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Artificial Intelligence and Machine Learning for Healthcare and Anesthesiology




Technological innovations like Artificial Intelligence and Machine learning allow the detection of valuable patterns within large datasets which – after been subjected to enough data – allows the algorithms to perform predictions on previously unseen data subjects. Such intelligent software has been used extensively in different fields of the healthcare industry – including neurology, cardiology, and oncology – with the purpose of aiding medical personnel with disease diagnostics, disease prevention, and personalized medical treatment [1].

However, previous attempts to incorporate machine learning within anesthesiology – which is the field within the healthcare industry that focuses on providing perioperative care to patients – have been unsuccessful [2]. This article will provide an overview of the difficulties that arise when automating the field of anesthesiology.

Difficulties with automating anesthesiology procedures

Generally, systems for the automation of anesthesiology procedures rely on a closed-loop feedback system which is able to successfully keep a quantifiable target measure – usually the bispectral index (BIS) when assessing depth of anesthesia – within a pre-defined range [1] [3]. Using various drug administration rates – which depend on the measured BIS level – the patient’s level of consciousness can be controlled in an autonomous way.

Various studies have shown that the use of such closed-loop feedback systems could be beneficial in the context of keeping the patient’s level of consciousness within a pre-determined BIS range, with the additional benefit of providing a lower dose of anesthetic in comparison to the human-controlled case [4] [5] [6]. Whereas there is evidence that closed-loop feedback systems are feasible to assist in guaranteeing required anesthetic levels for both simple and more complex cases [7], they by no means are able to fully automate the – usually human-controlled – process.

However, innovations such as Artificial Intelligence – which implement a bottom-up rather than a top-down approach like rule-based feedback loops – are able to learn to take the required patient’s level of consciousness actions from real-world patient data without being explicitly programmed to. Whereas these algorithms are able to tackle tasks that are much more complex in comparison to rule-based systems, in practice they still possess flaws which require the need of a professional anesthetist during medical interventions:

  1. Artificial Intelligence is especially well-suited for performing cognitive tasks (i.e., carrying out accurate predictions and crunching large data sets). However, the technology is yet unable to deliver the dexterity-based labor that is involved with the field of anesthesiology. [8]
  • Artificial Intelligence and Machine Learning – implemented in robotic devices – do not have the finesse to deal with complex tasks such as neural blockades, venous cannulation or tracheal intubation. [8]
  • The field of anesthesiology is characterized by providing micro-doses in order to remain the required level of patient consciousness. However, patients are uncomfortable with the thought of replacing a human anesthetist with fully autonomous decision-making software without human control.


Whereas current procedures – such as rule-based systems or artificial intelligence – are yet unable to fully take over human anesthetic tasks, they are thought to play a major role in the future of anesthesiology. Computer software – powered by artificial intelligence – will ultimately aid in all decisions made by anesthetist and, when innovations in robotics allow it, take over dexterity-based labor as well.


[1] Murali, Nivetha & Sivakumaran, Nivethika. (2018). Artificial Intelligence in Healthcare-A Review. 10.13140/RG.2.2.27265.92003.

[2] Alexander, J. C., & Joshi, G. P. (2018, January). Anesthesiology, automation, and artificial intelligence. In Baylor University Medical Center Proceedings (Vol. 31, No. 1, pp. 117-119). Taylor & Francis.

[3] Kissin, I. (2000). Depth of anesthesia and bispectral index monitoring. Anesthesia & Analgesia90(5), 1114-1117.

[4] Brogi, E., Cyr, S., Kazan, R., Giunta, F., & Hemmerling, T. M. (2017). Clinical performance and safety of closed-loop systems: a systematic review and meta-analysis of randomized controlled trials. Anesthesia & Analgesia124(2), 446-455.

[5] Pasin, L., Nardelli, P., Pintaudi, M., Greco, M., Zambon, M., Cabrini, L., & Zangrillo, A. (2017). Closed-loop delivery systems versus manually controlled administration of total IV anesthesia: a meta-analysis of randomized clinical trials. Anesthesia & Analgesia124(2), 456-464.

[6] Puri, G. D., Mathew, P. J., Biswas, I., Dutta, A., Sood, J., Gombar, S., … & Arora, I. (2016). A multicenter evaluation of a closed-loop anesthesia delivery system: a randomized controlled trial. Anesthesia & Analgesia122(1), 106-114.

[7] Zaouter, C., Hemmerling, T. M., Lanchon, R., Valoti, E., Remy, A., Leuillet, S., & Ouattara, A. (2016). The feasibility of a completely automated total IV anesthesia drug delivery system for cardiac surgery. Anesthesia & Analgesia123(4), 885-893.

[8] Angie, D. (2018). 6 insights on how artificial intelligence could transform anesthesia. Becker’s ASC Review. Obtained from:

Mark David is a writer best known for his science fiction, but over the course of his life he published more than sixty books of fiction and non-fiction, including children's books, poetry, short stories, essays, and young-adult fiction. He publishes news on related to the science.


As ChatGPT turns one, big tech is in charge




As ChatGPT turns one, big tech is in charge

The AI revolution has arrived a year after ChatGPT’s historic release, but any uncertainty about Big Tech’s dominance has been eliminated by the recent boardroom crisis at OpenAI, the company behind the super app.

In a sense, the covert introduction of ChatGPT on November 30 of last year was the geeks’ retaliation, the unsung engineers and researchers who have been working silently behind the scenes to develop generative AI.

With the release of ChatGPT, OpenAI CEO Sam Altman—a well-known figure in the tech community but little known outside of it—ensured that this underappreciated AI technology would receive the attention it merits.

With its rapid adoption, ChatGPT became the most popular app ever (until Meta’s Threads took over). Users were amazed at how quickly the app could generate poems, recipes, and other content from the internet.

Thanks to his risk-taking, Altman, a 38-year-old Stanford dropout, became a household name and became a sort of AI philosopher king, with tycoons and world leaders following his every word.

As for AI, “you’re in the business of making and selling things you can’t put your hands on,” according to Margaret O’Mara, a historian from the University of Washington and the author of “The Code,” a history of Silicon Valley.

“Having a figurehead of someone who can explain it, especially when it’s advanced technology, is really important,” she added.

The supporters of OpenAI are sure that if they are allowed unrestricted access to capital and freedom to develop artificial general intelligence (AGI) that is on par with or superior to human intellect, the world will be a better place.

However, the enormous expenses of that holy mission compelled an alliance with Microsoft, the second-biggest corporation in the world, whose primary objective is profit rather than altruism.

In order to help justify Microsoft’s $13 billion investment in OpenAI earlier this year, Altman steered the company toward profitability.

This ultimately led to the boardroom uprising this month among those who think the money-makers should be kept at bay, including the chief scientist of OpenAI.

When the battle broke out, Microsoft stood up for Altman, and the young employees of OpenAI supported him as well. They understood that the company’s future depended on the profits that kept the computers running, not on grand theories about how or why not to use AI.

Since ChatGPT launched a year ago, there has been conflict over whether AI will save the world or end it.

For instance, just months after signing a letter advocating for a halt to AI advancements, Elon Musk launched his own business, xAI, entering a crowded market.

In addition to investing in AI startups, Google, Meta, and Amazon have all incorporated AI promises into their corporate announcements.

Businesses across all industries are registering to test AI, whether it be through magic wands or killer robots, usually from OpenAI or through cloud providers like Microsoft, Google, or Amazon.

“The time from learning that generative AI was a thing to actually deciding to spend time building applications around it has been the shortest I’ve ever seen for any type of technology,” said Rowan Curran, an analyst at Forrester Research.

However, concerns are still widespread that bots could “hallucinate,” producing inaccurate, absurd, or offensive content, so business efforts are currently being kept to a minimum.

In the aftermath of the boardroom drama, tech behemoths like Microsoft, which may soon have a seat on the company’s board, will write the next chapter in AI history.

“We saw yet another Silicon Valley battle between the idealists and the capitalists, and the capitalists won,” said historian O’Mara.

The next chapter in AI will also not be written without Nvidia, the company that makes the graphics processing unit, or GPU—a potent chip that is essential to AI training.

Tech behemoth, startup, or researcher—you have to get your hands on those hard-to-find and pricey Taiwan-made chips.

Leading digital firms, such as Microsoft, Amazon, and Google, are leading the way.

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Amazon is launching Q, an AI business chatbot




Amazon is launching Q, an AI business chatbot

The announcement was made by Amazon in response to competitors who have introduced chatbots that have drawn attention from the public. It was made in Las Vegas during an annual conference the company organizes for its AWS cloud computing service.

San Francisco-based startup A year ago, OpenAI released ChatGPT, which ignited a wave of interest in generative AI tools among the general public and industry. These tools can produce textual content such as essays, marketing pitches, emails, and other passages that bear similarities to human writing.

Microsoft, the primary partner and financial supporter of OpenAI, benefited initially from this attention. It owns the rights to the underlying technology of ChatGPT and has utilized it to create its own generative AI tools, called Copilot.

However, it also encouraged rivals like Google to release their own iterations.

These chatbots represent a new wave of artificial intelligence (AI) that can converse, produce text on demand, and even create original images and videos based on their extensive library of digital books, online articles, and other media.

Q, according to Amazon, is capable of helping staff with tasks, streamlining daily communications, and synthesizing content.

It stated that in order to receive a more relevant and customized experience, businesses can also link Q to their own data and systems.

Although Amazon is seen as the leader in AI research, it is not as dominant as competitors Microsoft and Google when it comes to cloud computing.

According to the researchers, among other issues, less transparency may make it more difficult for users of the technology to determine whether they can depend on it safely.

In the meantime, the business has kept up its AI exploration.

In September, Anthropic, a San Francisco-based AI start-up founded by former OpenAI employees, announced that Amazon would invest up to $4 billion (£3.1 billion) in the business.

Along with new services, the tech giant has been releasing AI-generated summaries and an update for its well-liked assistant Alexa, which allows users to have more human-like conversations. of customer reviews for products.

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WatchGuard reveals 2024 cybersecurity threats forecasted




WatchGuard reveals 2024 cybersecurity threats forecasted

The world leader in unified cybersecurity, WatchGuard Technologies, recently released information about their predictions for cybersecurity in 2024. Researchers from WatchGuard’s Threat Lab predict that in 2024, a variety of new technologies and advancements will open the door for new cyberthreats. Large language models (LLMs), AI-based voice chatbots, and contemporary VR/MR headsets are a few possible areas of focus. Managed service providers (MSPs) play a big part in thwarting these threats.

“Every new technology trend opens up new attack vectors for cybercriminals,” Said WatchGuard Technologies’ Chief Security Officer, Corey Nachreiner. The persistent lack of cybersecurity skills will present the cybersecurity industry with difficult challenges in 2024. As a result, MSPs, unified security, and automated platforms are more crucial than ever for shielding businesses from ever-more-complex threats.

The Threat Lab team at WatchGuard has identified a number of possible threats for 2024. Large Language Models (LLMs) will be one major area of concern as attackers may use LLMs to obtain confidential information. With 3.4 million cybersecurity jobs available globally and a dearth of cybersecurity expertise, MSPs are expected to focus heavily on security services utilizing AI and ML-based automated platforms.

Artificial intelligence (AI) spear phishing tool sales on the dark web are predicted to soar in 2024. These AI-powered programs can carry out time-consuming operations like automatically gathering information, creating persuasive texts, and sending spam emails. Additionally, the team predicts a rise in voice phishing or “vishing” calls that use deepfake audio and LLMs to completely bypass human intervention.

The exploitation of virtual and mixed reality (VR/MR) headsets may pose a growing threat in 2024. Researchers from Threat Lab claim that hackers might be able to obtain sensor data from VR/MR headsets and replicate the user environment, leading to significant security breaches. The widespread use of QR code technology may not come without risks. The group predicts that in 2024, a significant cyberattack will occur when a worker scans a malicious QR code.

These professional observations from the WatchGuard Threat Lab team center on the convergence of artificial intelligence and technology. It is anticipated that in the future, entities of all sizes, will depend more heavily on managed and security service providers due to the rapid advancements in AI technology and the accompanying cybersecurity threats.

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