🌞 Shapeshifting DNA controls memory

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Welcome to your briefing:

• AI ROUNDUP: 10 tips to avoid planting AI time bombs in your organization

• INDUSTRY ROUNDUP: Could the U.S. force treatment on mentally ill people (again)?

• WELLNESS BYTES: How Fitbit can help you measure stress and use it to your advantage

• INSIGHTS CORNER: Leader Spotlight: C.K. Wang is the Chief Medical Officer at COTA

• TRIVIA: Which AI-powered technology has been used to develop "synthetic patients" to test new medical treatments and accelerate clinical trials?

AI ROUNDUP

WATCH THIS

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• Apple’s work in Healthcare is just getting started (Read More)

• AI beats doctors in accurately assessing eye problems (Read More)

• 10 tips to avoid planting AI time bombs in your organization (Read More)

• GPT-4 can detect radiology report errors at the same rate as members of the specialty (Read More)

• How conversational AI can empower healthcare providers (Read More)

• Patented AI platform identifies promising early-stage BioPharma assets (Read More)

• FDA delivers de novo clearance to Scopio's bone marrow pathology AI for blood disorders, cancer (Read More)

INDUSTRY ROUNDUP

• Could the U.S. force treatment on mentally ill people (again)? (Read More)

• UnitedHealth skips hearing focused on change ransomware attack (Read More)

• Health records giant Epic cracks down on startup for unauthorized sharing of patient data (Read More)

• Eli Lilly’s weight loss drug Zepbound shows promise as a sleep apnea treatment in late-stage trials (Read More)

• FDA announces recall of heart pumps linked to deaths and injuries (Read More)

WELLNESS BYTES

• How Fitbit can help you measure stress and use it to your advantage (Read More)

• These states and cities have the best and worst brain health in America (Read More)

• Neuroscientists discover shapeshifting DNA controls memory formation (Read More)

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INSIGHTS CORNER

LEADER SPOTLIGHT

I’m a medical oncologist, and I was part of a developmental initiative at Watson Health. The promise of artificial intelligence (AI) had already grabbed hold of the industry, and several companies were taking the first steps towards building machine learning models and large language models (LLMs) that would be the foundation of developing AI technology for mainstream use.

The excitement was centered around scale: ingesting, processing and producing insights for, it seemed, all the world’s information that could be poured into the models. As Google had proposed for digitizing the world’s libraries for public access, AI was sounding like it could be a colossus of global data striding towards medical researchers like me.

But what would be the purpose of all this data, and where could it have the most impact? It’s a truism that without a defined function, data generally results in confusion rather than clarity. I was working with a group of computer and AI engineers, asking, “What are the most pressing challenges that oncologists face in everyday real world practice?”

At that time my answer was to make sense of the rapidly increasing volume and complexity of medical literature and the lack of time oncologists had to sift through them. Wouldn’t it be great for oncologists to have every relevant piece of information right at their fingertips when they need it, and for a program to help guide the oncologist through the information?

Bringing the most relevant and current insights to the fingertips of clinicians would help them make the best clinical decision possible. Published textbooks and clinical trial data would be coupled with established practice guidelines, real-world data from similar patients, and lastly, available and relevant clinical trials. It would come together in what I called a “clinical insights navigator”. This navigator would reside within the EHR so that it can be easily accessed and utilized during patient encounters.

That said, there may be no more nuanced process in all of medicine than a physician considering the factors and data points available to them and recommending a therapeutic action for their patient. Some factors, like patient choice, lifestyle, and goals, aren’t clearly reflected in most data, but they’re known by the clinicians having a conversation in the exam room. And, they’re the most important; they can make or break a treatment.

That’s the art of medicine. Could AI, which is an extension of human learning, provide insights that embody these kinds of subjectivities and sensitivities that inform a doctor’s decision? Could the technology that was clearly going to dominate medical advancements for the next generation merge the theoretical and the practical without introducing unwarranted biases and hallucinations? These were complicated questions that no one at the time had answers to.

My idea didn’t go far then, but some ideas can be stubborn things. In the decade since, medical data and literature continue to multiply and the rate of oncology drug discovery and approvals continue to accelerate exponentially. I’m now leading oncology data research at another organization, and, it seems, we’re beginning to understand how to transform this idea into a tool at the doctor’s fingertips. Given the ubiquity of the AI tools and their speed of development, not only can this exist, I’m beginning to believe it will.

The growth in analytic methodologies and computing power over the past decade is making it easier for us to access and analyze this data. More importantly, AI is no longer a fringe or intangible concept but rather, a part of everyday vernacular. The willingness to utilize AI in different fields, including medicine, has become much more widespread and adoption has increased dramatically in the past few years.

AI is increasingly tested and utilized in the specialties of pathology and radiology, where pattern recognition is foundational, and AI models are being developed to provide summaries and reports from medical text, literature, and EHR documentation.

What has proven to be more elusive is the ability of AI to recommend a treatment plan that is tailored to a specific patient’s needs and circumstances. Clinical decision-making remains a complicated and nuanced art, and the ever-increasing armamentarium of therapeutics and their corresponding literature have made the process ever more complicated.

By the same token, clinical-trial eligibility criteria have become increasingly lengthy and complex. Despite all of the improvements in AI over the past decade, what remains unchanged are the questions that I asked nearly a decade ago.

In order to fully maximize AI’s potential at the point of care, the technology must be able to account for the subjective components of the clinical decision-making process. Is it possible for AI – or any other technology that may emerge – to do that? With any luck, we won’t be having that same conversation in ten years’ time.

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More on C.K. Wang

AI TRIVIA

HEALTHCARE CONFERENCES

APRIL

• May 5-7: American Telemedicine Association ATA Nexus 2024 (Phoenix)

• May 5-8: Milken Institute Global Conference (Beverly Hills, CA)

• May 6-8: Fierce Medical Affairs Strategic Summit East (Jersey City, NJ)

• May 13-15: Fierce Digital Pharma Engage (Newport Beach, CA)

• May 14-16: IHI Patient Safety Congress (Orlando)

• May 14-15: Fierce Pharma PR & Communications Summit West (Newport Beach, CA)

• May 21-23: Fierce Digital Pharma Innovation Week (virtual)

• May 21-23: WSJ The Future of Everything (New York City)

• May 29-31: thINc360 – The Healthcare Innovation Congress (Washington, D.C.)

• May 31-June 4: American Society of Clinical Oncology Annual Meeting (Chicago)