Op-ed: Gottlieb says A.I. may take on doctors’ roles sooner rather than later
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.
Technology
Apple and Samsung smartphone growth to take hit from tariff uncertainty: Counterpoint Research
Electric vehicles outside a Tesla dealership in Melbourne on April 19, 2023.
William West | AFP | Getty Images
Tesla may be facing declining sales in the U.S. and Europe, but it reported a bright spot in Australia — where its electric vehicle sales rebounded to their highest level in nearly 12 months in May.
The American EV maker said Tuesday that its vehicle sales jumped to 3,897, primarily driven by record sales of its recently revamped Model Y compact sport utility vehicle.
Australian sales of the Model Y soared 122.5% year over year, while sales of the company’s Model 3 dropped significantly.
Total deliveries in Australia were up just 9.3% year over year but surged over 675% from April when the company sold only 500 EVs, according to data from the Australian Electric Vehicle Council.
The EV Council is the exclusive source of Tesla and Polestar sales data in Australia after the brands exited the Federal Chamber of Automotive Industries (FCAI) last year.
Tesla’s April sales numbers for Australia had been the company’s worst performance of the year there. Despite the May rebound, the EV makers’ total sales in Australia remains down 48.2% year-to-date compared with the same period last year.
“Tesla’s strong sales growth in Australia this May is an encouraging sign, driven almost entirely by strong demand for the updated Model Y. But globally, Tesla is still facing headwinds,” Liz Lee, associate director at technology market research firm Counterpoint Research, told CNBC.
According to Counterpoint EV Sales Tracker, she added, Tesla’s sales were down 13% year on year in the first quarter. “Thus, while the latest Australian rebound is meaningful locally, it does not yet signal a broader global recovery.”
Musk and brand damage
Tesla’s global sales have suffered in recent months in light of increased competition and reputational damage related to CEO Elon Musk’s political rhetoric and activities.
For example, prior to May, Tesla’s Australia sales struggled amid reports of vandalism and protests related to Musk’s work with U.S. President Donald Trump’s administration and support for far-right parties in Europe.
Tesla reported on Tuesday that its sales in the U.S. were down 11% in May from last year. And European industry groups on Monday noted significantly lower sales for new Tesla vehicles in Spain, Portugal, Denmark and Sweden last month.
But there have been some bright spots. Tesla posted a surprise bounce back in Norway, where the Model Y helped it post 213% more vehicles in May from a year ago. Tesla also said it hit a record breaking 1,545 sales in Turkey last month.
That data comes after Trump hosted a press conference last week, where he announced that Elon Musk would be officially departing from his role within the federal government and White House.
Though Trump added that Musk will stay on as an advisor, in a research note following the announcement, Wedbush’s Dan Ives said he believed that Musk’s days in politics are essentially over after the brand damage suffered by Tesla.
The Tesla bull said Musk’s pivot back to the EV maker “was the best possible news Tesla investors could have heard,” with the rollout of its robotaxi launch expected later this month. Musk has said that Tesla has already been testing driverless Model Ys.
Tight competition
Musk’s return comes at a time when Tesla is also facing much tighter competition, especially from Chinese EV makers.
BYD, for example, has been expanding globally in the face of tight competition in its home market of China, and is increasingly going head to head with Tesla.
In April, China’s BYD outsold Tesla in Europe for the first time, according to JATO Dynamics. The automotive giant recently announced a slew of discounts, and other Chinese automakers are following suit. In March, it was revealed that Tesla fell behind BYD in total annual sales revenue.
And according to a report from JATO Dynamics, BYD sold more pure battery EVs in Europe than Tesla for the first time ever last month in what it called a “watershed moment.”
In May, however, Tesla was able to regain a lead against BYD in vehicle sales in Australia, with 3,897 sales compared with BYD’s 3,225, based on available data.
Its worth noting that Tesla exclusively sells battery electric vehicles, while BYD also sells hybrid cars. Battery EVs run entirely on electricity, while hybrid vehicles combine an electric battery with an internal combustion engine.
According to data that Australia’s FCAI sent to CNBC, sales of hybrid vehicles and plug-in hybrid electric vehicles — a type of hybrid that can be charged by being plugged into an external power source — rose by about 6% and 118%, respectively, year on year in May.
“Recent sales data indicate that consumers are increasingly turning to hybrid and plug-in hybrids as many Australians want to reduce their vehicle emissions,” said Tony Weber, chief executive of the FCAI.
He added that hybrids come without the range limitations associated with battery EVs, which is a particular concern in Australia.
Amid increasing global competition and threats from hybrid vehicles, Counterpoint’s Lee said, Tesla should continue to look to high-potential regions like India, Southeast Asia and parts of Latin America.
“These markets are ramping up EV infrastructure and incentives, and Tesla could benefit by moving early, especially if it localizes production and tailors offerings to local preferences,” she said.
Tesla announced on Tuesday that it is leasing a warehouse in Mumbai that is expected to be used for vehicle servicing as part of the company’s long-anticipated India expansion.
Tesla was up about 0.5% in trading on Tuesday and is down about 15% year-to-date.
George Kurtz, chief executive officer of Crowdstrike Inc., speaks during the Montgomery Summit in Santa Monica, California, U.S., on Wednesday, March 4, 2020.
Patrick T. Fallon | Bloomberg | Getty Images
CrowdStrike shares fell 7% in extended trading on Tuesday after the security software maker issued a weaker-than-expected revenue forecast.
Here’s how the company did against LSEG consensus:
- Earnings per share: 73 cents, adjusted vs. 65 cents expected
- Revenue: $1.10 billion vs. $1.10 billion expected
Revenue increased by nearly 20% in the fiscal first quarter, which ended on April 30, according to a statement. The company registered a net loss of $110.2 million, or 44 cents per share, compared with net income of $42.8 million, or 17 cents per share, in the same quarter last year.
Costs rose in sales and marketing as well as in research and development and administration, partly because of a broad software outage last summer.
For the current quarter, CrowdStrike called for 82 cents to 84 cents in adjusted earnings per share on $1.14 billion to $1.15 million in revenue. Analysts polled by LSEG were expecting 81 cents per share and $1.16 billion in revenue.
CrowdStrike bumped up its guidance for full-year earnings but maintained its expectation for revenue. The company now sees $3.44 to $3.56 in adjusted earnings per share, with $4.74 billion to $4.81 billion in revenue. The LSEG consensus was $3.43 per share and $4.77 billion in revenue. The earnings guidance provided in March was $3.33 to $3.45 in adjusted earnings per share.
Also on Tuesday, CrowdStrike said it had earmarked $1 billion for share buybacks.
“Today’s announced share repurchase reflects our confidence in CrowdStrike’s future and unwavering mission of stopping breaches,” CEO George Kurtz said in the statement.
As of Tuesday’s close, the stock was up 43% so far in 2025, while the S&P 500 index had gained less than 2%.
Executives will discuss the results on a conference call with analysts starting at 5 p.m. ET.
WATCH: Trade Tracker: Malcolm Ethridge buys more CrowdStrike, Palo Alto Networks, Spotify and Oracle
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