In a classic case of finding a balance between costs and benefits of science, researchers are grappling with the question of how artificial intelligence in medicine can and should be applied to clinical patient care – despite knowing that there are examples where it puts patients' lives at risk. The question was central to a recent university of Adelaide seminar, part of the Research Tuesdays lecture series, titled "Antidote AI." As artificial intelligence grows in sophistication and usefulness, we have begun to see it appearing more and more in everyday life. From AI traffic control and ecological studies, to machine learning finding the origins of a Martian meteorite and reading Arnhem Land rock art, the possibilities seem endless for AI research. The genuine excitement clinicians and artificial intelligence researchers feel for the prospect of AI assisting in patient care is palpable and honourable. Medicine is, after all, about helping people and the ethical foundation is "do no harm."

It is often infeasible or impossible to obtain ground truth labels for medical data. To circumvent this, one may build rule-based or other expert-knowledge driven labelers to ingest data and yield silver labels absent any ground-truth training data. One popular such labeler is CheXpert (Irvin et al., 2019), a labeler that produces diagnostic labels for chest X-ray radiology reports. CheXpert is very useful, but is relatively computationally slow, especially when integrated with end-to-end neural pipelines, is non-differentiable so can't be used in any applications that require gradients to flow through the labeler, and does not yield probabilistic outputs, which limits our ability to improve the quality of the silver labeler through techniques such as active learning. In this work, we solve all three of these problems with CheXpert, a BERTbased, highfidelity approximation to CheXpert. CheXpert achieves 99.81% parity with CheXpert, which means it can be reliably used as a drop-in replacement for CheXpert, all while being significantly faster, fully differentiable, and probabilistic in output. Error analysis of CheXpert also demonstrates that CheXpert has a tendency to actually correct errors in the CheXpert labels, with CheXpert labels being more often preferred by a clinician over CheXpert labels (when they disagree) on all but one disease task. To further demonstrate the utility of these advantages in this model, we conduct a proof-of-concept active learning study, demonstrating we can improve accuracy on an expert labeled random subset of report sentences by approximately 8% over raw, unaltered CheXpert by using one-iteration of active-learning inspired retraining. These findings suggest that simple techniques in co-learning and active learning can yield high-quality labelers under minimal, and controllable human labeling demands.

A visit to the doctor seems one-on-one. But how will that feeling change when the data gleaned from that interaction takes on unprecedented value? It's a question that doctors and health regulators are grappling with as algorithms learn how to spot pneumonia, and health data becomes the chaff needed to train artificial intelligence. "Previously, the patient is agreeing to supply their very intimate personal information ... to the doctor to help with the diagnosis and management of their own health," said Jacob Jaremko, an associate professor in radiology and diagnostic imaging at the University of Alberta. You provide, for your own care, for your own benefit ... your data."

Today, in these vision centers, technicians take eye scans and send them to doctors in Madurai for review. Automated diagnosis can streamline and expand the process, reaching more people in more places -- the kind of "McDonaldization" espoused by Dr. V. The technology still faces regulatory hurdles in India, in part because of the difficulty of navigating the country's bureaucracy. And though Google's eye system is now certified for use in Europe, it is still awaiting approval in the United States. Luke Oakden-Rayner, the director of medical imaging research at the Royal Adelaide Hospital in Australia, said these systems might even need new regulatory frameworks because existing rules weren't always sufficient. "I am not convinced that people care enough about the safety of these systems," he said.

Do not go gentle into that good night, Old age should burn and rave at close of day; Rage, rage against the dying of the light. Welsh poet Dylan Thomas' famous lines are a passionate plea to fight against the inevitability of death. While the sentiment is poetic, the reality is far more prosaic. We are all going to die someday at a time and place that will likely remain a mystery to us until the very end. Researchers are now applying artificial intelligence, particularly machine learning and computer vision, to predict when someone may die.

This AI will tell people when theyre likely to die -- and thats a good thing. Thats because scientists from the University of Adelaide in Australia have used deep learning technology to analyze the computerized tomography (CT) scans of patient organs, in what could one day serve as an early warning system to catch heart disease, cancer, and other diseases early so that intervention can take place. Using a dataset of historical CT scans, and excluding other predictive factors like age, the system developed by the team was able to predict whether patients would die within five years around 70 percent of the time. The work was described in an article published in the journal Scientific Reports. The goal of the research isn't really to predict death, but to produce a more accurate measurement of health, Dr. Luke Oakden-Rayner, a researcher on the project, told Digital Trends.

Luckily, foretelling such dire consequences may help doctors to stave them off. "Predicting the future of a patient is useful because it may enable doctors to tailor treatments to the individual," lead author Dr. Luke Oakden-Rayner told the University of Adelaide. "Instead of focusing on diagnosing diseases, the automated systems can predict medical outcomes in a way that doctors are not trained to do, by incorporating large volumes of data and detecting subtle patterns." For this study, the system was looking for things like emphysema, an enlarged heart and vascular conditions like blood clotting.The deep learning system was trained to analyze over 16,000 image features that could indicate signs of disease in those organs. Machines have become adept at it surprisingly quickly, even though it's "something that requires extensive training for human experts," said Oakden-Rayner.

Scientists from Google and its health-tech subsidiary Verily have discovered a new way to assess a person's risk of heart disease using machine learning. By analyzing scans of the back of a patient's eye, the company's software is able to accurately deduce data, including an individual's age, blood pressure, and whether or not they smoke. This can then be used to predict their risk of suffering a major cardiac event -- such as a heart attack -- with roughly the same accuracy as current leading methods. The algorithm potentially makes it quicker and easier for doctors to analyze a patient's cardiovascular risk, as it doesn't require a blood test. But, the method will need to be tested more thoroughly before it can be used in a clinical setting.

When 59-year-old Mimi Carroll was diagnosed with breast cancer in 2012, it was a complete shock. The California photographer was an active person who ate healthy. How could she be sick? Carroll's only symptom was a single swollen lymph node. While her paternal grandmother had died of the disease, not one of her five sisters or mother had any evidence of cancer.

Do not go gentle into that good night, Old age should burn and rave at close of day; Rage, rage against the dying of the light. Welsh poet Dylan Thomas' famous lines are a passionate plea to fight against the inevitability of death. While the sentiment is poetic, the reality is far more prosaic. We are all going to die someday at a time and place that will likely remain a mystery to us until the very end. Researchers are now applying artificial intelligence, particularly machine learning and computer vision, to predict when someone may die.