With Artificial Intelligence (AI) and Machine Learning (ML), the healthcare industry is continuing to undergo a transformation. Valued at US$10.4bn last year, the global artificial intelligence (AI) in healthcare market is expected to continue to grow at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. And with breakthroughs such as a report that AI could be used to identify conditions such as Parkinson's disease years before the appearance of physical symptoms, there appears to be a healthy future for the relationship between technology and medicine. Researchers at MIT have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns while they are sleeping. Parkinson's disease is hard to diagnose, researchers say, because it relies primarily on the appearance of motor symptoms, such as tremors, stiffness, and slowness, which can often appear several years after the disease onset.

All of the below papers are free to access and cover a range of topics from Hypergradients to modeling yield response for CNNs. Each expert also included a reason as to why the paper was picked as well as a short bio. We spoke to Jeff back in January, and at that time, he couldn't pick just one paper as a must-read, so we let him pick two. This paper unpacks two key talking points, the limitations of sparse training data, and also if recurrent networks can support meta-learning in a fully supervised context. These points are addressed in seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.

After the'top AI books' reading list was so well received, we reached out to some of our community to find out which papers they believe everyone should have read! All of the below papers are free to access and cover a range of topics from Hypergradients to modeling yield response for CNNs. Each expert also included a reason as to why the paper was picked as well as a short bio. We spoke to Jeff back in January and at that time he couldn't pick just one paper as a must-read, so we let him pick two. This paper unpacks two key talking points, the limitations of sparse training data and also if recurrent networks can support meta-learning in a fully supervised context.

Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding--and no deep learning--expertise.

Health-care institutions have been anticipating the impact that artificial intelligence (AI) will have on the performance and efficiency of their operations and their workforces--and the quality of patient care. But many have already been reaping the benefits of AI tools. And contrary to common, yet unproven, fears that machines will replace human workers, AI technologies in health care may actually be "re-humanizing" health care, just as the system itself shifts to value-based care models that may favor the outcome patients receive instead of the number of patients seen. A survey of more than 900 health-care professionals by MIT Technology Review Insights, in association with GE Healthcare, finds that health-care professionals are already using AI to improve data analysis, enable better diagnoses and treatment predictions, and free medical staff from administrative burdens. These findings are even more critical as health-care delivery and administration are becoming more complex and costly, and professional and technological capacity is ever more burdened, with doctors buried amid vastly expanding workloads and administrative, lower-yield work, and patients robbed of personal interactions with their physicians.

Artificial intelligence can identify illnesses as accurately as trained doctors, a major review has claimed. Research shows AI can spot a host of conditions - ranging from cancer to rare eye diseases - with the same precision as medical professionals. The computer programs uses'deep learning' to train itself to spot diseases by analysing thousands of medical images. It draws on data from past health records to spot similarities in conditions and make an accurate diagnosis without human assistance. Doctors who led the review claimed AI has'enormous potential' for improving the speed and accuracy of diagnosing conditions.

BACKGROUND Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding--and no deep learning--expertise. METHODS We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC).