FREMONT, CA: Digital transformation, affecting all major domains, holds particular promise for healthcare systems across the world. In particular, in medical imaging, the expectation of obtaining a more accurate diagnosis from detailed anatomical and functional images forces radiologists to deal with these ever-evolving technologies. Excellent results are achieved, when artificial intelligence (AI) is used to classify images into several categories correctly.
The applications of AI systems in medical imaging require a human doctor to oversee each decision with diagnostic or therapeutic impact. Instead of replacing human responsibility in its entirety, AI-based solution smart assistants are built into the workflow to augment and support medical practitioners. This eases the cognitive strain on doctors and frees up time to handle more complex tasks.
Expressions for AI in medical imaging have lacked standard inputs and outputs among comparable algorithms. Without standardized inputs and outputs for AI applications, it becomes daunting to develop standard data sets for training and testing AI algorithms, and after all these algorithms may show different results for the same finding. A standard method for accepting inputs and outputs for algorithms to process will be needed because algorithms may run on an internal server or in the cloud. Ideally, AI applications should be developed using a format that converts human narrative descriptions of what the algorithm should do to machine-readable.
To develop high-performing AI algorithms, models will require training on quality datasets that contain rich metadata. Privacy concerns are limiting the ability of institutions to make data publicly available, hindering the development of AI. Speeding up the release of publicly available data sets and AI techniques such as transfer learning can allow patient data to remain protected.
IT developers create an efficient user interface and user experience to integrate with existing clinical workflow tools to accelerate AI use. The medical imaging population must involve in assessing the clinical and infrastructure needs and work with existing authorities to find solutions that ease the adoption of AI in clinical practice. The future for AI applications to improve image-based diagnosis is enormous. The opportunities and challenges summarized here can serve as a road map for future development.