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
EU seeks information from X on content moderation amid first major probe under new tech rules
Jonathan Raa | Nurphoto | Getty Images
The European Union is seeking information from social media platform X about cuts to its content moderation resources as part of its first major investigation into the company under its tough new laws governing online content.
The European Commission, the EU executive arm, said in a statement Wednesday that it’s requested information from X under the Digital Services Act, its groundbreaking tech law which requires online platforms to take a far stricter approach to policing illegal and harmful content on their platforms.
The Commission said it was concerned about X’s transparency report submitted to the regulator in March 2024, which showed it had cut its team of content moderators by nearly 20% compared to the number of moderators it reported in an early October 2023 transparency report.
X reduced linguistic coverage within the EU from 11 languages to seven, the Commission said, again citing X’s transparency report.
The Commission said it’s seeking further details from X on risk assessments and mitigation measures linked to the impact of generative artificial intelligence on electoral processes, dissemination of illegal material, and protection of fundamental rights.
X, which was formerly known as Twitter, was not immediately available for comment when contacted by CNBC.
X must provide information requested by the EU on its content moderation resources and generative AI requested by May 17, the Commission said. Remaining answers to questions from the Commission must be provided no later than May 27, the agency said.
The Commission said its request for information was a further step in a formal probe into breaches of the EU’s recently introduced Digital Services Act.
The Commission initiated formal infringement proceedings against X in December last year after concerns were raised over its approach to tackling illegal content surrounding the Israel-Hamas war.
The Commission at the time said its investigation would focus on X’s compliance with its duties to counter the dissemination of illegal content in the EU, the effectiveness of the social media platform’s steps to combat information manipulation and its measures to increase transparency.
EU officials said the requests for information aim to build on evidence gathered so far in relation to its DSA investigation into X. That evidence includes X’s March transparency report, as well as replies to previous requests for information addressing what X is doing to tackle disinformation risks linked to generative AI risks.
The DSA, which only came into effect in November 2022, requires large online platforms such as X to mitigate the risk of disinformation and institute rigorous procedures to remove hate speech, while balancing this with freedom-of-expression concerns.
Companies found to have breached the rules face fines as high as 6% of their global annual revenues.
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