Both the industrialized and developing worlds are facing unprecedented demographic changes. Birth rates have reached a minimum in some of the world's largest countries, while literally billions of workers prepare to enter retirement.
Researchers and policymakers have, over the last two decades, started to actively seek ways of dealing with the rising healthcare costs of aging populations. Across the board, AI has come to be considered the most advantageous solution.
Not only does artificial intelligence automate basic tasks, removing the need for expensive human intervention in many cases, but it can be used to give a greater sense of privacy and discretion to patients. Moreover, thanks to machine learning, implementations put in place today can improve with time and adjust to new challenges that might arise in the future.
This article discusses a few possible applications of AI/ML technologies in healthcare. Nothing described below lies very far in the future, and will more than likely be a part of the healthcare artificial intelligence market that is expected to grow to 44.5 billion dollars in size by 2026.
Streamlined Pharmaceutical Development
Each year, the pharmaceutical industry spends nearly 100 billion dollars on research and development. Many costs involved in this process can be reduced through the application of big data analytics tools, including neural networks, to databases that categorize the molecular structures of potential medicinal components.
This strategy has especially shown promise in situations when time is of the essence, such as during pandemics. In 2015, during the Ebola outbreak in East Africa, the University of Toronto used AI to rapidly process a database of pharmaceutical compounds. The discovery of a treatment that would previously have required months or even years of analysis was achieved in less than a day.
As has been well reported, AI analysis also has been integral to the development of COVID-19 vaccines and treatments over the last year and a half. As new strains of the virus make their appearance, the same technology continues to be applied.
Automated Medical Documentation
With most clinic and hospital records already stored in a digital format, EHRs (‘electronic health records') play an important role in healthcare. While this technology has made it easier, faster, and ultimately cheaper, to access patients records, the actual digitization of medical documentation can represent a significant burden for time-pressed healthcare providers.
Natural language processing (NLP) technology currently exists that can streamline numerous processes related to medical data collection and storage. While voice recognition and dictation software is nothing new in medicine, proposals are now being made to apply artificial intelligence algorithms that document and analyze the entirety of medical professionals' interactions with patients.
One suggested implementation of this technology would be to use AI and machine learning to process videos that are recorded using cameras that would be worn by clinicians. In effect, this would be quite similar to body cams worn by many police officers today. Information collected in these videos could be quickly indexed and combined with other medical data for further analysis.
In some parts of the world, health clinics and hospitals are few and far between. In others, taking time out of one's busy day to see a physician for routine checks might seem to be an undue hassle. For people living in either of these situations, serious conditions often go undiscovered until it is too late.
Luckily, even in the most remote of locations, most people today already have a powerful diagnostic tool in their pockets—their smartphones. The quality of cell phone camera imagining is getting better every year, while the technology is becoming cheaper to produce. Pictures that are taken using these devices are certainly viable for analysis by AI algorithms.
Already, doctors in regions without access to clinical quality imaging have begun using pictures taken with their own mobile phones to analyze their patients. In fact, smartphones with machine-learning-powered software are currently being used to diagnose skin cancer and melanomas with rates of accuracy as high as 90%. Consumer-grade apps are already on the market that can allow regular users to detect skin changes on their own bodies.
Similar technology is being applied to ophthalmology. Algorithms have been developed and approved by the American FDA to detect retinopathy in diabetic individuals through photo analysis.
Everybody has certain things they prefer to keep private, and for many, health is one of them. Caution is certainly understandable when it comes to discussing medical issues with peers and colleagues, but for some people, even communicating with healthcare professionals can seem daunting.
Chatbots may offer a solution for these kinds of patients. The technology, which is already actively used in telemedicine for appointment scheduling, prescription refills, and triage, is actively being investigated as a way to engage with individuals who require advice about basic, self-administered, healthcare.
In fact, researchers in the United Kingdom found that chatbots would be the preferred choice for patients facing more stigmatizing health conditions, such as STDs. With greater anonymity, patients will be more likely to seek help for issues that may lead to larger concerns further down the line, if otherwise left untreated.
The use cases for AI in healthcare outlined in this article represent only a very small sampling of what may actually be possible. Going into the next decade of Medtech development, we are sure to discover a multitude of groundbreaking innovations, some of which we can only theorize about today.
The key, then, is the ability to turn theory into reality. At Daiger, we specialize in turning theoretical ideas relating to AI and machine learning into actionable solutions that add value to businesses. Please contact us or visit our website to learn more about our services.
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