Hope for the post-pandemic world (part I)
“Historically, pandemics have forced humans to break with the past and imagine their world anew. This one is no different. It is a portal, a gateway between one world and the next. We can choose to walk through it, dragging the carcasses of our prejudice and hatred, our avarice, our data banks and dead ideas, our dead rivers and smoky skies behind us. Or we can walk through lightly, with little luggage, ready to imagine another world. And ready to fight for it.”
This pandemic has wreaked havoc in healthcare, but the human spirit yearns for hope with each global catastrophe.
As we turn to a new year, it is a good opportunity to reflect on the current state of artificial intelligence in healthcare as well as future possibilities of artificial intelligence for health and disease. Part I of this series will highlight the current state of artificial intelligence and the following two weeks will be on the near future (this decade) and then future (beyond this decade) aspects of AI in healthcare.
Data science, machine and deep learning, artificial intelligence and a panoply of technological tools have had an impact on medicine and healthcare in several domains, especially in medical imaging and decision support. As the COVID-19 pandemic demonstrated, however, these tools have not been as successful as clinicians had hoped. This observation is probably more about deficiencies in healthcare data, databases, and information technology infrastructure than it is for AI itself. Despite this observation, expectations remain high that AI and its technological tools will deliver in the long term.
An impressive portfolio of technological tools are now available (or coming soon) in the domain of artificial intelligence in medicine. By far the most mature appears to be deep learning in the form of convolutional neural networks (CNN) in medical imaging. The Cambrian explosion of CNN tools have made progress in static imaging, but are now starting to make inroads into moving images such as ultrasound studies, endoscopic imaging, and even echocardiograms.
Both machine and deep learning have also made progress in electronic medical records – in projects on readmission criteria or decision support – but these have not been nearly as productive as medical imaging due to the records being fragmented in location and complex in nature.
In addition, there is promise in the area of drug design or repurposing in treatment for cancer patients and even for COVID-19 patients during the pandemic as a result of machine and deep learning, especially with protein structure determination based on genomic sequencing.
There are other exciting areas for artificial intelligence at present as we head into the future decades. Natural language processing (NLP) capabilities with transformer architectures such as the generative pre-trained transformer 3 (GPT-3) have started to be considered for its deployment in healthcare. This technological tool of NLP continues to advance at an exponential pace.
Unsupervised learning also holds great promise for discovery of new phenotypic expressions of disease subtypes and treatment responses.
Lastly, healthcare is starting to embrace an older AI technology of robotic process automation (RPA) for administrative tasks that can be automated by algorithms rather than completed by humans.
For data scientists, this past decade has been a journey into healthcare with mixed dividends. While the aspiration to help improve patients’ lives and/or create a viable business venture was a driving force for artificial intelligence experts, the nuances of access to healthcare data and inadequacies of databases was a deterrent for some. For clinicians at all levels of education and training as well as practice, there is an escalating need to learn about the basics of AI as it is becoming more evident that those clinicians who understand AI will have a growing advantage over those who do not.
Lastly, the myriad of issues and challenges in ethical, legal, regulatory, and financial domains focused on AI technology usually have a linear trajectory whereas emerging and disruptive AI technologies have mostly an exponential trajectory; the mismatch of these two curves creates both challenges and opportunities.
by Anthony Chang
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