Artificial Intelligence — a new dimension of unrivaled healthcare efficacy.
Adjusting to the seemingly endless torrent of data available to us via communication technologies like the internet is no easy feat, made even more complex for an industry like Healthcare, where even the slightest missteps can result in lives being lost. It’s a bit of a Catch-22 — more data and information enables better healthcare, but better healthcare also requires the management and utilization of more data and information. As data scales beyond the maximum capacity of existing infrastructures, healthcare is bottlenecked.
Today’s healthcare institutions are clamoring to adopt new technologies and infrastructures capable of handling the immense workload required of such data complexity and volume. One branch of technology thought to be most capable of scaling in both capacity and efficiency is that of artificial intelligence (AI) — “the development of computer systems that can perform tasks that normally require human intelligence”. This is because of its high fluidity, capable of being applied and fine-tuned to so many facets of the healthcare industry in incremental steps that will not hinder existing systems. Even in its infancy, this front of technology already groups together a large collection of advancements, research, and breakthroughs, including the likes of natural language processing, rule-based expert systems, tailored precision robots and machinery, as well as both software and hardware based process automation.
While a lot remains to be discovered and developed, AI tech is already making waves in the healthcare landscape. From aiding physicians in diagnosing to automating administrative and regulatory processes, these technologies are playing a major role in the revolution of global healthcare.
Enhancing diagnosis and treatment
One of the most well known applications of AI is machine learning. Machine learning is a statistical technique, made possible by AI, designed to create data models for training and eventually the efficient extrapolation of insights.
In healthcare, traditional machine learning is already assisting precision medicine by helping predict the most effective treatment protocols for patients based on different variables. Though nothing can replace the intuition, wisdom, and most of all experience of a human physician, machine learning greatly improves efficiency. Where a human physician might have to scour through piles of documents and data, machine learning systems can more quickly compare digital data for quicker decision making.
There are, however, some limitations. Traditional machine learning systems are only as capable and intelligent as their training allows them to be. In turn, training efficacy is also dependent on the accuracy of datasets, and still requires human supervised learning, meaning manual correction and adjustment is required to ensure accurate processing. Put simply, traditional machine learning can only directly help us with what we already know.
Neutral networks, on the other hand, are a more advanced and sophisticated form of machine learning. It takes machine learning to the next step, mimicking the way the human brain works to better discern and identify patterns in data, as opposed to simply comparing data for inconsistencies. The more data neural networks are provided with, the better their algorithms become, picking up on even the smallest changes and deviations in patient data that may lead to complication. Neural networks have been used in healthcare research for decades to help discover correlations in data that have paved the way for predictive healthcare.
The newest, most capable advancement in neural networking has led to a new branch in AI — deep learning. Made possible only with the latest graphic processing units (GPUs) and cloud architectures, deep learning operates on multiple layers of neural networks. With so much more computational power, it enables more precise, efficient, and faster recognition of ever more data.
Deep learning is playing a large part in the better analysis of radiological imaging, filling in the gaps where human eyes fall short. The combined advancements in radiomics and deep learning are achieving higher levels of diagnostic accuracy than that of computer-aided detection (CAD), the previous generation of automated tools for image analysis.
Improving patient engagement
Patient engagement is a significant obstacle in healthcare, contributing greatly to the rates of ineffective treatment outcomes. Joe Greskoviak, president and chief operating officer at Indiana-based health services provider Press Ganey Associates reveals that the outstanding strategy to enhance patient experience and engagement is the utilization of data and AI.
Big data and AI are highly capable of increasing healthcare efficiency, letting physicians and other healthcare workers do more with less. Freed from time restraints, physicians will be able to serve more patients, follow up better on patient aftercare, and — supplemented by AI — can identify risks of future disease and illness for cheaper, more effective treatment.
Machine learning is also capable of understanding every unique patients’ needs, able to formulate tailor-made treatment plans best suited to them. Because patient behavior can be similarly observed, reaching them digitally has never been easier. Perhaps some patients only respond to texts at certain times of the day — machine learning can identify that and respond accordingly. With communication being streamlined, healthcare institutions can spend less on engagement expenditure, and can focus on other areas.
Ethics and implementation
When machines are suddenly put in a position where they can affect, or even make decisions previously made by humans, ethical considerations should be considered, particularly when people’s health and lives are at risk. The same can be said for when AI promotes patient engagement; challenges related to privacy, responsibility, patient autonomy, and informed consent all arise. Transparency can be difficult with AI-powered diagnoses at times — how can a doctor communicate to a patient how an algorithm diagnosed them?
Some patients are hesitant to share personal health information with an algorithm since they are concerned with the same privacy and confidentiality problems that they would have if they shared private health information with a physician. Therefore, the American Medical Association (AMA) Journal of Ethics suggests that physicians should use prudence when implementing AI into medical care. It advises utilizing AI as a supplement, not as a replacement for physicians, and emphasizes the necessity of expert human supervision in identifying potential ethical quandaries.
However, AI remains to be an increasingly significant role in patient engagement and healthcare. Even if AI and ML models are unlikely to replace physicians and administrations any time soon, the technologies have already been shown to be an effective instrument for patient engagement, and further advancement for healthcare industries will undoubtedly continue.
The future of AI
Regardless of its infancy, ethical implications, and our limited understanding of it, AI is playing a significant role in healthcare. It is currently considered an irreplaceable backbone in the creation of precision medicine, generally recognized as a much-needed advancement in the healthcare landscape. Though early applications in diagnosis and supplemental advisory have been challenging, AI will ultimately serve to bolster those functions more than ever thought possible. One glaring challenge remains for AI’s further widespread implementation: its acceptance.
For that reason, the further study, research, and development of AI technologies is fundamental to improving healthcare across the globe. Ever’s own products are developed with the integration of AI, intended to be further trained and honed for the pinnacle of healthcare efficiency and effectiveness. Working hand in hand with pioneering medical experts in varying fields, Ever is facilitating the creation of comprehensive medical technologies that will help prove the necessity of widespread AI implementation, all while remaining patient-centric and adhering to international data standards. We hope that with time and results, healthcare can be transformed with AI’s benefits on a global scale.
Sources:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/
How AI in Healthcare is Revolutionizing Patient Engagement (capestart.com)
https://marketbusinessnews.com/financial-glossary/artificial-intelligence/
