This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q\&A) systems, a crucial form of Natural Language Processing. Traditionally, human evaluation has been indispensable for assessing the quality of these responses. However, manual evaluation by medical professionals is time-consuming and costly. Our study examines whether LLMs can reliably replicate human evaluations by using questions derived from patient data, thereby saving valuable time for medical experts. While the findings suggest promising results, further research is needed to address more specific or complex questions that were beyond the scope of this initial investigation.

Options in games like God of War Ragnarök and Street Fighter 6 help lessen unintentional barriers, introducing newcomers to previously inaccessible franchises and allowing others to join new communities. And accessible design innovations, awareness, and accommodations help create games we can all enjoy. Accessibility is equally important in the industry workspace. While it's great to buy a game for your Xbox or PlayStation and find dozens of accessibility features, disabled employees need systematic support to bring characters to life. Disabled voice actors shared with WIRED the ways their disabilities impact their work, and the importance of an inclusive industry.

The development of plans of action in disaster response scenarios is a time-consuming process. Large Language Models (LLMs) offer a powerful solution to expedite this process through in-context learning. This study presents DisasterResponseGPT, an algorithm that leverages LLMs to generate valid plans of action quickly by incorporating disaster response and planning guidelines in the initial prompt. In DisasterResponseGPT, users input the scenario description and receive a plan of action as output. The proposed method generates multiple plans within seconds, which can be further refined following the user's feedback. Preliminary results indicate that the plans of action developed by DisasterResponseGPT are comparable to human-generated ones while offering greater ease of modification in real-time. This approach has the potential to revolutionize disaster response operations by enabling rapid updates and adjustments during the plan's execution.

Robots are not limited to industrial works anymore! Thanks to the integration of artificial intelligence and voice recognition, robots are slowly invading our smart homes embedded with devices like wireless security cameras, Smart TVs, Amazon's Alexa, Amazon Echo, Google Assistant, Philips Hue lightbulbs, Ecobee4, etc. And it is not a secret that machine learning software development is on rise now. A lot of clients are coming to develop personalized ML solutions for their businesses. ABI Research predicts that this integration will grow, and by 2024 that over 79 million homes in the world will have a robot in the house.

Aktana, the leader in intelligent customer engagement for the global life sciences industry, reports new indicators of a shift in artificial intelligence (AI) implementation strategy in commercial life sciences. Notably, Aktana crossed a milestone of 30 million next-best-engagement (NBE) suggestions, including an unprecedented 45% increase in AI-driven omnichannel actions, directly impacting the customer experience of more than 700,000 healthcare professionals in 2021 alone. Leading pharmaceutical companies are now scaling AI capabilities simultaneously across multiple geographies, therapeutic areas and brands rather than taking a market-by-market approach. "Rather than just validating the value of AI, life sciences commercial and medical teams are now realizing the value of AI – in a big way" LEO Pharma, for instance, is expanding use of Aktana's Contextual Intelligence 360 platform across multiple European countries and Canada. "We want to leverage the power of AI to better support healthcare professionals with relevant information when they need it throughout the omnichannel journey," said Marc Tàpies Llobet, senior director, commercial excellence in the EU at LEO. "The rollout of Aktana's solution globally is also key to increase the effectiveness of all our commercial teams with helpful next-best-action suggestions and insights."

An article published in medical journal The Lancet on February 25 finds that reduced medical resource levels will trigger a spike in the coronavirus death rate in the local population beyond the current estimates. The study shows that death rates are over 3 percent in Wuhan city, 2.9 percent in Hubei province, while only 0.7 percent across the rest of China. Close to 30,000 medical staff from across China have been dispatched to Hubei province to help overworked local medical professionals in the fight against COVID-19. Fast and accurate diagnosis is critical on the front line, and now an AI-powered diagnostic assessment system is helping Hubei medical teams do just that. Currently, CT lung scans and nucleic acid tests are the two main diagnostic tools doctors use in confirming COVID-19 infections.

Artificial intelligence has accurately predicted the possibility of heart attack or stroke in a world's first. A study led by Barts Health NHS Trust and University College London used AI to analyse cardiac scans of more than 1,000 patients. Researchers said it's the first time blood flow scans, which reveal problems with the heart, have been read by a computer. The technology was more accurate at predicting major cardiovascular events within a 19-month follow-up than a doctor using traditional means. Researchers said it could be used by medical teams to recommend treatments.

Chatbots hit a hot streak in 2016. It seemed like every week there was a new bot. Many of these bots faded as fast as they came on the scene. Most likely, they weren't backed up with a proper business model, and many in the industry overestimated the capacity of chatbot and AI to tackle problems. Early on, we realized that a pure AI chatbot would never be able to replace a physician or benefits expert. Our solution to the problem was the "human-augmented chatbot."

Chatbots hit a hot streak in 2016. It seemed like every week there was a new bot. Many of these bots faded as fast as they came on the scene. Most likely they weren't backed up with a proper business model, and many of them overestimated the capacity of chatbot and AI to tackle problems. We quickly realized that a pure AI chatbot would never be able to replace a physician or benefits expert. Our solution to the problem was the "human-augmented chatbot."