Dr. Isaac Kohane, who's both a computer scientist at Harvard and a physician, teamed up with two colleagues to test drive GPT-4, with one main goal: To see how the newest artificial intelligence model from OpenAI performed in a medical setting. "I'm stunned to say: better than many doctors I've observed," he says in the forthcoming book, "The AI Revolution in Medicine," co-authored by independent journalist Carey Goldberg, and Microsoft vice president of research Peter Lee. In the book, Kohane says GPT-4, which was released in March 2023 to paying subscribers, answers US medical exam licensing questions correctly more than 90% of the time. It's a much better test-taker than previous ChatGPT AI models, GPT-3 and -3.5, and a better one than some licensed doctors, too. GPT-4 is not just a good test-taker and fact finder, though.

Top editors give you the stories you want -- delivered right to your inbox each weekday. Dr. Isaac Kohane, who's both a computer scientist at Harvard and a physician, teamed up with two colleagues to test drive GPT-4, with one main goal: To see how the newest artificial intelligence model from OpenAI performed in a medical setting. "I'm stunned to say: better than many doctors I've observed," he says in the forthcoming book, "The AI Revolution in Medicine," co-authored by independent journalist Carey Goldberg, and Microsoft vice president of research Peter Lee. In the book, Kohane says GPT-4, which was released in March 2023 to paying subscribers, answers US medical exam licensing questions correctly more than 90% of the time. It's a much better test-taker than previous ChatGPT AI models, GPT-3 and -3.5, and a better one than some licensed doctors, too.

In 1904, the horse du jour was Clever Hans, widely reputed to be so much smarter than his brethren that he could do math, tell time, and even read and spell. Word spread fast by word of mouth, and eventually the occasionally gullible The New York Times reported that Hans was so smart that he "can do almost everything but talk". Ask Hans what 12 plus 13 is, and he would stamp his feet 25 times. People were amazed, and paid good money to see him. Turns out the horse knew no math; it had solved the arithmetic problems--all of them --in a different way.

Within precision medicine, and specifically rare diseases, clinicians and researchers rely on genetic and diagnostic testing to help drive accurate diagnosis and treatment. However, genomic data alone are often insufficient to unlock the life-changing diagnoses of rare diseases. Well-curated and accurate phenotype data, which may include quantified observable traits such as short stature, low set ears, and blood biochemistry, along with genetic and diagnostic test results, are vital for shortening the diagnostic journey of these patients and identifying the most effective treatments available. The need for accurate patient phenotyping is not a new concept. In fact, over 20 years ago, Isaac Kohane, Chair of the Department of Biomedical Informatics and the Marion V. Nelson Professor of Biomedical Informatics at Harvard Medical School, predicted that the accurate practice of patient phenotyping would become essential as the volume of genomic information continued to surge.

Improving the speed and accuracy of clinical diagnosis, augmenting clinical decision-making, reducing human error in clinical care, individualizing therapies based on a patient's genomic and metabolomic profiles, differentiating benign from cancerous lesions with impeccable accuracy, identifying likely conditions a person may develop years down the road, spotting early tell-tale signs of an ultrarare disease, intercepting dangerous drug interactions before a patient is given a new medication, yielding real-time insights amidst a raging pandemic to inform optimal treatment of patients infected with a novel human pathogen. These are some of the promises that physicians and researchers look to fulfill using artificial intelligence -- promises poised to transform clinical care, lead to better patient outcomes, and, ultimately, improve human lives. Yet, AI is no silver bullet. It can fall prey to the cognitive fallibilities and blind spots of the humans who design it. AI models can be as imperfect as the data and clinical practices that the machine-learning algorithms are trained on, propagating the very same biases AI was designed to eliminate in the first place. Beyond conceptual and design pitfalls, realizing the potential of AI also requires overcoming systemic hurdles that stand in the way of integrating AI-based technologies into clinical practice.

Third in a series that taps the expertise of the Harvard community to examine the promise and potential pitfalls of the coming age of artificial intelligence and machine learning. The news is bad: "I'm sorry, but you have cancer." Those unwelcome words sink in for a few minutes, and then your doctor begins describing recent advances in artificial intelligence, advances that let her compare your case to the cases of every other patient who's ever had the same kind of cancer. She says she's found the most effective treatment, one best suited for the specific genetic subtype of the disease in someone with your genetic background -- truly personalized medicine. And the prognosis is good.

Early this spring as the pandemic began accelerating, AJ Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford-trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies. He was awed when the program reported back that the new virus appeared to have a snippet of DNA code - "RRARSAS" - distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance. Venkatakrishnan, director of scientific research and partnerships at AI start-up Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates?

Early this spring as the pandemic began accelerating, AJ Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford-trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies. He was awed when the program reported back that the new virus appeared to have a snippet of DNA code -- "RRARSAS" -- distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance. Venkatakrishnan, director of scientific research and partnerships at AI start-up Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates?

Early this spring as the pandemic began accelerating, AJ Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford-trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies. He was awed when the program reported back that the new virus appeared to have a snippet of DNA code – "RRARSAS" – distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance. Venkatakrishnan, director of scientific research and partnerships at AI start-up Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates?

The healthcare industry hoped that AI would play a crucial tool in curbing the spread of the COVID-19 virus across the world. The results up till now are a letdown. Dr Isaac Kohane (Department of Biomedical Informatics at Harvard Medical School) states that in a few cases, they were anti-constructive. He even states that they were shooting for the moon in healthcare, but they weren't even out of their own backyard. He felt that weren't getting anywhere due to the lack of high-grade data. Yet faith isn't lost on the AI contribution to address the pandemic.