Detection of a Case of Pulmonary Embollism Using AI and a Medical Imaging System: How to Scale it for a Health System
But even seven years after Hinton’s prediction, radiologists are still very much in demand. The clinicians seem underwhelmed by the performance of these technologies.
But if the AI makes a mistake, it can have the opposite effect. Perchik says he recently spotted a case of pulmonary embolism that the AI had failed to flag. He decided to take extra review steps, which confirmed his assessment but slowed down his work. If I had made a decision to trust the Artificial Intelligence and move forward, then that could have gone undetected.
He says if you scale that to a high level of health system, you can see a lot of choices to make about the devices and how to integrate them. “It can quickly become a kind of IT soup.”
Artificial intelligence is going to transform health care: A pedagogical review of an AI system that uses breast X-ray images to help doctors make diagnostic decisions
It is now known that you need an external validation in the field. But, she adds, “there’s only a handful of institutions in the world that are very aware of this”. It is not possible to know if the tools are actually helping if the model is not tested.
The following month, Google researchers described in a preprint6 They integrated that approach and the Med-Palm model, which can answer some open-ended medical queries almost as well as a physician. The result is Med-PaLM Multimodal, a single AI system that demonstrated that it could not only interpret chest X-ray images, for example, but also draft a medical report in natural language6.
These are examples of what some scientists call a foundation model. The term, coined in 2021 by scientists at Stanford University, describes models trained on broad data sets — which can include images, text and other data — using a method called self-supervised learning. Pre-trained models are called base models and can later be adapted to perform different tasks.
The foundation models are ideal because almost all the eye can be imaged at high resolution. Data sets of these images are available to train models. He says that artificial intelligence is going to transform health care. Ophthalmology is an example for other medical specialities.
Big tech companies are already investing in medical-imaging foundation models that use multiple image types — including skin photographs, retinal scans, X-rays and pathology slides — and incorporate electronic health records and genomics data.
Some scientists think that the models may be able to identify patterns that humans can’t. An Artificial Intelligence study by the researchers of the company was described that could determine a person’s age and gender from images of their eyes. That is something that even experienced ophthalmologists can’t do, Keane says. There is hope that there are a lot of scientific information in these images.
With artificial intelligence seemingly working its way into every technology out there, one area where it’s considered particularly promising is in helping doctors make medical diagnoses.
Mansour, the transplant fungal infection specialist at Massachusetts General Hospital, says he hopes AI allows him more time to spend with patients. He says that you could allow him to go and talk to the person about what to expect when they get their management degree. “It restores that patient-doctor relationship.”
Succi says that artificial intelligence won’t replace doctors, but it will replace those who don’t. “It’s the equivalent to writing an article on a typewriter or writing it on a computer. It’s that level of leap.”
“It needs improvement,” says Dr. Marc Succi of Mass General Brigham, who was one of the paper’s authors. “We’ve drilled down on specific parts of the clinical visit where it needs to improve before it’s ready for prime time.”
“It’s a time-consuming and very haphazard process,” says Dr. June-Ho Kim, who directs a program on primary care innovation at Ariadne Labs, which is a partnership of Brigham and Women’s Hospital and the Harvard T.H. Chan School of Public Health. A large language model that is able to digest that is able to produce a kind of natural language summaries of it being useful.
It cited a journal article in his area of expertise, but he wasn’t familiar with it. I was looking to see if I could find the study in that journal. It didn’t exist,” Bonis says. I queried the large language model about “Did you make this up?” It said yes.”
Gen AI Helps Doctors Make Better Diagnoses Up todate: A Case Study with a Hawaiian Infection Case (Wolters Kluwer Health)
At this point, Wolters Kluwer Health is just sharing the AI-enhanced program in a beta form for testing. Bonis says the company needs to make sure it’s entirely reliable before it can be released.
“If you have a question, it can maintain the context of your question,” says Dr. Peter Bonis, chief medical officer for Wolters Kluwer Health. “And saying, ‘Oh, I meant this’ or ‘What about that?’ And it knows what you’re talking about and can guide you through, in much the same way that you might ask a master clinician to do that.”
“I get things like the typhus, but I’m not sure about the mosquitos,” he says, scrolling down a long list of responses. “I think the list would be better if it were more specific, I think Gen AI gives you the opportunity to really refine that,” said the man.
“Here’s an example,” Mansour says, turning to his computer. “If I meet a patient who is coming from Hawaii.” The hypothetical patient’s symptoms make Mansour worry about an infection that the patient acquired back home, so he types “Hawaii” and “infection” into UpToDate.
It’s possible to search for articles written by doctors in a huge database of articles, which are all pulling from the latest research.
Source: AI could help doctors make better diagnoses
UpToDate: A program for detecting infectious fungal infections in transplant patients with up to date patient data and a mouse approximation
When a patient comes in with a mysterious infection, Mansour turns to a computer program called UpToDate. It’s an incredibly common tool, with more than 2 million users at 44,000 health care organizations in over 190 countries.
Mansour specializes in invasive fungal infections in transplant patients. “Got a nice picture of mushrooms in my office,” Mansour says with a laugh. “I just really enjoy helping patients through, you know, pretty devastating mold and yeast infections.”