Buzzword Generative AI and it's potential impact on Healthcare & Fintech
Recently, we have seen a lot of positive βbuzzβ around Generative AI, for instance by the recent funding announcement of Jasper.ai (Series A β¬125m), or the eagerly awaited launch of ChatGPT which conquered the VC and Twitter world by storm πͺοΈ - well, we also had shortly the meme time when Web3 VCs apparently turned Generative AI, but that is another story π
So now, I tried to collect my thoughts on it and express a couple of high-level thoughts on what impact it could have on two of the verticals I am looking at i) Healthcare & ii) Fintech on the very first layer.
π Laying the base, what is Generative AI?
Generative AI, also known as generative adversarial networks (GANs), is a type of AI that can create new data based on existing data. GANs use two neural networks - a generator and a discriminator - to create new data that is similar to the existing data. This has the potential to revolutionize various industries, such as healthcare and fintech, by providing new insights and solutions.
π₯ Potential Impact on Healthcare
In healthcare, generative AI could have a significant impact by providing new insights and solutions in various areas. Some potential areas where generative AI could have an impact on healthcare include:
Diagnosis and treatment
Generative AI could be used to create new medical images and data for use in diagnosis and treatment. For example, a GAN could be trained on a large dataset of medical images and be able to generate new images with high accuracy. This could be useful in cases where there is a lack of data, such as rare diseases (i.e. leukemia) or conditions.
Clinical trials
Further, it could be used in clinical trials to generate new data and insights, the networks could be trained on a large dataset of clinical trial data and be able to generate new data on top of it. This could be useful in identifying patterns and trends in the data, as well as in developing new treatment approaches.
Drug Discovery
Lastly, it could also be used in drug discovery to generate new molecules and compounds by generating new molecule data sets, which then could be useful in identifying new potential drugs and in speeding up the drug discovery process.
Overall, the potential impact of generative AI on healthcare is significant. It could lead to more accurate and efficient diagnosis and treatment, as well as new insights and solutions in other areas of healthcare.
π° Potential impact on Fintech
On the other side, it could also be used in fintech:
Fraud Detection
A GAN could be trained for financial transactions and be able to generate new transaction patterns, which could be used to identify patterns and anomalies that may indicate fraud or risk. By detecting fraud early, fintech companies could prevent losses and improve their risk management.
Payment Orchestration (most excited here!!!)
It could be used to create new and more efficient methods of managing and coordinating multiple payment channels and services. This could help streamline the payment orchestration process and make it more efficient for merchants and their customers.
Additionally, generative AI could be used to create more personalized and convenient payment experiences for users by generating customized payment plans and options based on their specific needs and preferences. This could make the payment process more seamless and user-friendly for merchants and their customers.
Overall, the use of generative AI in the payments industry has the potential to revolutionize the way that payments are made and processed. By providing new insights and solutions, generative AI could enable the development of new payment methods and technologies that are more convenient, secure, and efficient.
π Now, what do I expect from the next wave of startups in this space?
Startups are well-positioned to take advantage of the opportunities presented by generative AI, especially through superior access to a wide range of funding, as seen in the market in the last couple of quarters.
However, there are also challenges that startups will need to overcome in order to successfully integrate generative AI into their healthcare and fintech offerings. One of the main challenges is the need for large datasets to train GANs. Startups will need to secure access to high-quality datasets, which may be difficult and expensive.
Another challenge is the need for expertise in generative AI. While technology is rapidly advancing, it still requires specialized knowledge and skills to implement effectively. Startups will need to invest in the right talent and resources in order to successfully integrate generative AI into their products and services.
πͺ’ But where can startups get high-quality datasets from?
There are several potential sources for high-quality datasets for GANs (take no responsibility for correctness!)
Public data repositories
There are many public data repositories that contain a wide range of datasets, including medical and financial data. These repositories can be accessed by startups for free or for a fee, depending on the specific repository and dataset.
Partnerships with institutions
Startups can partner with institutions, such as hospitals and universities, to access their data for use in GANs. These partnerships can be valuable for both parties, as the institutions can benefit from the insights and solutions generated by the GANs, and the startups can benefit from access to high-quality data.
Crowdsourcing
Crowdsourcing to gather data for GANs. This involves enlisting a large number of people to contribute their data, such as medical images or financial transactions. Crowdsourcing can be an effective way to quickly gather a large amount of data, but it can also be difficult to ensure the quality and consistency of the data.
Generated data
In some cases, startups may be able to generate their own data for use in GANs. For example, they may be able to use simulation or other techniques to create data that is similar to real-world data. Generated data can be useful in cases where real-world data is not available or not suitable for training GANs.
The potential impact of generative AI on healthcare and fintech, but also other industries is massively exciting. As an investor, I am looking for startups that are leveraging this technology to drive innovation and growth in these sectors. I believe that generative AI has the potential to transform healthcare and fintech, and I am excited to see what the future holds for startups in these areas.
See you next time π¦
KD
πΆ Closing with a podcast recommendation πΆ