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Where I significantly noticed the productivity increase by the usage of Generative AI tools has been in my coding processes. During my bachelor degree, which was prior to the general launch of such GenAI tools, I learned coding “the hard way” and had to spend a lot of time on syntax- and semantics-errors, while generally only being able to create code for simpler tasks (e.g. to create a simple API connection).

Now, with the launch of innovative GenAI tools, I am suddently able to change the way I approach coding. Using for example the Microsoft Copilot which is embedded in the program “Visual Studio Code”, I am able to solve much more complex problems with a lot less effort. Using text-to-code tools enables “low-level-coders” like me to be way more productive and focus on creating the correct output instead of having to deal with syntax issues (McKinsey, 2023).

However, while these tools have greatly enhanced efficiency, I believe they are not perfect yet. For instance, I once had to build a web scraper for AirBnB to gather 100 listings, including their descriptions and prices per night. Building the general web scraper was quite easy, even though I had little prior experience. Nevertheless, achieving the final 5-10% of the quality required took considerable manual effort and time. In this case, while the initial code took about 30 minutes, I struggled to identify the correct HTML containers with Copilot’s help and had to find them myself (which took me hours…). As a result, I still needed to understand the code generated by GenAI, interpret what it was doing, and make adjustments on my own to achieve a fully functioning script.

(A quick look at the AirBnB Website and at one of the HTML divs I was trying to extract information from)

(an extract of the code I wrote; here you can see some “div”-examples I had to extract)

This experience highlighted to me that while Generative AI can automate many aspects of coding, a solid understanding of programming and the problem domain is still essential. AI-generated code is often a great starting point but rarely flawless. As a developer, you still have to refine the code and make adjustments where necessary, which required both technical knowledge and problem-solving skills.

In terms of improvement, I believe that integrating more advanced web page parsing capabilities and real-time feedback on the relevance of selected elements could further optimize the experience with such GenAI tools. Despite its limitations, Copilot’s potential to increase productivity in coding tasks is undeniable, and I look forward to seeing how it evolves.

Sources:

• The Program I used to Code: https://code.visualstudio.com/
• GenAI Tool (embedded in Visual Studio Code): https://copilot.microsoft.com/
• McKinsey. (2023). Unleashing developer productivity with generative AI. Retrieved from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai

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Remote surgery, also known as telesurgery, is increasingly becoming a reality due to the emergence of digital technologies. A recent pilot project has highlighted new possibilities, in which surgeons based in Switzerland conducted the world’s first remote tele-endoscopy on a pig (Mesot et al., 2024, 1). In this post, I would like to quickly explore this innovative technology as well as dive into the benefits and challenges.

As with most industries, digital disruption has redefined the boundaries of healthcare. Technologies that leverage AI and robotics, such as teleoperated magnetic endoscopy (the technology used to conduct the world’s first tele-endoscopy), are excellent examples of how digital platforms enable remote procedures. These technologies empower surgeons to perform operations without being physically present. This can be particularly useful in various situations, such as time-sensitive procedures or specialized surgeries where local expertise is unavailable. Moreover, this revolutionary technology could also serve as a useful platform to train local operators. (Mesot et al., 2024, 4–5)

Nevertheless, although this sounds very promising, there are a few challenges to keep in mind. The case study of the first tele-endoscopy, for example, highlighted the importance of a fast and reliable internet connection with low latency (Marescaux et al., 2002; Mesot et al., 2024, 2). Furthermore, it emphasized the need for the right equipment to allow remote surgery without introducing additional risks to the patient, which would not be present in a local surgery. Additionally, a robust communication platform between the remote surgeon and the local support operators is of crucial importance (Mesot et al., 2024, 4–5).

As digital technologies continue to improve and medical procedures gradually incorporate more modern innovations, it is increasingly safe to say that remote operations, as seen in the pilot project, are becoming a reality. It remains to be seen if, in the future, robotic surgeries will become commonplace.

Sources:

Marescaux, J., Leroy, J., Rubino, F., Smith, M., Vix, M., Simone, M., & Mutter, D. (2002). Transcontinental robot-assisted remote telesurgery: feasibility and potential applications. Annals of surgery, 235(4), 487–492. https://doi.org/10.1097/00000658-200204000-00005

Mesot, A., Mattille, M., Boehler, Q., Schmid, N., Lyttle, S., Heemeyer, F., Chan, S.M., Chiu, P.W.Y. and Nelson, B.J. (2024), Teleoperated Magnetic Endoscopy: A Case Study and Perspective. Adv. Intell. Syst. 2400522. https://doi.org/10.1002/aisy.202400522