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Hope for the post-pandemic world (part II)

Hope for the post-pandemic world (part II)

“It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.”

Andrew Ng, AI expert and Stanford faculty

Last week, we summarized the current state of artificial intelligence in clinical medicine and healthcare. This week, in the midst of a global public health crisis, we project into the future, considering just how artificial intelligence could be deployed in the “near” future (this decade). A further glimpse into the future will be covered in the last part of this series.

There will be exciting developments coupled to artificial intelligence for diagnosis and treatment of medical conditions during the remainder of this decade. For instance, there is exciting work on pushing AI “peripherally” to devices – even at the microprocessor level. This artificial intelligence of things, or AIoT, provides a portfolio of “intelligent” devices for the future of chronic disease management as well as population health strategies. In short, AI in healthcare will be in two directions: a centralized cloud for analytics and concomitantly a peripheral network with AI embedded in many devices and sensors. This will be the AI equivalent of a brain and peripheral nervous system.

In addition, the limitations and nuances of existing electronic medical records in their current state demands a disruptive technology in the future. A promising technology is graph and hypergraph databases coupled with knowledge graphs to create a paradigm shift in how electronic medical records are structured and curated. Federated learning consists of edge devices with local data that can train their own copy of the model from a central server, and only the parameters/weights from these models (but not the data) are sent to the global model. Multimodal AI, such as combining perception and linguistic capabilities of machines, can increase the potential for AI to deal with the complexities of healthcare.

In the area of medical education and clinical training, adding an AI dimension to extended reality can be termed intelligent reality. Along with this virtualization of clinical medicine and healthcare can be AI imbued in the virtual twin concept for both the patient as well as the health system. All of this demand for artificial intelligence will warrant the availability of quantum computing. For AI experts, there will be an increasingly dire need for more talent, especially at the PhD level, to work in healthcare, but an escalating amount of automated machine learning will be accessible.

In addition, AI alone in medicine is not going to make an impact long term unless it is applied “intelligently” with human clinician insight and intuition to render it truly meaningful.  For the clinicians, adoption will need to be accelerated to accommodate the technology that is available. A small cohort of clinicians will need to be champions of AI by learning a minimal amount of knowledge to be able to be conversant with a data scientist. Creative uses of AI in the future can include embedding knowledge into the EHR while gaining continuing medical education credits. The ethical and legal aspects of healthcare AI continue to be widely and publicly discussed and debated.

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