Artificial intelligence (AI) and associated technologies are becoming more prevalent in business and society. Now, they’re making their way into healthcare, gradually changing medical practice and healthcare facilities everywhere. Technologies like machine learning and digitised data acquisition allow AI to expand and revolutionise areas and roles that were once only suitable for humans, says Guido Voigt is the director of engineering, at Lantronix.
No matter the application, the central goal of AI is to mimic human cognitive function. In healthcare, this is a considerable paradigm shift. Healthcare is amassing more and more data and will require more AI applications to maximise its value, but several different AI technologies are already in use by healthcare providers and biomedical companies.
The complexity and rise of data in healthcare will require more AI applications in the future, and several types of AI are already in use by healthcare providers and biomedical companies.
What Is AI in healthcare?
The healthcare industry is growing and evolving. One of the major changes is the growth of data, both in volume and complexity. Accuracy and speed in collecting and accessing data are essential for healthcare providers to deliver timely and effective care.
Several AI technologies are beneficial for healthcare, including Machine Learning (ML), Deep Learning (DP), and Natural Language Processing (NLP). These technologies imitate human cognitive functioning and have been shown to perform better than humans in certain areas, such as diagnostics.
AI has numerous use cases in healthcare, including disease diagnosis and treatment planning, enhanced patient monitoring, supporting administrative work, assisting in medical research. Along with improving patient outcomes, AI can streamline tasks, improve productivity, and reduce stress on physicians and support staff.
Types of AI in healthcare
AI isn’t a single technology, but a collection of them. Most of these technologies have relevance to healthcare, but they can support a variety of processes and tasks. These are the technologies that hold the highest importance to healthcare currently.
Machine learning is a statistical technique that fits models to data and uses it to learn with training models. One of the most common forms of AI, machine learning has a broad technique that can be suited to different industrial uses. In healthcare, machine learning may be used for precision medicine applications and can predict the success of treatment protocols to prioritise hypotheses.
Healthcare may also employ neural networks, a type of machine learning that’s used to categorise information. Though this technology has been around since the 1960s, it was historically used for categorisation applications and views problems in terms of inputs, outputs, and weights or other variables.
Deep learning, a neural network model with different levels of features and variables that can indicate outcomes, may be used to recognise potentially cancerous lesions in radiographs or identify features and artifacts that are too difficult for humans to perceive. Deep learning has been applied to radiomics in oncology-oriented settings more recently, promising greater accuracy in diagnostics than previous image analysis tools.
Natural language processing
Making sense of human language has been among the primary goals of AI researchers. Natural language processing (NLP), which includes applications like speech recognition, text analysis, and translation, can be used to create, understand, and classify documentation and research.
The two basic approaches to NLP include statistical and semantic NLP. Statistical NLP is based on deep learning neural networks, which increase the accuracy of recognition. It requires a large body of language to learn properly, however. Semantic NLP helps to understand the emotions and context of the language, which is essential to achieving a human level of accuracy.
This data can be analysed for reports, patient interactions, and other tasks. It can also be used to predict patient outcomes, augment triage capabilities, and generate accurate diagnostic models.
Most people are aware of the use of physical robots in healthcare and other industries. They’re among the best-known applications of AI and can be used for lifting, repositioning, welding, assembling, and delivering. These uses are common in healthcare, but recently, robots have taken a more collaborative role with humans.
As physical robots become more intelligent, they can be used for more sophisticated purposes like surgery. Surgical robots have been in use since around 2000 in the U.S., but it still requires a human surgeon. They can augment human senses to allow surgeons to create more precise and minimally invasive incisions, but the ultimate decision is up to the medical mind and training of the surgeon. We can think of robots as an extension of the surgeon, not a surgeon on their own.
Robotic process automation
Robotic process automation performs tasks to relieve the administrative burden of healthcare, which is considerable. The technology is among the more affordable and easier to implement in the AI spectrum, but it boasts an incredible return on investment. Properly implemented, it can significantly improve workflow and reduce costs.
In healthcare environments, robotic process automation is used for mundane tasks like updating patient records, authorisation, and patient billing. It may be combined with other technologies, such as image recognition, to collect data and input it into systems.
Benefits of AI in healthcare
AI’s ability to learn, understand, interpret, and predict is where the promise lies in healthcare. Here are some of the benefits of AI in healthcare:
Data-driven decision making
AI supports clinical decision-making with real-time data. ML algorithms can identify potential risks and provide status alerts on critical patients, prevent diagnostic errors, and foster doctor-patient relationships.
Enhancing healthcare processes
Task automation enables smoother processes. AI allows providers to schedule appointments, follow-ups, surgeries, and more, as well as update patient details, track medical history, and communicate effectively between departments.
Save time and resources
Automation with AI helps physicians analyse and diagnose diseases in a fraction of the time. AI enhances precision medicine with customised treatments instead of a general approach, saving time, effort, and resources for patients and physicians. This process can also prioritise treatments and detect risks early to enable faster medical attention.
Physicians and support staff face a lot of pressure from patients and busy schedules. Automation software can eliminate manual processes to streamline efficiency and improve productivity.
Enhanced patient access
Remote or underdeveloped areas lose access to quality healthcare facilities or well-staffed facilities. AI provides a digital infrastructure that can help with diagnostics and healthcare services for these underserved communities. Remote monitoring helps patients stay in touch with physicians and staff to support better outcomes, regardless of distance.
AI healthcare use cases
AI can efficiently take on some of the most common but considerable problems in the medical industry.
Provide accurate diagnoses
Accurate diagnoses are essential in healthcare. Diagnostic errors can result in physical suffering and pain, high medical expenses, and possibly death. AI has the potential to diagnose diseases with accuracy using technologies like ML and NLP. These technologies can assess medical histories and learn by watching human physicians to accurately assess medical conditions.
Help with drug development
Creating new drugs is an ongoing effort in the medical field to address new or evolving diseases. AI can efficiently and accurately perform tasks using machine learning to discover and refine new drugs, identify correct resources, and analyse clinical trials to reduce time-to-market and produce reliable results.
Monitor patients with robotics
In both the hospital setting and remotely, AI can provide round-the-clock healthcare support with virtual nurses and bots. These technologies can provide testing reports, analyse images, or monitor vital signs from patients in critical care units or in remote areas. Patients can also communicate with AI-based bots with questions or concerns to enhance the level of care.
Manage healthcare data
The healthcare industry is inundated with data that has to be stored and analysed. AI can intelligently process high volumes of data, classify it, and store it securely for future availability. AI is also integral in maintaining up-to-date electronic health records and monitoring doctor-patient interactions for future analysis.
Assist in surgery
As mentioned, robotics in surgery has been one of the most publicised uses of AI in the healthcare industry. For the past few decades, AI robots have been in operation to reduce complications and time for complex or time-consuming surgeries. Robots provide physicians with real-time data to guide them through the surgical procedure.
Looking to the future of AI in the healthcare industry
Though it’s been around since the mid century, AI adoption is increasing in healthcare. Though there are some limitations at this point, AI technologies can be used to enhance physician care and amplify their knowledge to provide better patient diagnostics, treatment, and healthcare outcomes.
The author is Guido Voigt is the director of engineering, at Lantronix.
About the author
Guido Voigt is the director of engineering, at Lantronix, a global provider of turnkey solutions and engineering services for the internet of things (IoT). Guido’s and Lantronix’s goal is to enable their customers to provide intelligent, reliable, and secure IoT and OOBM solutions while accelerating time to market.
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