Artificial intelligence is changing medical practice and the healthcare industry.
Technologies including machine learning and digitised data acquisition are allowing AI to expand into areas that were once reserved for humans – and this can be clearly seen in the healthcare sector.
We spoke to Xavier Dupont, Senior Director, Product Line, at Lantronix, to get his take on the industry and to understand more about AI applications in healthcare.
Xavier’s and Lantronix’s goal is to enable digital transformation for their clients by providing Software as a Service (SaaS) solutions, connectivity services, engineering services, intelligent hardware and turnkey solutions for the Internet of Things (IoT) and Remote Environment Management (REM).
AI History in Healthcare
The goal of AI is to mimic human cognitive function, which represents a paradigm shift in healthcare. The complexity and rise of data in healthcare will require more AI applications in the future. Several types of AI are already in use by healthcare providers and biomedical companies.
AI can be traced to the 1950s when Alan Turing, a British mathematician, questioned whether machines could think. It wasn’t until over a decade later that AI was incorporated into life sciences, and in the 1970s, it made its way into healthcare.
In the 1980s and beyond, AI found its way into more clinical settings, using artificial neural networks, Bayesian networks, and hybrid intelligence systems.
It wasn’t until recently, however, that AI in healthcare had grown to a predominant industrial application of AI in aggregate equity funding. Now, both physical and virtual AI are used to assist patients and providers.
Types of AI in Healthcare
AI isn’t one singular technology, but a group of technologies with specific processes and tasks to support healthcare goals. The processes and tasks these technologies support can vary, and some are of high importance to improving the quality of healthcare.
In industry, machine learning is one of the most common forms of AI and the core of many different AI applications. In healthcare, machine learning is often used for precision medicine applications to predict the success of treatment protocols.
Healthcare also uses neural networks, a complex form of machine learning that can be used for categorisation. For example, neural networks can determine if a patient can acquire a disease. Deep learning, a neural network model with levels of features of variables that indicate outcomes, is often used to recognise potentially cancerous lesions in radiographs or detect relevant features that humans can’t perceive.
Natural Language Processing
AI may involve natural language processing (NLP), which includes applications like text analysis, speech recognition, and translation. In healthcare, NLP is primarily used in the creation, understanding, and classification of documentation and research.
This unstructured data can be analysed to prepare reports, transcribe patient interactions, and more. It can also aid in the prediction of patient outcomes, augment triage systems, and generate diagnostic models.
Physical robotics are among the best-known applications of AI, in healthcare and other industries. Advanced robotics are collaborative with humans and can be used for more sophisticated purposes, such as surgery.
Surgical robots were approved in the US since around 2000. This technology can enhance human senses to allow surgeons to see more effectively and create more precise and minimally invasive incisions, stitches, and more. The decision-making is still left up to the human surgeon with robots acting as an extension of the surgeon, but not doing the surgery themselves.
Robotic Process Automation
Robotic process automation performs tasks to relieve the administrative burden of healthcare facilities, which can be considerable. This technology is distinct among types of AI in that it’s less expensive and easier to implement, but can significantly improve workflow and cost-effectiveness.
In healthcare, robotic process automation is used for mundane tasks like updating patient records, patient billing, and authorisation. It can be combined with technology like image recognition to extract data and input it into systems.
AI for Diagnostics and Treatment
AI for diagnostics and treatment has been a big focus of AI since its earliest use in medicine. Rule-based systems were used for diagnosing and treating disease, but they were not fully adopted because they couldn’t compete with human diagnosticians.
In combination with machine learning and NLP, precision medicine with AI is now used for cancer diagnosis and treatment. Though there are obstacles, such as treating certain types of cancer, precision medicine is a capable application that offers potential for better diagnostics and treatment in the future.
AI technology in cancer care could improve the accuracy and speed of diagnosis, aid in decision-making, and present better health outcomes. This relies on AI’s ability to process large volumes of data and extract relationships, perceiving characteristics that can’t be seen by humans.
AI is used in neurology to help with the diagnosis and treatment of neurodegenerative diseases, such as Parkinson’s Alzheimer’s, and Amyotrophic Lateral Sclerosis (ALS). This is done using AI to interpret cognitive test scores, speech recordings, and neuro-imaging to assess biomarkers of these diseases and compare results to normative values.
AI is being used to promote better treatment outcomes for heart patients. The primary uses are for detecting heart disease, enhancing diagnostic radiology capabilities, treating strokes, predicting cardiac risk, and monitoring heart rhythms using biomedical devices.
The Future of AI in Healthcare
Though it’s been in practice for decades, widespread AI adoption in healthcare still faces obstacles. AI systems are unlikely to replace physicians completely but can be used to augment their knowledge and expertise to provide better patient care and outcomes.