GenAI as a lifesaver

21

October

2023

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Generative AI has been around since the 1960s already but gained its mainstream breakthrough in 2022 (White, 2023). This is when OpenAI launched ChatGPT and the whole world could not stop talking about AI. At first, people started experimenting with silly stuff such as writing a blog or making a recipe with certain ingredients. It was all fun and games until people started to realize the real possibilities GenAI could have. This new GenAI thing… Could it eventually replace people? Does it make me obsolete? Will it eventually destroy people? This all crossed people’s minds. It may be pessimistic, but it is good to ask these kinds of questions when new technology becomes available. Of course, these questions were already asked in the past, but it now became part of the public debate. 

In these situations, there are also people who see opportunities. So are people in the healthcare industry. The increasing costs of healthcare pose a big threat to the health of billions of people (Bodenheimer, 2005). This asks for a proper solution. This is where GenAI can play a big role. Although the possibilities are limitless, we will only discuss the (what I think) most important ones. Namely Electronic Health Records (EHRs), patient engagement and personalized patient care. 

EHRs are crucial in managing patient information. But nowadays, this data is not used to its full power. Integrating GenAI in EHRs can automate the conversation summarization during appointments. This will save doctors a lot of time and will provide them with much better quality of data (Nova, 2023). In addition, GenAI can be used to engage with patients. There can be a chatbot at which patients can ask questions 24/7. This has already been experimented with in a couple of countries (Zhang, 2023). Furthermore, it could be used to personalize patient care. By accessing comprehensive patient data, including genetic information and medical history, generative AI can assist healthcare professionals in making informed treatment decisions, predicting intervention efficacy, and tailoring care plans to individual needs, leading to enhanced patient outcomes (Nova, 2023).

As I already mentioned, the possibilities are limitless. It is very interesting to learn about both proponents and opponents to this technological breakthrough. I would love to see more research into this topic and maybe some of you feel interested as well after this blog. What do you think about the use of GenAI in healthcare?

Bodenheimer, T. (2005). High and rising health care costs. Part 1: seeking an explanation. Annals of internal medicine, 142(10), 847-854.

Nova, K. (2023). Generative AI in healthcare: advancements in electronic health records, facilitating medical languages, and personalized patient care. Journal of Advanced Analytics in Healthcare Management, 7(1), 115-131.

White, M. (2023, July 8). A brief history of Generative AI – Matt White – medium. Medium. https://matthewdwhite.medium.com/a-brief-history-of-generative-ai-cb1837e67106

Zhang, P., & Kamel Boulos, M. N. (2023). Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges. Future Internet, 15(9), 286.

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Digital prototyping with Dall-E 3

18

October

2023

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This year has seen the increasing popularity of many text-to-image tools such as Midjourney and Dall-E 2. I did not have any prior experiences with these tools, but when I opened up ChatGPT this week I was surprised with the option to try the beta version of Dall-E 3 (unfortunately only available for ChatGPT Plus if you want to try…). The fact that this was now included in a tool I already used, left me with no other choice than to experiment with the generator. While I started out with prompts such as “Create a painting in Picasso style of a cat eating a burger” or “Create a painting of an F1 racing car in New York 100 years ago”, I eventually started to see the potential to use Dall-E 3 for real-world applications.

One of the applications that makes me enthusiastic is the use of Dall-E 3 for digital prototyping. I could not find a lot of literature or sources for this application, which makes it a perfect subject to start a discussion on. This application will be useful for especially less complicated products or for marketeers. To illustrate this, we should first have a look at what capabilities make the tool suitable for digital prototyping. Firstly, Dall-E 3 offers the opportunity for rapid visualization. Traditionally, it is very time consuming and complex to translate the idea that is in your brain to a useful design. With Dall-E 3, it is possible to just describe your idea and insert a prompt. The tool will provide you with the first results and will significantly speed up the initial stages of prototyping. It also makes it accessible for people that do not have the skills to design a prototype. After this you will get a first round of generated images. The second advantage Dall-E 3 offers, is that you can easily create numerous variations if you are not completely satisfied yet. Both advantages will bridge the gap between idea and visualization, and with that create cost efficiencies for the businesses using this tool.

Now that we know what kind of advantages Dall-E 3 offers for digital prototyping, we should talk about the practical applications. Imagine you are a designer of clothes, and you want to generate visual representation of an idea for a print. You insert your prompt and seconds later you have a first draft of your designs. Or in another situation you are a marketeer who wants to see how their designed clothes will look in different cultural settings. With Dall-E 3 you can simulate various situations and adjust the product accordingly. This can help with strategy planning.          

Of course, this is just a small glimpse of the endless possibilities that Dall-E 3 and other text-to-image tools offer. It will be very interesting to see where this journey will take us in the coming years. I am curious what you guys think. Will this technology change business or is it just a hype?

The results of a prompt asking for a sample pattern for clothes on the left side and on the rights side on of the results that was provided for a white t-shirt in different cultural settings.

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