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Dr. Scott Gottlieb is a CNBC contributor and is a member of the boards of Pfizer, genetic testing startup Tempus, health-care tech company Aetion Inc. and biotech company Illumina. He is also a partner at the venture capital firm New Enterprise Associates.

Researchers at Harvard presented a study demonstrating an achievement that would challenge any medical student. ChatGPT, a large language model, passed the U.S. Medical Licensing Exam, outperforming about 10 percent of medical students who fail the test annually.

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The inevitable question isn’t so much if but when these artificial intelligence devices can step into the shoes of doctors. For some tasks, this medical future is sooner than we think.

To grasp the potential of these tools to revolutionize the practice of medicine, it pays to start with a taxonomy of the different technologies and how they’re being used in medical care.

The AI tools being applied to health care can generally be divided into two main categories. The first is machine learning, which uses algorithms to enable computers to learn patterns from data and make predictions. These algorithms can be trained on a variety of data types, including images.

The second category encompasses natural language processing, which is designed to understand and generate human language. These tools enable a computer to transform human language and unstructured text into machine-readable, organized data. They learn from a multitude of human trial-and-error decisions and emulate a person’s responses.

A key difference between the two approaches resides in their functionality. While machine learning models can be trained to perform specific tasks, large language models can understand and generate text, making them especially useful for replicating interactions with providers.

In medicine, the use of these technologies is generally following one of four different paths. The first encompass large language models that are applied to administrative functions such as processing medical claims or creating and analyzing medical records. Amazon’s HealthScribe is a programmable interface that transcribes conversations between doctors and patients and can extract medical information, allowing providers to create structured records of encounters.

The second bucket involves the use of supervised machine learning to enhance the interpretation of clinical data. Specialties such as radiology, pathology and cardiology are already using AI for image analysis, to read MRIs, evaluate pathology slides or interpret electrocardiograms. In fact, up to 30% of radiology practices have already adopted AI tools. So have other specialties. Google Brain AI has developed software that analyzes images from the back of the eye to diagnose diabetic macular edema and diabetic retinopathy, two common causes of blindness.

Since these tools offer diagnoses and can directly affect patient care, the FDA often categorizes them as medical devices, subjecting them to regulation to verify their accuracy. However, the fact that these tools are trained on closed data sets, where the findings in data or imaging have been rigorously confirmed, gives the FDA increased confidence when assessing these devices’ integrity.

The third broad category comprises AI tools that rely on large language models that extract clinical information from patient-specific data, interpreting it to prompt providers with diagnoses or treatments to consider. Generally known as clinical decision support software, it evokes a picture of an brainy assistant designed to aid, not to supplant, a doctor’s judgment. IBM’s “Watson for Oncology” uses AI to help oncologists make more informed decisions about cancer treatments, while Google Health is developing DeepMind Health to create similar tools.

As long as the doctor remains involved and exercises independent judgment, the FDA doesn’t always regulate this kind of tool. The FDA focuses more on whether it’s meant to make a definitive clinical decision, as opposed to providing information to help doctors with their assessments.

The fourth and final grouping represents the holy grail for AI: large language models that operate fully automated, parsing the entirety of a patient’s medical record to diagnose conditions and prescribe treatments directly to the patient, without a physician in the loop.

Right now, there are only a few clinical language models, and even the largest ones possess a relatively small number of parameters. However, the strength of the models and the datasets available for their training might not be the most significant obstacles to these fully autonomous systems. The biggest hurdle may well be establishing a suitable regulatory path. Regulators are hesitant, fearing that the models are prone to errors and that the clinical data sets on which they’re trained contain wrong decisions, leading AI models to replicate these medical mistakes.

Overcoming the hurdles in bringing these fully autonomous systems to patient care holds significant promise, not only for improving outcomes but also for addressing financial challenges.

Health care is often cited as a field burdened by Baumol’s theory of cost disease, an economic theory, developed by economist William J. Baumol, that explains why costs in labor-intensive industries tend to rise more rapidly than in other sectors. In fields such as medicine, it’s less likely that technological inputs will provide major offsets to labor costs, as each patient encounter still requires the intervention of a provider. In sectors such as medicine, the labor itself is the product.

To compensate for these challenges, medicine has incorporated more non-physician providers to lower costs. However, this strategy reduces but doesn’t eliminate the central economic dilemma. When the technology becomes the doctor, however, it can be a cure for Baumol’s cost disease.

As the quality and scope of clinical data available for training these large language models continue to grow, so will their capabilities. Even if the current stage of development isn’t quite ready to completely remove doctors from the decision-making loop, these tools will increasingly enhance the productivity of providers and, in many cases, begin to substitute for them.

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How Elon Musk’s plan to slash government agencies and regulation may benefit his empire

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How Elon Musk’s plan to slash government agencies and regulation may benefit his empire

Elon Musk’s business empire is sprawling. It includes electric vehicle maker Tesla, social media company X, artificial intelligence startup xAI, computer interface company Neuralink, tunneling venture Boring Company and aerospace firm SpaceX. 

Some of his ventures already benefit tremendously from federal contracts. SpaceX has received more than $19 billion from contracts with the federal government, according to research from FedScout. Under a second Trump presidency, more lucrative contracts could come its way. SpaceX is on track to take in billions of dollars annually from prime contracts with the federal government for years to come, according to FedScout CEO Geoff Orazem.

Musk, who has frequently blamed the government for stifling innovation, could also push for less regulation of his businesses. Earlier this month, Musk and former Republican presidential candidate Vivek Ramaswamy were tapped by Trump to lead a government efficiency group called the Department of Government Efficiency, or DOGE.

In a recent commentary piece in the Wall Street Journal, Musk and Ramaswamy wrote that DOGE will “pursue three major kinds of reform: regulatory rescissions, administrative reductions and cost savings.” They went on to say that many existing federal regulations were never passed by Congress and should therefore be nullified, which President-elect Trump could accomplish through executive action. Musk and Ramaswamy also championed the large-scale auditing of agencies, calling out the Pentagon for failing its seventh consecutive audit. 

“The number one way Elon Musk and his companies would benefit from a Trump administration is through deregulation and defanging, you know, giving fewer resources to federal agencies tasked with oversight of him and his businesses,” says CNBC technology reporter Lora Kolodny.

To learn how else Elon Musk and his companies may benefit from having the ear of the president-elect watch the video.

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Why X’s new terms of service are driving some users to leave Elon Musk’s platform

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Why X's new terms of service are driving some users to leave Elon Musk's platform

Elon Musk attends the America First Policy Institute gala at Mar-A-Lago in Palm Beach, Florida, Nov. 14, 2024.

Carlos Barria | Reuters

X’s new terms of service, which took effect Nov. 15, are driving some users off Elon Musk’s microblogging platform. 

The new terms include expansive permissions requiring users to allow the company to use their data to train X’s artificial intelligence models while also making users liable for as much as $15,000 in damages if they use the platform too much. 

The terms are prompting some longtime users of the service, both celebrities and everyday people, to post that they are taking their content to other platforms. 

“With the recent and upcoming changes to the terms of service — and the return of volatile figures — I find myself at a crossroads, facing a direction I can no longer fully support,” actress Gabrielle Union posted on X the same day the new terms took effect, while announcing she would be leaving the platform.

“I’m going to start winding down my Twitter account,” a user with the handle @mplsFietser said in a post. “The changes to the terms of service are the final nail in the coffin for me.”

It’s unclear just how many users have left X due specifically to the company’s new terms of service, but since the start of November, many social media users have flocked to Bluesky, a microblogging startup whose origins stem from Twitter, the former name for X. Some users with new Bluesky accounts have posted that they moved to the service due to Musk and his support for President-elect Donald Trump.

Bluesky’s U.S. mobile app downloads have skyrocketed 651% since the start of November, according to estimates from Sensor Tower. In the same period, X and Meta’s Threads are up 20% and 42%, respectively. 

X and Threads have much larger monthly user bases. Although Musk said in May that X has 600 million monthly users, market intelligence firm Sensor Tower estimates X had 318 million monthly users as of October. That same month, Meta said Threads had nearly 275 million monthly users. Bluesky told CNBC on Thursday it had reached 21 million total users this week.

Here are some of the noteworthy changes in X’s new service terms and how they compare with those of rivals Bluesky and Threads.

Artificial intelligence training

X has come under heightened scrutiny because of its new terms, which say that any content on the service can be used royalty-free to train the company’s artificial intelligence large language models, including its Grok chatbot.

“You agree that this license includes the right for us to (i) provide, promote, and improve the Services, including, for example, for use with and training of our machine learning and artificial intelligence models, whether generative or another type,” X’s terms say.

Additionally, any “user interactions, inputs and results” shared with Grok can be used for what it calls “training and fine-tuning purposes,” according to the Grok section of the X app and website. This specific function, though, can be turned off manually. 

X’s terms do not specify whether users’ private messages can be used to train its AI models, and the company did not respond to a request for comment.

“You should only provide Content that you are comfortable sharing with others,” read a portion of X’s terms of service agreement.

Though X’s new terms may be expansive, Meta’s policies aren’t that different. 

The maker of Threads uses “information shared on Meta’s Products and services” to get its training data, according to the company’s Privacy Center. This includes “posts or photos and their captions.” There is also no direct way for users outside of the European Union to opt out of Meta’s AI training. Meta keeps training data “for as long as we need it on a case-by-case basis to ensure an AI model is operating appropriately, safely and efficiently,” according to its Privacy Center. 

Under Meta’s policy, private messages with friends or family aren’t used to train AI unless one of the users in a chat chooses to share it with the models, which can include Meta AI and AI Studio.

Bluesky, which has seen a user growth surge since Election Day, doesn’t do any generative AI training. 

“We do not use any of your content to train generative AI, and have no intention of doing so,” Bluesky said in a post on its platform Friday, confirming the same to CNBC as well.

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The Pentagon’s battle inside the U.S. for control of a new Cyber Force

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The Pentagon's battle inside the U.S. for control of a new Cyber Force

A recent Chinese cyber-espionage attack inside the nation’s major telecom networks that may have reached as high as the communications of President-elect Donald Trump and Vice President-elect J.D. Vance was designated this week by one U.S. senator as “far and away the most serious telecom hack in our history.”

The U.S. has yet to figure out the full scope of what China accomplished, and whether or not its spies are still inside U.S. communication networks.

“The barn door is still wide open, or mostly open,” Senator Mark Warner of Virginia and chairman of the Senate Intelligence Committee told the New York Times on Thursday.

The revelations highlight the rising cyberthreats tied to geopolitics and nation-state actor rivals of the U.S., but inside the federal government, there’s disagreement on how to fight back, with some advocates calling for the creation of an independent federal U.S. Cyber Force. In September, the Department of Defense formally appealed to Congress, urging lawmakers to reject that approach.

Among one of the most prominent voices advocating for the new branch is the Foundation for Defense of Democracies, a national security think tank, but the issue extends far beyond any single group. In June, defense committees in both the House and Senate approved measures calling for independent evaluations of the feasibility to create a separate cyber branch, as part of the annual defense policy deliberations.

Drawing on insights from more than 75 active-duty and retired military officers experienced in cyber operations, the FDD’s 40-page report highlights what it says are chronic structural issues within the U.S. Cyber Command (CYBERCOM), including fragmented recruitment and training practices across the Army, Navy, Air Force, and Marines.

“America’s cyber force generation system is clearly broken,” the FDD wrote, citing comments made in 2023 by then-leader of U.S. Cyber Command, Army General Paul Nakasone, who took over the role in 2018 and described current U.S. military cyber organization as unsustainable: “All options are on the table, except the status quo,” Nakasone had said.

Concern with Congress and a changing White House

The FDD analysis points to “deep concerns” that have existed within Congress for a decade — among members of both parties — about the military being able to staff up to successfully defend cyberspace. Talent shortages, inconsistent training, and misaligned missions, are undermining CYBERCOM’s capacity to respond effectively to complex cyber threats, it says. Creating a dedicated branch, proponents argue, would better position the U.S. in cyberspace. The Pentagon, however, warns that such a move could disrupt coordination, increase fragmentation, and ultimately weaken U.S. cyber readiness.

As the Pentagon doubles down on its resistance to establishment of a separate U.S. Cyber Force, the incoming Trump administration could play a significant role in shaping whether America leans toward a centralized cyber strategy or reinforces the current integrated framework that emphasizes cross-branch coordination.

Known for his assertive national security measures, Trump’s 2018 National Cyber Strategy emphasized embedding cyber capabilities across all elements of national power and focusing on cross-departmental coordination and public-private partnerships rather than creating a standalone cyber entity. At that time, the Trump’s administration emphasized centralizing civilian cybersecurity efforts under the Department of Homeland Security while tasking the Department of Defense with addressing more complex, defense-specific cyber threats. Trump’s pick for Secretary of Homeland Security, South Dakota Governor Kristi Noem, has talked up her, and her state’s, focus on cybersecurity.

Former Trump officials believe that a second Trump administration will take an aggressive stance on national security, fill gaps at the Energy Department, and reduce regulatory burdens on the private sector. They anticipate a stronger focus on offensive cyber operations, tailored threat vulnerability protection, and greater coordination between state and local governments. Changes will be coming at the top of the Cybersecurity and Infrastructure Security Agency, which was created during Trump’s first term and where current director Jen Easterly has announced she will leave once Trump is inaugurated.

Cyber Command 2.0 and the U.S. military

John Cohen, executive director of the Program for Countering Hybrid Threats at the Center for Internet Security, is among those who share the Pentagon’s concerns. “We can no longer afford to operate in stovepipes,” Cohen said, warning that a separate cyber branch could worsen existing silos and further isolate cyber operations from other critical military efforts.

Cohen emphasized that adversaries like China and Russia employ cyber tactics as part of broader, integrated strategies that include economic, physical, and psychological components. To counter such threats, he argued, the U.S. needs a cohesive approach across its military branches. “Confronting that requires our military to adapt to the changing battlespace in a consistent way,” he said.

In 2018, CYBERCOM certified its Cyber Mission Force teams as fully staffed, but concerns have been expressed by the FDD and others that personnel were shifted between teams to meet staffing goals — a move they say masked deeper structural problems. Nakasone has called for a CYBERCOM 2.0, saying in comments early this year “How do we think about training differently? How do we think about personnel differently?” and adding that a major issue has been the approach to military staffing within the command.

Austin Berglas, a former head of the FBI’s cyber program in New York who worked on consolidation efforts inside the Bureau, believes a separate cyber force could enhance U.S. capabilities by centralizing resources and priorities. “When I first took over the [FBI] cyber program … the assets were scattered,” said Berglas, who is now the global head of professional services at supply chain cyber defense company BlueVoyant. Centralization brought focus and efficiency to the FBI’s cyber efforts, he said, and it’s a model he believes would benefit the military’s cyber efforts as well. “Cyber is a different beast,” Berglas said, emphasizing the need for specialized training, advancement, and resource allocation that isn’t diluted by competing military priorities.

Berglas also pointed to the ongoing “cyber arms race” with adversaries like China, Russia, Iran, and North Korea. He warned that without a dedicated force, the U.S. risks falling behind as these nations expand their offensive cyber capabilities and exploit vulnerabilities across critical infrastructure.

Nakasone said in his comments earlier this year that a lot has changed since 2013 when U.S. Cyber Command began building out its Cyber Mission Force to combat issues like counterterrorism and financial cybercrime coming from Iran. “Completely different world in which we live in today,” he said, citing the threats from China and Russia.

Brandon Wales, a former executive director of the CISA, said there is the need to bolster U.S. cyber capabilities, but he cautions against major structural changes during a period of heightened global threats.

“A reorganization of this scale is obviously going to be disruptive and will take time,” said Wales, who is now vice president of cybersecurity strategy at SentinelOne.

He cited China’s preparations for a potential conflict over Taiwan as a reason the U.S. military needs to maintain readiness. Rather than creating a new branch, Wales supports initiatives like Cyber Command 2.0 and its aim to enhance coordination and capabilities within the existing structure. “Large reorganizations should always be the last resort because of how disruptive they are,” he said.

Wales says it’s important to ensure any structural changes do not undermine integration across military branches and recognize that coordination across existing branches is critical to addressing the complex, multidomain threats posed by U.S. adversaries. “You should not always assume that centralization solves all of your problems,” he said. “We need to enhance our capabilities, both defensively and offensively. This isn’t about one solution; it’s about ensuring we can quickly see, stop, disrupt, and prevent threats from hitting our critical infrastructure and systems,” he added.

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