Artificial Intelligence (AI) has been undeniably one of the hottest tech buzzwords of the 21st century.
Just the mention of the word in the same phrase as a company such as Amazon, Google, or Facebook, can send a company’s stock price skyrocketing. But, even though tech companies can create a lot of buzz off this small but significant keyword, the industry that is really turning heads with AI is the healthcare industry.
That’s right: the healthcare industry. It may not seem as enticing an option for AI to thrive at first glance, but once you uncover the nuances of the industry, you will undoubtedly find that when AI is positioned within a healthcare organization using an optimized model, it can really do some good. This incredibly complex future tech is flexible enough to be configured into several unique models to find optimized routes to wisely approach the brave new world of digital healthcare. Let’s peruse some of the more intriguing AI models that may be the source of success for the healthcare industry and what the future holds for this industry on the precipice of digital greatness.
Artificial Intelligence Models for the Future
Deloitte Global predicts that 2019 is the year that cloud-based AI software and services will begin to be used at an accelerated rate by companies. 70% of these companies are set to obtain these innovative AI capabilities through formulating cloud-based enterprise software solutions. As such, these organizations will need to implement a durable yet flexible cloud-based AI model that performs the tasks they need to automate to scale in the future.
Cloud Native Model
The first cloud-based AI model that companies should consider is the Cloud Native Model. Through this model, companies can deploy applications at scale using massive cloud computing and storage capabilities. Cloud native apps run in an elastic computing environment, never settling within an in-house environment. This allows cloud native to deliver reusable features via containers that can operate using an agile project management methodology.
This allows businesses to use the cloud to deploy AI micro-services that can find data insights that allows its machine learning (ML) engine to run at full speed. This allows the model to deploy programs faster, progressively bigger, and without taxing physical network resources. Cloud native model also removes access limitations, thus allowing stakeholders to access the program and insights.
Taking advantage of a cloud-native ML platform can result in significant competitive advantages for organizations that pivot in this direction. Simon Evans, CTO at Amido, says that “cloud-native applications are specifically designed to run on cloud infrastructure, hence the term ‘native'. They are growing in popularity because they deliver benefits, which include: high availability and responsiveness, plus also strong resilience and flexibility through autonomous and self-healing capabilities, such as designing for failure.” In short, cloud native AI gives businesses the ability to deploy services without having to have native AI apps, thus allowing them to leverage the power of cloud AI to perform big data pulls on demand.
Package-Adjunct Model
In complete contrast to the cloud-native AI model, the package-adjunct model takes the finer points of AI capabilities and embeds them in a company’s existing product suites. While some vendors are deciding to surround themselves in a cocoon of cloud technology, others are looking to entrench their applications in AI to supplement their cloud architecture.
This package-adjunct model allows entities to incorporate the technology into practical and achievable activities that increase productivity, strengthen regulatory compliance, and derive meaning from massive data sets. This allows companies to bridge the gap between B2B and become a hybrid B2B/B2C organization that is widely productive and scalable due in part to their automated AI model. This opens up the possibilities of how to automate communications with customers, clients, or patients while simultaneously gathering a plethora of insights from the cloud and inherently through the network.
Open-Algorithm Model
Organizations that focused on dipping their toes into a cloud-based AI model to build something truly unique from scratch are currently focused on developing within an open-algorithm model. This type of model gives companies the freedom to develop cloud-based AI solutions that meet specific business needs, use cases, and verticalized issues. Developers in open-algorithm models are able to deploy and A/B test their integrated AI solution quickly, thus giving them the ability to go to market quickly with a scalable solution.
How Does AI Translate to the Healthcare Industry?
The healthcare industry is booming thanks in part to AI which Accenture estimates will be a $6.6 billion market by 2021. Market adoption is increasing so much that one estimate projects AI in healthcare to be an almost $200 billion industry by 2025. Therefore, we can see that the future of AI healthcare technologies is looking bright. All the industry needs to do is build the AI solutions that allows them to create their niche within the industry at large and personalize their products and services in a way that is beneficial to consumers.
The industry has reached a point where patients are more willing to participate in the healthcare cycle with AI at the wheel driving that trend forward. AI offers a solution to democratize access to electronic health records (EHR) and smartphone apps for at-home health solutions that can altogether change the landscape of healthcare. This technology also offers many interesting applications for the healthcare industry that solve a surplus of issues that have been stagnating industry growth and causing decreases in quality of care for patients. From apps that assess your risk for skin cancer to new technology for accessing health records at home, patient-centric AIs are making it easier for people to receive high-quality care.
AI research and development is answering the call for better communication in healthcare. Research has shown that the effective application of AI is projected to result in annual savings of $150 billion in the US healthcare industry. Yet communication among healthcare staff isn't the only problem: The patient-doctor relationship is growing strained these days, and doctors are looking to improve it. AI offers that next great solution that will help make application and administration of medications with the precise dosage more streamlined and communication with patients easier.
AI Benefits and Applications for the Healthcare Industry
AI is of great importance in healthcare and industry investors are beginning to notice this fact. One of the greatest benefits of AI is its ability to enhance the capacity to process & store large amounts of data, thus allowing hospitals to process patient data in a standardized, modernized manner that translates the personally identifiable information (PII) into functional tools. This functionality is a great addition to the expertise of the human factor in healthcare that offers the following great benefits to providers and practices around the world:
- Fast, Accurate Diagnostics
AI gives teams the ability to collect patient data over time that allows them to also read online medical books and research notes in a matter of seconds. This allows doctors to make better educated decisions based on all the data that AI has accumulated. These artificial neural networks can already diagnose quickly and accurately diseases that include eye problems, malignant melanoma, and more. Now, it’s just a matter of time before these solutions are massively implemented and adopted around the world. - Human Error Reduction
AI may be most effective at reducing human error. Medical errors are the third leading cause of death in Americans, following heart disease and cancer. 80% of these cases occur because of miscommunication during patient care transfer. AI could be the best assistant for surgical scenarios as it would monitor the whole procedure and greatly decrease stressful situations. According to research by Accenture, effective application of AI to medical dosage error reduction can result in a savings of $16 billion for the healthcare industry by 2026. - Virtual Robotic Opportunities
With AI, patients can get timely assistance from their doctor without visiting the hospital via telemedicine. This venture could result in cost-cutting due to efficiency improvements that could help address the roughly 20% of unmet clinical demand. Virtual AI assistants can also optimize this experience by offering online care to patients while doctors are not on call and being able to add their data immediately to the cloud for physician review.
These are just some of the benefits known to us that are known at this moment. While evidence of the potential benefits of AI applications in healthcare mount and funding pours into the space, a number of challenges to widespread adoption and implementation of these tools remain. Some investors will always be naturally skeptical of this innovative technology. There will be a cohort of patients that will be apprehensive to change to AI because they may not understand enough about how AI works or worry that the technology will not understand them. As AI technology continues to develop in the healthcare industry, we will be more likely to see new breakthroughs in science and medicine regularly occurring.
What Does the Future Hold for Healthcare AI?
AI powered by the cloud can empower healthcare staff (from doctors to administrative staff members) with strategic, transformative new capabilities in an agile and scalable way. This can enable healthcare organizations to reduce costs and improve security, agility, and scalability. Now, more than ever, AI technologies are being integrated with Machine Learning (ML) capabilities that allows organizations to process incredible amounts of healthcare data in real time. This reduced latency can maximize the value of the patient data, allowing hospitals to deliver actionable insights that will improve patient outcomes and improve the experiences of both patients and healthcare professionals.
Othman Laraki, co founder and CEO of Color Genomics, says that “Through advances in machine learning and artificial intelligence, we have the ability to reason about and utilize data at an unprecedented scale in order to predict, prevent, and treat disease more effectively." Machine learning and AI will eventually close the loop in the equation for healthcare organizations to harness the predictive power of data to ensure patients don’t receive the wrong medication or diagnosis. The beauty behind AI + ML applications is in its ability to be deployed through an app available to providers in low-resource areas. This allows for easier adoption on a global scale for potentially billions of patients and decreases the need to have a trained diagnostic radiologist on site.
What’s Next for AI in Healthcare?
Founder of MEDGIC, Dr. Reid Lim, tells us that “healthcare systems are becoming unsustainable and we need AI to help automate some things and to help alleviate the burden on doctors. AI is not new and it seems strange that some people are only beginning to grasp the use AI.” Although AI in the healthcare industry is booming right now, the industry still needs to find solid technical teams to handle the development of healthcare AI within the network.
Compliance and regulatory laws will also need to be fleshed out to ensure that these solutions are in-line with HIPPA. Once uniform standards of practices are defined for AI in healthcare, demand will continue to increase at the same or similar rate that we are seeing today.
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