Healthcare is an integral part of modern life. As our growing global population ages, there is a greater need for a more efficient and cost-effective healthcare industry to serve all, regardless of socio-economic standing. Chronic illnesses like cardiovascular disease or diabetes, in particular, negatively impact the healthcare sector because this ever-growing population cannot be managed at already full hospitals.
Digital healthcare, "smart healthcare," via the Internet of Medical Things (IoMT) and embedded AI are changing how doctors and nurses serve patients. Through the use of edge computing, let's see what these new technologies have to offer to benefit patients, healthcare providers, drug companies, and insurers alike.
The notion of "smart healthcare" was initially coined by IBM in 2009 through their "Smart Planet" initiative. This was pitched as an intelligent infrastructure that uses sensor data information transmitted through the Internet of Things (IoT) to be processed by machine learning and artificial intelligence. All to improve efficiency, optionality, and outcomes for whatever industry or vertical it applies to.
The idea was to provide healthcare providers, patients, and insurers with a more cohesive data information landscape to create a multi-level change throughout the sector. Shifting away from a disease-first approach to a patient-first approach focused on preventative medicine instead of treatment after the fact. "Smart healthcare" involves non-human and human participants (patients, doctors, nurses, hospitals, research institutes, medical equipment manufacturers, drug companies, insurers, etc.). It comprises multiple different technologies, including:
Artificial intelligence has been getting a lot of publicity lately. It has become a "catch-all" term for systems that perform complex tasks that used to require human input. Chatbots like Open AI's ChatGPT or Google's Bard take center stage in technology, investment, and public conversations. But AI in healthcare does a lot more than answer a few simple clerical questions or give you a great recipe for baked chicken thighs. AI is often used interchangeably with its related pieces, like deep learning and machine learning, which can muddy the waters for a clear understanding of what AI means in a specific case for the general population.
In healthcare, AI is starting to get used for many applications that previously required human input.
Machine learning is a branch of computer science and artificial intelligence that focuses on using data and algorithms to imitate learning the way the human brain does, with gradual improvements in accuracy and efficiency as more data is captured and "learned." It is essential in the rapidly accelerating field of data science and is integral to the efficacy of embedded tech and embedded AI for the healthcare sector.
Machine learning is what makes AI "artificially intelligent." It is an artificially intelligent machine operating through human-created algorithms and learning protocols to learn and act as a human brain would in a specific situation if the human brain could analyze the same data sets. The beauty of ML is that the data sets can be far more complex and protracted than what a human brain can handle and can process that massive amount of data in real time, making learning far faster than a human.
Because healthcare requires strong and correct communication, AI was developed to use natural language processing (NLP) to make sense of human language within the medical field. Basic systems like Apple's Siri or Amazon's Alexa use NLP to understand the questions and commands we tell them. In healthcare, this is particularly important as the names of specific treatments and drugs are often complex and must be understood by the AI system correctly, or the outcome could be completely wrong.
One of the core focuses of AI in healthcare over the past several decades is the diagnosis and treatment of disease. Initially, rule-based system algorithms showed they have the practical potential for diagnosing certain illnesses far more accurately than their human counterparts. But the medical community pushed back, and clinical practices did not fully embrace the outcomes. The consensus was that more research was needed.
Despite the accuracy of the diagnoses and prescribed treatments, the rule-based AI systems came up with the problem was integrating these into medical workflows and Electronic Health Records (EHR). The problem relies on the lack of providers offering advanced healthcare AI systems; those that exist are often developed to find a single issue, not many illnesses. But that is beginning to change as the focus becomes integrating robust AI findings into the medical hierarchy.
Training these neural networks to build a library of data they can pull from is underway. Specific deep learning systems can diagnose multiple illnesses, but it doesn't come without cost. To fully take advantage of the enormous potential of AI in health care often requires massive investment or leveraging capable third-party technology partners who have AI capabilities and can correctly integrate them into existing EHR systems.
There are a few ways artificial intelligence gets used to diagnose patients. This typically happens by analyzing a rule-based set of symptoms the patient is experiencing. For example, imaging tools help clinicians diagnose hard-to-see problems like Cancer or liver disease, which assist in the diagnostic procedure. Different applications establish deep learning tools to enhance radiological diagnostics through processing large amounts of medical data. These strategies help doctors define and understand cancer severity. Sometimes, AI is used to conduct a "virtual biopsy," assisting clinicians in tumor analysis and genetic trait detection.
AI can simultaneously look at the micro and macro pictures of a single patient or entire population. This is just the cusp of the potential of AI in the medical industry.
One of the most common ways embedded AI is being used in the medical field is the real-time monitoring of patients. Through wearable medical technology like heart rate monitors, oxygen sensors, fitness trackers, etc., clinicians can access moment-to-moment medical information about the patients they care for. This is especially useful when tracking the elderly or disabled, who cannot easily reach a doctor's office or the hospital if needed. By providing clinicians with crucial patient data in real-time, clinicians can ensure important treatment changes can be implemented immediately.
The typical path of medical information flow using embedded wearable med-tech:
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These devices are designed to improve different aspects of life. For those with chronic illnesses like diabetes, having real-time access to their blood glucose levels without constantly pricking their fingers for a sample is a game changer. Diabetics no longer have to carry pins to take blood and test strips to analyze their blood when wearing this embedded technology. Their quality of life is increased, and the risk of catching an illness from unsanitary blood testing is no longer possible. In the past, a person with diabetes would have to find a public restroom or sit and take blood in a crowded train station. That is no longer the case with this simple embedded medical device.
The point of medicine is to provide longer life and a better quality of life to patients. The Oxford English dictionary defines medicine as "the science or practice of diagnosing, treating, and preventing disease." AI in healthcare does just that but can be extremely difficult and costly to implement across different medical practices, communities, and institutions. For this reason, working with a qualified technology partner with expertise and knowledge in using advanced AI & ML for the medical world is a must to get off the ground with a hospital or medical facility digital transformation.
But you should not work with just any provider offering an AI solution for the medical industry. There are severe legal concerns around patient privacy and data security. Using a provider operating on a traditional cloud won't provide the protection needed for medical AI applications. The more sensors and devices connected to a system and a centralized cloud, the greater the vulnerability points for hackers and cyber-criminals; this means a security-first system must be employed to prevent a breach at any single point throughout a medical facility.
Internet of Everything (IoE) corporation has developed a system, unlike any other, that is ideal for medical applications knowns as Eden. It doesn't transmit data through a standard WWW internet protocol, so security is built into the foundation. Instead, Eden establishes an "online private garden" that changes most networks' traditional "client in a client-server" setup via blockchain. An Online Private Garden service comprises a secure pool of devices communicating over public and private key-protected connections and autonomously nominated workflow paths. Every device in an OP Garden knows the state of all other devices on the blockchain, which additionally keeps track of all data movement and verifies the data is coming from a trusted node.
The point is that Eden is secure. With quantum tunneling and TCP/IP port communications, attack vectors are removed, and the system is stable & secured. The unique features of Eden's Online Private Gardens are decentralized, autonomous, scalable, self-healing, and built-in malware detection and isolation.
Eden is a premiere ecosystem ideal for healthcare installations and is now open for partnership applications. Let the expertise of IoE make your digital transformation seamless through a technology partnership.
Apply for open Eden Acess Here, and a partner crew member will reach out soon to learn more about your needs and how IoE can help.
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