If A.I. Can Diagnose Patients, What Are Doctors For? Large language models are transforming medicine--but the technology comes with side effects. "I'm worried these tools will erode my ability to make an independent diagnosis," a medical student said. In 2017, Matthew Williams, a thirtysomething software engineer with an athletic build and a bald head, went for a long bike ride in the hills of San Francisco. Afterward, at dinner with some friends, he ordered a hamburger, fries, and a milkshake. Midway through the meal, he felt so full that he had to ask someone to drive him home. That night, Williams awoke with a sharp pain in his abdomen that he worried was appendicitis. He went to a nearby emergency clinic, where doctors told him that he was probably constipated. They gave him some laxatives and sent him on his way. A few hours later, Williams's pain intensified. He vomited and felt as though his stomach might burst. A friend took him to a hospital, where a CT scan revealed cecal volvulus--a medical emergency in which part of the intestine twists in on itself, cutting off the digestive tract. The previous medical team had missed the condition, and may even have exacerbated it by giving him laxatives. Williams was rushed to the operating room, where surgeons removed about six feet of his intestines. After recovering from surgery, Williams began to experience severe diarrhea almost every time he ate. Doctors told him that his bowel just needed time to heal. "It got to the point where I couldn't go out, because I would constantly eat something that would make me sick," he said.

Advanced artificial intelligence models score well on professional medical exams but still flunk one of the most crucial physician tasks: talking with patients to gather relevant medical information and deliver an accurate diagnosis. "While large language models show impressive results on multiple-choice tests, their accuracy drops significantly in dynamic conversations," says Pranav Rajpurkar at Harvard University. That became evident when researchers developed a method for evaluating a clinical AI model's reasoning capabilities based on simulated doctor-patient conversations. The "patients" were based on 2000 medical cases primarily drawn from professional US medical board exams. "Simulating patient interactions enables the evaluation of medical history-taking skills, a critical component of clinical practice that cannot be assessed using case vignettes," says Shreya Johri, also at Harvard University.

Chatbots could be used to diagnose patients in a bid to reduce waiting times in emergency departments, researchers have indicated. It comes after a study found that ChatGPT, powered by artificial intelligence (AI), 'performed well' in generating a list of diagnoses for patients and suggesting the most likely option. Researchers in the Netherlands entered the records of 30 patients who visited an emergency department in 2022, as well as anonymous doctors' notes, into ChatGPT versions 3.5 and 4.0. The AI analysis was compared to two clinicians who made a diagnosis based on the same information, both with and without laboratory data. When lab data was included, doctors had the correct answer in their top five differential diagnoses in 87% of cases, compared with 97% for ChatGPT 3.5 and 87% for ChatGPT 4.0.

The most obvious advantage of using synthetic training data is that it can supplement datasets that would otherwise lack sufficient examples to train a model. As a general rule, more and higher-quality training data equals better performance, so synthetic data can play a hugely important role for machine learning engineers working in fields that suffer from a scarcity of data. However, using synthetic data comes with pros and cons. Let's look at some advantages and disadvantages of using synthetic training data. When high stakes models, such as those used to run autonomous vehicles or diagnose patients, run in the real world, they need to be able to deal with edge cases.

For the last couple of years AI has been touted as a possible solution for the endemic inefficiency that plagues the healthcare industry. In the United States approximately a third of government spending on healthcare goes to administrative costs. The current global pandemic has accelerated this process by further exposing the frailties of the healthcare industry. In particular, the pandemic has stressed the need for better processes to help medical staff deal with unprecedented surges in demand and further reiterated that the only thing that spreads faster than a virus in the modern world is misinformation and panic. Emphasis on these issues has led many to proclaim that the future of AI in healthcare is Conversational.

Although the initial wave of the SARS-CoV-2 pandemic has abated in many countries, healthcare providers are still looking to identify as many COVID-19 patients as possible and contain the disease. Fast and accurate diagnosis is especially important when unsuspecting patients with a coronavirus infection come to the hospital with health complaints but don't yet show symptoms of COVID-19. Nasal swab samples analyzed by RT-PCR are currently recommended for the diagnosis of COVID-19, however, supply shortages, a wait time of up to two days for results, and a false negative rate as high as 1 in 5 mean alternative, large-scale COVID-19 screening tools are still being sought. SARS-CoV-2 is known to damage lung tissue, and in a distinct way that doctors are now seeking to exploit for new diagnostic approaches. Many COVID-19 patients develop pneumonia, which can progress to respiratory failure and sometimes death.

Students at the Indianapolis-based medical school, one of the nation's largest, learn stethoscope skills but also get training in hand-held ultrasound in a program launched there last year by Dr. Paul Wallach, an executive associate dean. He created a similar program five years ago at the Medical College of Georgia and predicts that within the next decade, hand-held ultrasound devices will become part of the routine physical exam, just like the reflex hammer.

The healthcare IT world is buzzing about the growing role of artificial intelligence (AI). This emerging, promising technology may soon help physicians diagnose and treat their patients faster and more accurately using complex algorithms and datasets–and in some cases, can even diagnose patients faster than human physicians. This future is not far away, either. Here are three ways AI will change the patient experience in the near future. AI in medical imaging has existed since the 1980s.

Switzerland based SaaS startup Sophia Genetics is hoping to give IBM Watson a run for its money in the healthcare diagnostics space. It's built a big data analytics platform that harnesses clinicians' medical expertise to enhance genomic diagnostic via AI algorithms -- leading, it says, to better and faster diagnoses for patients with diseases such as cancer. Hospitals that use the platform are intended to jointly benefit from expert-fed, algorithmic DNA sequencing diagnostic insights exactly because they are shared across the platform. So as the user-base scales -- it says it's adding 10 new hospitals each month -- Sophia Genetics' AIs get smarter and more accurate, and patients anywhere can benefit from the pooled knowledge. The company is announcing a $30 million Series D funding round today, adding UK-based VC firm Balderton Capital to its investor roster, along with 360 Capital Partners.

Health care start-up Babylon Health has said that its artificial intelligence (AI) is able to diagnose medical conditions as accurately as a doctor, but physicians have called the claims "dubious." Babylon has created a chatbot that lets you talk with an AI that can interpret symptoms and tell a person what the problem is. It is not an official medical diagnosis, however. The British company fed its chatbot questions from the official exam set by the the Royal College of General Practitioners (RCGP) in the U.K. The RCGP is a membership body for family doctors in the U.K. and overseas. Any doctor practicing in Britain must pass this exam.