☀️ TikTok's Viral Misinfo

Welcome to your briefing:

• AI ROUNDUP: Google CEO admits its AI totally screwed up

• INDUSTRY ROUNDUP: 4 emerging strategies to advance big data analytics in healthcare

• WELLNESS BYTES: Brain’s dual code for social memory unraveled

• INSIGHTS CORNER: Leader Spotlight: Emily Lewis, AI & Innovation Lead, Neurology at UCB

• TRIVIA: What percentage of the world's data is generated by the healthcare industry, and how much data does the average hospital produce annually?

• HEALTHCARE CONFERENCES: April conferences added

AI ROUNDUP

• Google CEO admits its AI totally screwed up (Read More)

• ViVE 2024: Health systems are making big bets on AI. Here's how (Read More)

• Mayo Clinic: Advancing rare disease breakthroughs with genomics, AI and innovation (Read More)

• McKinsey & Co.: Public health’s inflection point with generative AI (Read More)

• Cedars Sinai: AI captures electrocardiogram patterns that could signal a future sudden cardiac arrest (Read More)

• NLP framework could improve medical summarization tools (Read More)

• JAMA: Blind Spots, shortcuts, and automation bias. Researchers are aiming to improve AI clinical models (Read More)

• Ema, a startup building conversational AI for Women's Health, raises nearly $2M (Read More)

Hey…a question for you

INDUSTRY ROUNDUP

• DOJ launches antitrust probe into UnitedHealth (Read More)

• 4 emerging strategies to advance big data analytics in healthcare (Read More)

• White House issues executive order to protect Americans’ sensitive personal data (Read More)

• TikTok sparks misinformation, fears about mysterious virus spread (Read More)

• I’m an Amazon patient now: How the tech giant is taking on health care (Read More)

WELLNESS BYTES

• Woman, 103, credits this 1 fruit and special green juice for her longevity (Read More)

• Brain’s dual code for social memory unraveled (Read More)

• The unexpected ways your skin impacts your health and longevity (Read More)

INSIGHTS CORNER

LEADER SPOTLIGHT

This week we asked her five questions

What inspired you to focus on AI in neurology, and how do you envision its role in advancing patient care?

I have had several deeply personal connections to patients suffering with neurologic diseases. I tragically lost my uncle to Lou Gehrig’s Disease (ALS) and my father to Frontotemporal Dementia (FTD). After losing my Uncle, I decided to be proactive with my grief and I worked in the very neuromuscular clinic that he attended while he received care at Northwestern Medicine in Chicago, IL. I did everything I could in my early days to learn about neurology, including transporting donated brains and spinal cords underground from the hospital to the lab. No task was off-limits.

From this curiosity in understanding how the brain and central and peripheral nervous systems work, I eventually became interested in cognitive computing and neural networks. I am fascinated by the brain and how humans learn, and while we as a species have been at the pinnacle of intelligence for a while there’s certainly room for improvement. So I certainly subscribe to a philosophy of digital humanism and believe that we can augment human intelligence while also valuing human dignity. I believe we should be very thoughtful in using our past human experience with previous industrial revolutions and technological advancements to inform how we integrate new technology into society. I see AI as the Swiss army knife of this century.

In medicine AI has the potential for improving diagnostic accuracy, personalizing treatment, discovering new therapies, providing clinical decision support, improving care delivery operations and efficiency, and restoring the clinician-patient relationship by allowing clinicians to spend more quality time with their patients.

How do you see AI influencing future healthcare policies and regulations, especially in neurology and rare disease management?

As healthcare AI relies heavily on deeply personal health data for training and making predictions, there will be an increased focus on policies and regulations governing data privacy, confidentiality, security, and sharing. Traditional regulatory pathways are already being shaped in the US, particularly with FDA’s pre-cert program, but there is undoubtedly still a lot of work to do to develop guidelines for evaluating the safety, efficacy, and quality of AI-based medical devices, diagnostic tools, and clinical decision support algorithms.

Developers will have to disclose algorithmic principles, data sources, and performance metrics to enable accountability and foster trust among providers and patients. Rare disease will be an especially tough one for privacy and confidentiality as data may be more traceable back to the patient, however I believe this is a good use case for using synthetic or digital twin data. We have to ensure data is as representative as possible as to not perpetuate biases and lead to disparities in outcomes. Algorithmic bias is real and policymakers will need to devise strategies to mitigate it to promote fairness.

With the rapid advancement of AI, what steps are necessary to ensure the sustainability of AI technologies in healthcare, considering both environmental impact and technological longevity?

AI’s dirty little secret is that it is absolutely not a green technology (at least currently). We need to develop data-efficient AI algorithms that minimize the amount of data required for training and inference, reducing storage and bandwidth requirements. Data compression techniques can also be employed to optimize data transmission and storage. We also need to find ways to simplify models, perhaps using domain-specific small language models (SLMs) where it is possible to utilize less compute power.

Luckily a lot of work is underway where experts are trying to develop energy-efficient algorithms and hardware to minimize the environmental footprint. This includes optimizing algorithms for low-power consumption, edge computing, and designing energy-efficient hardware architecture. We need to design AI systems that focus on longevity and upgradability to minimize waste and ensure sustainable use of resources (e.g. modularity for component replacement, reducing the need for frequent replacements).

Reflecting on the role models who influenced your career, what qualities did they possess that you find most valuable in mentoring the next generation of women in tech?

Admittedly, I absolutely idolize Dr. Eric Topol in an unhealthy way. He has influenced my career in innumerable ways. He is endlessly curious. He’s not afraid to publicly try something new or make mistakes. He is always the first to know. He is not afraid to hypothesize or make predictions.

He’s a big thinker and academic; one of the most cited researchers in peer-reviewed literature. He pioneered the field of digital medicine but has also been instrumental in advocating for the acceleration of the adoption of digital technology in medicine to improve patient care.

He’s a prolific author, speaker, visionary, and tenacious mentor and trailblazer who unapologetically pairs his expertise with evangelism and activism. I would love to meet him one day and hope there may even be an opportunity to move from discussion to collaboration.

You don’t seem afraid to go against the status quo. Can you share one of your boldest visions for the future of healthcare?

Ah, yes. I have some controversial opinions. But I am not always alone in these opinions, I actually tend to side with experts. In this vain, I have a bold vision that the healthcare sector will not require full explainability and transparency of AI systems within healthcare in instances where algorithms or models are empirically validated to work.

While I fully understand that, by human nature, we want to fully understand step by step how a model came to a conclusion. But if a neural network it is proven to be more efficacious, although not entirely explainable, I will take that any day. When it comes down to it, human decisions are not really explainable either. I believe we should focus on testing for accuracy and precision with clinical trials for these “black box” algorithms and models rather than focusing purely on explainability.

We need studies that don’t pit AI against clinicians, but rather AI versus AI plus the clinician. Lately, as in the case with Google’s AIMI we’ve unfortunately seen that AI alone can perform better than AI + human so that was a bit disconcerting for me, but I do fundamentally believe in AI augmenting human intelligence and always keeping a “human in the loop” for the foreseeable future.

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More about Emily Lewis

TREND

According to Google Trends in US IVF is more searched now than at any time in Google Trends history.

Key highlights

• The top trending explained over the past week is “IVF ruling explained” with Alabama being the top state searching for it

• Over the past week, the top trending topic searched with Roe v Wade is in vitro fertilization

• “what is an embryo” increased +600% over the past week and “what is

• IVF” increased +850%; Alabama is the top state searching both

• In vitro fertilization is the top trending topic searched with “stance” over the past week

Other reproductive health trends highlights in US

• Interest in fertility has reached unprecedented levels, with the total fertility rate hitting a record high in the last six months.

• The metro areas of Beaumont-Port Arthur, TX, and Juneau, AK, lead in searches for fertility clinics over the past year.

• Menopause ranks among the top three trending topics in sexual and reproductive health searches in the past month.

• "IVF" (In Vitro Fertilization) and "vasectomy" are the most searched reproductive health-related procedures over the past month.

• Searches for "HPV vaccines" top the list of reproductive health-related vaccine searches in the past month.

AI TRIVIA

HEALTHCARE CONFERENCES

APRIL

• April 4-5: Community Oncology Conference (Orlando)

• April 8-11: Becker’s Hospital Review: 14th Annual Meeting (Chicago) Virtual April 16-18

• April 8-11: American Organization for Nursing Leadership Inspiring Leaders Conference (New Orleans)

• April 9-12: Business Group on Health 2024 Annual Conference (Tucson, Arizona)

• April 10-12: National Association of ACOs Spring Conference (Baltimore)

• April 12-15:  Society of Hospital Medicine (SHM) Converge Conference (San Diego)

• April 14-16: American Hospital Association Annual Meeting (Washington, D.C.)

• April 16-17: 24th Annual Employee Health Care Conference (New York City)

• April 23-26: National Association of Healthcare Access Management Annual Conference (Dallas)

• April 25-27: MGMA Focus | Financial Conference (San Diego)