemented in the coming days considering the fact how swiftly the pandemic forced us to change the way we look at life. The pandemic led to a complete lockdown at least for a few days in a majority of countries.


Cancer cases are on the rise: preparing now will pay off in the long-run

#artificialintelligence

Cancer care teams have faced unprecedented pressure over 2020. But out of these challenges have come important learnings about how we rethink cancer care for the future. Cancer Research UK anticipates that there will be 514,000 new cancer cases per year by 2035, an increase of more than 40%. These are significant numbers and we can not underestimate the pressure this will put on healthcare teams. Especially given that we are experiencing international shortages across all key cancer care professionals from radiation oncologists to specialist cancer nurses. Rising cases, combined with a shortage of skilled staff, could lead to a dangerous crisis point for cancer care.