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.
The Huawei flagship store and the Apple flagship store at Nanjing Road Pedestrian Street in Shanghai, China, Sept. 2, 2024.
Cfoto | Future Publishing | Getty Images
Huawei reclaimed the top spot in China’s smartphone market in the second quarter of the year, while Apple returned to growth in the country — one of its most critical markets — data released by technology market analyst firm Canalys showed on Monday.
Huawei shipped 12.2 million smartphones in China in the three months ended June, a rise of 15% year on year — equating to 18% market share. It’s the first time Huawei has been the biggest player by market share in China since the first quarter of 2024, according to Canalys.
Apple, meanwhile, shipped 10.1 million smartphones in the quarter in China, up 4% year on year and ranking fifth. It is the first time Apple has recorded growth in China since the fourth quarter of 2023, Canalys said.
Shipments represent the number of devices sent to retailers. They do no equate directly to sales but are a gauge of demand.
The numbers come ahead of Apple’s quarterly earnings release this week, with investors watching the company’s performance in China, a market where the Cupertino giant has faced significant challenges, including intense competition from Huawei and other local players such as Xiaomi.
Huawei, which made a comeback at the end of 2023 after its smartphone business was crippled by U.S. sanctions, has eaten away at Apple’s share.
Apple’s return to growth in China will be a welcome sign for investors. The U.S. tech giant “strategically adjusted its pricing” for the iPhone 16 series in China, which helped it grow, Canalys said. Chinese e-commerce firms discounted Apple’s iPhone 16 models during the quarter. And Apple itself also increased trade-in prices for some iPhone models.
Canalys’ numbers back up figures released by Counterpoint Research earlier this month showing Apple’s return to growth in China.
Shares of Apple have fallen around 14.5% this year, partly on concerns over China and geopolitical headwinds.
U.S. President Donald Trump has threatened Apple with tariffs and urged CEO Tim Cook to manufacture iPhones in America, a move experts have said would be near impossible.
Meanwhile, competition in China has intensified. Huawei has aggressively launched various smartphones in the past year and has started to roll out HarmonyOS 5, its self-developed operating system, across various devices. It is a rival to Google’s Android and Apple’s iOS.
“This move is expected to accelerate the expansion of its independent ecosystem’s user base, while also placing greater demands on system compatibility and user experience,” Lucas Zhong, analyst at Canalys, said in a press release.
Alibaba announced plans to release a pair of smart glasses powered by its AI models. The Quark AI Glasses are Alibaba’s first foray into the smart glasses product category.
Alibaba
Alibaba on Monday unveiled a pair of smart glasses powered by its artificial intelligence models, marking the Chinese firm’s first foray into the product category.
The e-commerce giant said the Quark AI Glasses will be launched in China by the end of 2025 with hardware powered by the firm’s Qwen large language model and its advanced AI assistant called Quark.
The Hangzhou, headquartered company is one of the leaders in China’s AI space, aggressively launching new models with capabilities that compete with Western counterparts like OpenAI.
Many tech companies see wearables, specifically glasses, as the next frontier in computing alongside the smartphone. Quark, which was updated this year, is currently available as an app in China. Alibaba is stepping into the hardware game as a way to distribute the app more widely.
The Quark AI Glasses are Alibaba’s answer to Meta’s smart glasses that were designed in collaboration with Ray-Ban. The Chinese tech giant will also now compete with Chinese consumer electronics player Xiaomi who this year released its own AI glasses.
Alibaba said its glasses will support hands-free calling, music streaming, real-time language translation, and meeting transcription. The glasses also feature a built-in camera.
Alibaba owns a range of different services in China from mapping to an online travel agent. Its affiliate company Ant Group also runs the widely-used Alipay mobile service. Alibaba said users will be able to use a navigation service via the glasses, pay with Alipay and compare prices on Taobao, its China e-commerce platform.
The firm has yet to release other details such as the price and technical specifications.
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