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The last interesting genAI experience I would like to present in this blog post was during my internship at a major telecommunications firm. Different than my usual application of ChatGPT, I had to work with Microsoft Copilot. And let me tell you, I am not a fan. 

My team (of less tech-savvy employees I must add) was asked to implement Copilot in a new project. As the gen Z, tech enthusiast intern, I was told to lead this project with a colleague. The project itself was quite simple. It constitutes of a database which had to be summarized, analyzed, and finally presented to the customer. This database was nothing out of the ordinary, a few hundred columns and rows with basic inputs in MS Excel. After working and experimenting with ChatGPT since a good year or so, I was sure that basic prompts would work just as fine with MS Copilot. I was so wrong.

Copilot was able to tell me the largest/ smallest number in each category over the years stated in the database. 

That was about it. I prompted “create a pivot table including data from A2:G8 (for example)”. That, apparently, was too difficult to understand so I tried again. “Please create a pivot table using data, starting from cell A1 to cell G8. Include all data in this range”. It still did not work. 

Apart from the example of the pivot tables, analyzing this data turned out to be the most demanding task I got during that internship, since writing out the correct prompts alone cost me several hours. I must add, Copilot was just recently published for the private customer, and was far from perfect, however, thinking back to the beginnings of ChatGPT, the level of completeness in Copilot was far from the one of ChatGPT. I was happy that due to several issues with the AI, the project was closed a few weeks after. 

In general, MS Copilot is a brilliant idea. I could think of a lot of tasks in my Microsoft applications which would be simplified by implementing AI in the program itself. However, unless Copilot receives a major update, I think I will further rely on ChatGPT. 

(It has been over half a year since my internship, so Copilot might have evolved since then) 

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After the difficult and confusing times of COVID, people and businesses are slowly coming back to normal. The pandemic did not only affect our ways of living, working, but also our opportunities to receive medical care. Since the restrictions of COVID, Telemedicine has experienced a surge in the healthcare industry. 

Most of us have heard of telemedicine. It describes the use of communication technologies to provide patient consultations, diagnoses, and patient analysis remotely. In simpler terms, it provides an online doctor. During lockdown, and even after restrictions have been eliminated, patients had a hard time getting to their doctors without absurd waiting times for appointments (van Ginneken et al., 2022). “You have a headache? Alright, you can come see us March 2025”. Ignoring those waiting times, even after getting an appointment, patients might wait for hours just to have a 5-minute consultation. Telemedicine aims to resolve these issues. Telemedicine is accessible for anyone with internet and a mobile device. Patients from rural areas must not take an hour train to get to the next best doctor. Employees must not take up half their working day to get an annual check-up. Most importantly, telemedicine offers convenience (Stoltzfus et al., 2023). After COVID people got “lazy”. They are used to working from home, enjoy receiving takeout food, ordering groceries to their front door, and ultimately get a medical consultation online. If chores like these can be made more convenient, people are doing it. 

This convenience can be matched with high levels of accuracy using AI in the medical field. AI is revolutionising patient care in diagnostics and early detection, predictive analysis, personalised treatment plans, robotic surgery, and virtual health assistants. To name an example, AI was able to detect breast cancer years before doctors could diagnose it with their bare eyes (Pesapane et al., 2024). AI-driven chatbots and virtual health assistants provide initial consultations and health advice, streamlining the patient experience and reducing the burden on healthcare professionals. AI also enhances remote patient monitoring by analysing data from wearable devices to detect anomalies and alert providers in real time (Sharma et al., 2022). In Telemedicine, AI enhances the quality of the consultation by assisting with real-time decision support systems and data-driven insights, which might be missed without assistance. 

Both of these applications are disrupting the current healthcare industry. This new generation of medical care cannot replace doctors as we know them, but sure adds an immense value to it. Enhanced patient care and convenience, makes the pairing of AI and telemedicine an unbeatable opportunity for the medical care of tomorrow. 

List of references:

van Ginneken, E., Reed, S., Siciliani, L., Eriksen, A., Schlepper, L., Tille, F., & Zapata, T. (2022). The impact of the COVID-19 pandemic on noncommunicable disease resources and services: Results of a rapid assessment (Policy brief No. 47). World Health Organization. https://iris.who.int/bitstream/handle/10665/358832/Policy-brief-47-1997-8073-eng.pdf?sequence=1

Pesapane, F., Giambersio, E., Capetti, B., Monzani, D., Grasso, R., Nicosia, L., Rotili, A., Sorce, A., Meneghetti, L., Carriero, S., & others. (2024). Patients’ perceptions and attitudes to the use of artificial intelligence in breast cancer diagnosis: A narrative review. Life, 14(4), 454. https://doi.org/10.3390/life14040454

Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren J

Artificial Intelligence Applications in Health Care Practice: Scoping Review

J Med Internet Res 2022;24(10) DOI: 10.2196/40238 

Stoltzfus, M., Kaur, A., Chawla, A. et al. The role of telemedicine in healthcare: an overview and update. Egypt J Intern Med 35, 49 (2023). https://doi.org/10.1186/s43162-023-00234-z