Report from the AI trenches

As we know, when it comes to technology, doctors are from Venus and technologists are from Mars. So, it is commendable that Dr. Anthony Chang, the organizer of the AI in Medicine Conference, was able to get both in the same orbit in impressive numbers from countries around the world.

The latest conference was held in San Diego from June 4-7.

There is a culture clash between the techies and the clinicians. The former go fast and break things. The latter go slow and validate things. Some have even noted the differences in the approach between the West coast of the US and East coast.

Since the last conference, large language models have sucked most of the oxygen out of the room.

As a result, outsiders from a wide range of industries are sharing ideas about how to apply data lessons learned with sick care practitioners. Here are some of their observations:

1. The idea is to scale, not replace, humans with AI
2. There are many companies that provide data services tohospitals,s and they are trying to figure out what they offer: Data as a service? Data science as a service? AI as a service?
3. New business models are evolving. What if companies charged by the bot/time, much like power charges for a kilowatt/hour? Supply you could donate your bot time to the grid and get credit or unused capacity?
4. Everyone is sensitive to the hype cycle and shiny new objects and there are trying to under deliver and over perform
5. Humans evolve through tools and AI is no different. Technologies drive innovation
6. Sick care has increasingly become a data business that happens to take care of patients. We need a mission control to manage it all
7. Sick care will be fixed from outside in
8. We are a long way from artificial general intelligence or anything really "brain like". Like the internet, no one can predict the impact and unintended consequences it will have on society. Elon Musk thinks AI is far more dangerous than nukes.
9. Robotic process automation and computer vision, particularly as it applies to pattern recognition specialties, like radiology, is the tip of the spear. AI has already been implemented in a number of different ways in healthcare. There are three main areas of use: clinical decision support, clinical trials and hospital operations.
10. Integrating data is so 50's. The new thing is out grating data.
11. Global data awareness is the new platform
12. Data is the new oil. Those who produce and control it will make fortunes.
13. We are today in AI where computers were in the days of mainframes. Democratizing AI is the new frontier
14. Trust in AI will depend on security
15. Patient control of data will depend on trust
16. Facial recognition is coming to sick care. However, there are serious concerns about its ethical use and weaponizing it.
17. Here are the 2018 Shark Tank finalists
18. Increase in applications and investments. Here is a primer on how AI is being applied in medicine.
19. Increase in presence in academic journals
20. More books about AI in the lay press and applications to healthcare and medicine
21. The globalization of AI technology and innovation and AI clusters
22. The international AI community of interest and collaboration
23. Applications of AI in EMRs
24. Rethinking workflow and systems organization as sick care changes to healthcare

25. Translating data to experience to value.

26. Everyone in your organization needs to understand AI ethics

27. The AI/ML FDA regulatory pathways are evolving

28. While there are many predictions, the impact of Chat GPT is already being felt, but the long term impact remains to be seen.

29. AI is impacting operations. Evidence is accumulating that AI is safe and effective for many use cases.

30. Regulatory standards are evolving internationally

31. Workforce shortages are pushing AI dissemination and implementation

33. AI medical education and training programs are spreading internationally, push by medical students and residents

The Stanford 2021 AI Index report concludes:

AI investment in drug design and discovery increased significantly: “Drugs, Cancer, Molecular, Drug Discovery” received the greatest amount of private AI investment in 2020, with more than USD 13.8 billion, 4.5 times higher than 2019.

The industry shift continues: In 2019, 65% of graduating North American PhDs in AI went into industry—up from 44.4% in 2010, highlighting the greater role industry has begun to play in AI development.

Generative everything: AI systems can now compose text, audio, and images to a sufficiently high standard that humans have a hard time telling the difference between synthetic and non-synthetic outputs for some constrained applications of the technology.

AI has a diversity challenge: In 2019, 45% new U.S. resident AI PhD graduates were white—by comparison, 2.4% were African American and 3.2% were Hispanic.

China overtakes the US in AI journal citations: After surpassing the United States in the total number of journal publications several years ago, China now also leads in journal citations; however, the United States has consistently (and significantly) more AI conference papers (which are also more heavily cited) than China over the last decade.

The majority of the US AI PhD grads are from abroad—and they’re staying in the US: The percentage of international students among new AI PhDs in North America continued to rise in 2019, to 64.3%—a 4.3% increase from 2018. Among foreign graduates, 81.8% stayed in the United States and 8.6% have taken jobs outside the United States.

Surveillance technologies are fast, cheap, and increasingly ubiquitous: The technologies necessary for large-scale surveillance are rapidly maturing, with techniques for image classification, face recognition, video analysis, and voice identification all seeing significant progress in 2020.

AI ethics lacks benchmarks and consensus: Though a number of groups are producing a range of qualitative or normative outputs in the AI ethics domain, the field generally lacks benchmarks that can be used to measure or assess the relationship between broader societal discussions about technology development and the development of the technology itself. Furthermore, researchers and civil society view AI ethics as more important than industrial organizations.

AI has gained the attention of the U.S. Congress: The 116th Congress is the most AI-focused congressional session in history with the number of mentions of AI in congressional record more than triple that of the 115th Congress.?

?The FDA has recognized that AI and machine learning technologies pose a number of challenges from a regulatory perspective. And a key challenge here is that when FDA regulates software as a medical device, there's a general question about how to determine when changes to a software algorithm are so significant that they merit reevaluation of the software product, its safety and effectiveness.?

Here are the most pressing AI legal issues.

Here is a series of podcasts about AI and the future of healthcare.

McKinsey estimates that AI techniques have the potential to create between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries. This constitutes about 40 percent of the overall $9.5 trillion to $15.4 trillion annual impact that could potentially be enabled by all analytical techniques?

Some day soon, the new invisible hand of AI will be part and parcel of how doctors take care of patients. But, like an AI algorithm, it will take a lot of trial and error to avoid crashes.

Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs on Twitter@SoPEOfficial and Co-editor of Digital Health Entrepreneurship