Insights About Generative AI In Healthcare From 400 Healthcare Leaders

Recently, we were struck with an idea: Why not gauge our followers' perceptions regarding generative AI, Large Language Models, and their imminent role in healthcare? Based on how active and responsive The Medical Futurist audience is on our weekly LinkedIn polls, we were optimistic to see tangible results. If successful, our intention was to compile and present these findings in a comprehensive, free report.?

And here we are, today we are honored to share the results, informed by almost 400 profound responses from our fantastic community.

Download it for free!

Understanding the Landscape

Our primary objective was clear: decipher how medical professionals and affiliated individuals perceive the impending AI and LLM revolution. With these innovations steadily altering the healthcare paradigm, understanding shared anticipations is crucial.

The diversity of our respondents enriches the data:

• Physicians: 36.9%
• Non-healthcare professionals: 25.4%
• Company leaders: 22.8%
• Healthcare administrators: 11.3%
• Nurses: 3.7%

In terms of experience, most of our participants are seasoned professionals:

• Over 20 years of experience: 48%
• 10-20 years of experience: 22.7%
• 5-10 years of experience: 15.7%
• Under 5 years of experience: 13.6%

Three surprising findings from the survey

You have full access to the survey report with all the questions, so here we only want to highlight a few intriguing findings that emerged from the data.?

1. Diverse use cases

Analysing the data we found that a significant majority (over two-thirds) of our followers have already used ChatGPT or some other Large Language Model, occasional users being in slightly higher numbers than black-belted regulars. When browsing the answers we received to the question asking about typical use cases, we found a very diverse range of applications. Here are several interesting examples from our audience:?

• Verbal introduction in meetings on specific topics
• Assistance with writing code.
• New content creation
• Outpatient communication and creation of discharge notes
• Administrative texts, essay and text generation, ideation, and others
• Prepare presentations
• Proofreading and help with writing/editing
• Fun questions, non-medical
• Marketing support
• Differential diagnosis
• Med device development; legal document interpretation
• Sales messages
• Industry analysis, leadership impact, policy evaluation
• Compiling workshop feedback into summaries and quickly identifying common or reoccurring themes

2. Looking at prompt engineering from quite some distance

A salient point emerged around prompt engineering. A majority (57%) are acquainted with its concept. However, a closer look reveals only 45% have actively sought to refine these skills. Given the importance of prompt engineering in harnessing AI's full potential – an aspect we've discussed in great detail in this article – this skill gap warrants attention and educational interventions.

3. The future vision of LLMs in healthcare:?

The prevailing sentiment leans toward optimism about LLMs' contribution to healthcare. This underscores that our respondents possess a more informed understanding of the domain. Thus, even in the face of dire predictions by the mainstream media and current model limitations (e.g., occasional "hallucinations"), the majority remain upbeat, recognizing the transformative potential of this technology.

If you're as excited as we are about these findings, grab your copy of the full report now! As always, we value your feedback and encourage an active exchange of ideas. Your perspective enriches this ongoing dialogue.