ANDREW GROJEANHow Generative AI and Synthetic
Content Will Revolutionize Healthcare
A few short months ago, ChatGPT was released to theworld and it’s already changing the conversation aroundAI. This tool is the latest to use generative AI and has
Microsoft, Google, and others vying to win the generative
AI race.
Whether you’ve seen deep fakes of Tom Cruise, heard
Darth Vader’s new voice, or AI winning art contests, you’ve
probably already been exposed to content generated by AI
one way or another.
The current state of the technology is rapidly evolving. Ina matter of weeks, AI has made tremendous strides in thearea of generative models and synthetic content. It has thepotential to revolutionize everything from entertainment toadvertising. We’ll look at existing and future applications
and the challenges that must be overcome for it to reach its
full potential.

What is Synthetic Content andGenerative AI?
There’s a ton of terminology overlap in this field.
Generative AI, ChatGPT, and synthetic content are all
related to the field of Artificial Intelligence (AI) and Natural
Language Processing (NLP). However, they each havedistinct characteristics and applications.
Generative AI is a type of AI that is able to create newcontent based on the data it has been trained on. This caninclude text, images, and audio.
ChatGPT is a specific type of generative AI built by
OpenAI. It is a large language model that is trained togenerate human-like text using natural human languagein prompts. It uses a large amount of data to generate
text that is often indistinguishable from that written bya human. ChatGPT can be used in healthcare to help
generate medical reports, summaries, and to assist in
medical research and data analysis.
Synthetic content refers to the creation of new content
that is not based on existing data. It is created by amachine or algorithm, often also using natural language

Associate Director, Innovation

inputs. DALL-E, Stable Diffusion, and MidJourney are alltools that help create synthetic content producing realistic
images from basic prompts like “an astronaut riding ahorse” or “a chair that looks like an avocado.” The systemanalyzes the prompt and can return images in a variety ofstyles in a matter of seconds. Artists are using these tools
for inspiration to efficiently create concepts that they canbuild on to save time in the creative process.

How is this Technology Being Used Today?
Here are several ways brands are already using thistechnology to engage with consumers:
1.

Creating headlines, marketing copy, and image assets
for ads and social media posts

2.

Chatting with customers to answer common questions
and product issues

3.

Generating virtual try-ons with custom avatars andcontent

4.

Helping design immersive metaverse experiencesusing text to produce videos and 3D assets

5.

Assisting copywriters by drafting outlines for complex
educational content (like this one)

6.

Expediting A/B testing by creating a variety ofmessaging and personalized content quickly

7.

Reducing costs for video production – creating videos
for training and education using virtual avatars thathave customizable appearances, voices, and settings

8.

Helping programmers write and test code for websites
and applications

This list isn’t exhaustive and grows by the day, but we’realready seeing innovative ways for brands to use generative
AI to help reach their audience.

1 | © Copyright 2023 EVERSANA | EVERSANA.COM

What to Expect in 2023 and Beyond
Even though powerful tools have already been builtwith this tech, we’re still in the infancy for generative AI.
ChatGPT is built on a language model called GPT-3 thathas 175 billion parameters (800 GB of data). While we are
eagerly waiting for confirmations from OpenAI, according
to some reports, GPT-4 is expected to be released in thefirst part of 2023, with 170 trillion parameters. The scale ofimprovement means future models will be trained on moredata, more recent data, and could enable new use cases.
Other companies are also racing to release their own
large language models and generative AI tools. Google’sversion, LaMDA, was announced in 2021, but released tothe public for limited beta testing in late 2022. LaMDA
will eventually be used to help with Google Search andAssistant. Besides LaMDA, Google is also planning on
releasing Sparrow in 2023. Sparrow might be similar toChatGPT in functionality or even more powerful with theresources of Google behind it.
Microsoft has a significant partnership with OpenAI andhas already announced it will integrate GPT-3 into theirproducts. Microsoft announced the release of Teams
Premium with GPT 3.5 and there are reports of integrating
ChatGPT like features with the Bing search engine.
This is an inflection point. Like in the early days of internet
and email, the now-novel technology with soon becommonplace.

Impact for Healthcare
Generative AI and synthetic content have the potential togreatly impact every industry including healthcare and life
sciences companies.
One application is the use of synthetic images and videos
to create realistic and engaging product demonstrations
and advertisements. For example, companies could create
virtual tours of medical facilities, or use synthetic images
and videos to demonstrate the features and benefits ofmedical equipment or devices. This could help to increase
the effectiveness of healthcare marketing campaigns andmake it easier for healthcare providers to communicatecomplex information to patients and other stakeholders.
Another potential application is the use of generative AI
to create unique and personalized content for healthcare
marketing campaigns. For example, generative AI could
be used to create personalized medical information andtreatment plans for patients based on their individual
medical history and needs. This could help to increase
the effectiveness of healthcare marketing campaigns andimprove patient engagement and adherence.

2 | © Copyright 2023 EVERSANA | EVERSANA.COM

Once generative AI is integrated into enterprise systems,
new plugins and integrations will make healthcare
education and patient interactions a lot easier.
Pairing these systems with healthcare-specific AI platforms
like Cognitive Core, doctors will be able to respondquicker, generate pre-approval forms, create relevant andaccurate healthcare documents, and more patient-friendly
content.
Google and DeepMind built a language model specificallyfor healthcare called Med-PaLM. Their research papersays Med-PaLM can help with clinical decision support,
summarizing key findings in studies, and triaging patients’
primary care concerns.
Synthetic content and generative AI could also be usedin medical education and training. For example, synthetic
simulations and virtual patients could be used to train
medical students and professionals with virtual avatars withreal clinical dilemmas.
Using these AI tools for efficiencies in medical, legal, andregulatory approvals may help expedite feedback andapprovals.
However, it’s important to keep in mind that synthetic
content and generative AI have ethical implications,especially in the healthcare field. For example, synthetic
images and videos that depict unrealistic results oftreatments may create false expectations and trust issues.
Additionally, the use of generative AI in healthcare may
raise privacy and security concerns depending on where
the data is sourced.
Humans will always play a key role in the healthcare
process, especially in healthcare and life sciences whenlives and livelihoods are on the line. Even language models
built specifically for healthcare are no replacement forclinicians. Human judgment and creativity will still drive thisprocess, even when brands take an AI-first approach.
This technology is moving fast, so we’ll be keeping a pulseon the industry for new opportunities and considerations
for pharma.