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There are few things are more complex than managing health conditions. The healthcare system is very good at tracking the prescribing of medicines. It doesn’t track deprescribing of medicines.

Seasons change, fashions change, US presidents change, but for many patients, prescriptions never do—except to become more numerous.

Among US adults aged 40 to 79 years, about 22% reported using 5 or more prescription drugs in the previous 30 days. Within that group, people aged 60 to 79 years were more than twice as likely to have used at least 5 prescription drugs in the previous month as those aged 40 to 59 years.

Over time, a drug’s benefit may decline while its harms increase, Johns Hopkins geriatrician Cynthia Boyd, MD, MPH, told JAMA. “There are a pretty limited number of drugs for which the benefit-harm balance never changes.”

Deprescribing requires shared decision-making that considers “what patients value and what patients prioritize.”

Deprescribing lacks proven clinical guidelines and time for a patient and physician discussion. The average patient visits are twelve minutes for new patients and seven for return patients*.

* Topol, Eric, Deep Medicine, Basic Books, New York, 2019, p17

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Self-driving Cars – Autonomous AI Challenge

A popular physicist joke is nuclear fusion is thirty years away and always will be.

While robo-taxis are picking up passengers today in San Francisco, Austin and Phoenix, robo-taxis seem perpetually a few years away.

We’ve had military aircraft drones deployed since 1995, a modified autonomous Volkswagen vehicle won the 132-mile DARPA Grand Challenge in 2005, and six automakers announced in 2015 delivery plans for their self-driving vehicles between 2017 and 2020. One of those companies announced yesterday:

General Motors (GM) will slash spending in its self-driving car unit Cruise, after an accident last month seriously injured a pedestrian and prompted regulators to retract its operating permit for driverless cars in San Francisco.

In October, the company said it would no longer operate its vehicles without safety drivers behind the wheel.

The horrific accident in San Francisco highlighted a significant challenge for autonomous vehicles, “long tail” or edge cases. These instances are at end of the distribution curve of occurrence and are often unique. Robo-taxis can operate perfectly for ten thousand miles then break down with an edge case. AI requires many training examples to learn. Humans are more flexible and leverage common sense to navigate these cases. Another challenge for autonomous vehicles is social acceptance. While humans learned to live with over forty thousand deaths from car accidents per year, it is too early to know what will be accepted from autonomous vehicles.

For more, see GM Slashes Spending on Robotaxi Unit Cruise, a Setback for Driverless Cars

 

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Amazing Brain in Color

This a photo of Purkinje neuron cells that connect the brain and spinal cord to help control breathing, heart rate, balance and more. Silas Busch from the University of Chicago captured this slightly eerie scene, noting it reminded him of people shuffling through the dark of night. The photo won first place this year in the National Institute of Health Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative’s annual Show Us Your BRAINs! Photo and Video Contest.

More from NIH

Photo Credit: Silas Busch, The University of Chicago

 

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Is clean, sustainable energy a few miles away?

Hot rock is everywhere, with temperatures rising hundreds of degrees Fahrenheit within the first few miles of the surface, … yet geothermal plants are built where naturally heated water can be easily tapped. 

Gregory Barber writes in Wired about a new “enhanced” geothermal system (EGS) built on wells drilled 7,000 feet down into completely dry, 375 degrees Fahrenheit, rock to create an artificial hot spring by pumping water into the well. The returned hot water drive turbines to create 2 to 3 megawatts of electricity. Enough to power a few thousand homes.

Pre-eighteenth-century mills were powered by wind and water wheels until the steam engine made in possible for factories to locate anywhere. EGSs show promise in creating clean, sustainable energy that may help address climate change.

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More Health Complexity – Molecules and Microbes

The complexity of human health doesn’t change each week. The complexity of our understanding does when another traunch of peer-reviewed medical journal articles arrive. Two million articles are published each year*.

Each week, our world becomes more complex. Some complexity is human made, like our Byzantine-like healthcare reimbursement system, some complexity is discovering our existing realities, such as new information about molecules (DNA, immune proteins) and microbes.

Stanford Medicine-led study clarifies how ‘junk DNA’ influences gene expression – When the first whole genome sequencing was announced in 2000, they found 20,000 genes representing just 1-2% of the 3 billion base pairs. They called the remaining 98-99% of the genome non-coding DNA (a.k.a., junk DNA). This study shows how junk DNA regulates gene expression (“the chef), essentially choosing which gene recipe to make.

Your “immune resilience” greatly impacts your health and lifespan

  • Immune resilience is the capacity to control inflammation and rapidly restore immune balance following a disease challenge. 
  • People with high levels of immune resilience live longer, resist diseases, and are more likely to survive diseases when they do develop. 

Over time, our immune resilience decreases as our immune systems are subjected to multiple respond-and-recover cycles.

How Many Microbes Does It Take to Make You Sick? – The concept of “infectious dose” suggests there are ways to stay safer from harm.

You may need to add “junk DNA”, “immune resilience,” and “infectious dose” to your “staying healthy” strategy. A great opportunity for an AI digital twin to help us make sense of our molecules and microbes in managing the complexity of health.

* Topol, Eric; Deep Medicine, Basic Books, New York, 2019, p138

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AI – Hallucinations or BS?

We all know someone who lacks the three-word phrase “I don’t know” in their vocabulary. I’ll call him Bob. Bob confidently answers questions with fabrications that can make you question what day it is. After second guessing myself and double checking the facts, I’m able to conclude it’s just Bob BS.

Do Generative AI models hallucinate or BS like Bob?

A new study argues that the perception of AI intelligence is marred by linguistic confusion. While AI, such as ChatGPT, generates impressive text, it lacks true understanding and consciousness.

University of Cincinnati professor Anthony Chemero contends that AI cannot be intelligent in the way that humans are, even though “it can lie and BS like its maker.”

While Generative AI technology like ChatGPT is breathtakingly amazing, we need to be confident we are not talking to Bob. Although Bob is very smart, I would want his advice on life threatening medical decisions.

More from the builders of AI.

“I don’t think that there’s any model today that doesn’t suffer from some hallucination,” said Daniela Amodei, co-founder and president of Anthropic, maker of the chatbot Claude 2.

“They’re really just sort of designed to predict the next word,” Amodei said. “And so there will be some rate at which the model does that inaccurately.”

 

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How our brains make memories

Our brains are constantly changing, making it hard for scientists to determine the exact changes made to formulate a memory or something learned.

A new study aimed to understand how information may be stored in the brain.

“Memory engram cells are groups of brain cells that, activated by specific experiences, change themselves to incorporate and thereby hold information in our brain. Reactivation of these ‘building blocks’ of memories triggers the recall of the specific experiences associated to them. The question is, how do engrams store meaningful information about the world?”

“In 21st century neuroscience, many of us like to think memories are being stored in engram cells, or their sub-components. This study argues that rather than looking for information within or at cells, we should search for information between cells, and that learning may work by altering the wiring diagram of the brain – less like a computer and more like a developing sculpture.

“In other words, the engram is not in the cell; the cell is in the engram.”

It would be interesting to know how many of our 86 billion neurons and 100 trillion connections are required to store various memories.

 

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AI was already in your hands

When ChatGPT launched November 30, 2022, AI was already in your hands. Within 60 days of launch, ChatGPT had over 100 million first time users. We were able to summarize complex explanations of Artificial Intelligence or virtually any topic in poetry in the style of Emily Dickenson. Software engineers were able to draft complex code with ChatGPT prompts.

While Foundational Models like ChatGPT enabled hundreds of millions to experience AI, most of us already experience the power of AI almost every time we used our smartphones.Discussions about AI often focus on the futuristic threat posed by superhuman intelligence. But AI is already woven into the fabric of our daily lives. The way we travel, the food we eat, how we spend our money, the news we read and our social interactions – the influence of AI is everywhere …

A day in the life of AI takes us through our day from using our face to unlock our phone when we wake to scrolling before we close our eyes at night. Written by the Guardian team of ,   and illustrated by Alex Mellon.

 

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AI Agents – Are They The Next Big Thing?

The recent rollouts of Large Language Models (LLMs) (a.k.a Foundational Models) have provided hundreds of millions of people hands-on experience with what is possible with AI. We are getting to know the amazing things ChatGPT (OpenAI), Claude (Anthropic), LLaMA (Meta), PaLM (Google) and LaMDA (Google) can do, along with where they need work.

The University of Pennsylvania Professor Ethan Mollick has been a leader in the “use of LLMs” discussions. He explores what could be next with AI Agents.

Many people think the future of AI lies in “agents” – a fuzzily-defined term that refers to an autonomous AI program that is given a goal, and then works towards accomplishing it on its own. There has been a lot of buzz about agents over the past few months, but not much technology that actually works well.

As always, Professor Mollick provides examples of how this would work.

While it is easy to imagine AI agents, it may be hard to know when we will trust carrying our goals with our money.

 

 

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When Will We Trust AI for Clinical Decisions?

There are few things more complex than clinical decision making. We ask our physicians for diagnosis, prognosis and treatments based on limited sets of known factors. They certainly can’t imagine the action, reaction, and interaction of 42 million proteins within each cell of our 30 trillion human cells, nor understand the 60 – 85% of determinants of an individual health outcomes (which doesn’t include healthcare and genetics) or keep up with the two million peer-reviewed medical journal articles published each year. Hopefully, one day AI will help them with that.

When will physicians and patients be able to trust AI to help?

Christina Jewett provides some insight in her New York Times article.

The F.D.A. has approved many new programs that use artificial intelligence, but doctors are skeptical that the tools really improve care or are backed by solid research.

Google has already drawn attention from Congress with its pilot of a new chatbot … designed to answer medical questions, raising concerns about patient privacy and informed consent.

She writes how physicians are being careful with AI using it as a scribe, for occasional second opinions and to draft various reports. Physicians don’t trust the 350 FDA approved AI powered solutions, thus increasing healthcare cost with duplicate efforts (AI and physician) and false positives. AI has shown some benefits such as expediting stroke treatments by moving brain scans to the top of radiologist inbox if the AI detected a stroke.

Generative AI has produced great benefits for software coders, generating first draft of the desired software code using standalone point solutions like Chat GPT. The promise is one day Generative AI will be able to help doctors make sense of numerous factors contributing to a health condition. We will also need physicians to make sense of the credibility of the AI.

 

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