Humans can easily identify anatomical planes (axial, coronal, and sagittal) on a 2D MRI slice, but automated systems struggle with this task. Missing plane orientation metadata can complicate analysis, increase domain shift when merging heterogeneous datasets, and reduce accuracy of diagnostic classifiers. This study develops a classifier that accurately generates plane orientation metadata. We adopt a 2.5D context-aware model that leverages multi-slice information to avoid ambiguity from isolated slices and enable robust feature learning. We train the 2.5D model on both 3D slice sequences and static 2D images. While our 2D reference model achieves 98.74% accuracy, our 2.5D method raises this to 99.49%, reducing errors by 60%, highlighting the importance of 2.5D context. We validate the utility of our generated metadata in a brain tumor detection task. A gated strategy selectively uses metadata-enhanced predictions based on uncertainty scores, boosting accuracy from 97.0% with an image-only model to 98.0%, reducing misdiagnoses by 33.3%. We integrate our plane orientation model into an interactive web application and provide it open-source.

And given rising interest rates and the deep decline in the public markets, … are building artificial intelligence and machine-learning-powered …

In a nondescript hospital laboratory in Denbighshire, a quiet revolution is taking place. It involves nothing more glamorous than a digital scanning machine and a computer terminal. But the ramifications for medicine could be profound. Here, it could be argued, virtual consultants are being generated. READ MORE: Wales' worst ever shipwreck disaster was off Anglesey - and no one's ever heard of it These are "experts" who, every day, are getting better and better at their jobs.

Doctors are developing novel solutions to make sure they come up with the right diagnoses. A flood of new initiatives by researchers, physicians, health-care systems, nonprofits and malpractice insurers is yielding new insights and approaches. These include sophisticated computer programs, some that use artificial intelligence to help analyze and diagnose tough cases, and others that scan records for errors such as missed test results and appointments. Advanced technologies aren't just bringing the processing power of big data and machine learning to bear. They are also allowing more doctors to share their knowledge--including lessons they've learned from their own diagnostic mistakes.

I recently attended the ReWork Deep Learning conference in London, a fantastic event bringing world leading academics, large multinationals and start-ups together to discuss the latest advances in this branch of artificial intelligence and how it can improve our lives. Although very few applications include AI at present, it is predicted to increase exponentially over the next few years as the technology currently in development gets released for general use. While there were many fascinating talks about advances in healthcare and construction, there were two distinct themes that are relevant to marketers that ran through almost all the talks I attended, and there is no doubt that both issues will need addressing soon. The first of the issues was the moral and legal agency of AI applications. If your doctor misdiagnoses you, you have somewhere to go to complain and have the potential for compensation.