The emergence and refinement of AI technology have altered many industries for the better. AI is making waves in the healthcare sector and has been groundbreaking. It has begun to reshape and improve the way that healthcare professionals diagnose symptoms, treat conditions, and monitor patients even when they are far from hospital premises. Research has been simplified and made less labor intensive, diagnoses have become more accurate and made earlier, and treatment is becoming more and more personalized and patient-centric every day. Let’s explore how AI is impacting the healthcare industry.
The Technology
Google Health’s annual Check Up event in 2023 introduced the medical industry to its new Med-PaLM 2 technology- its Large Language Model of AI tech, which has been fine-tuned to manage data in the healthcare sphere. Since the event, the model has been made available to global customers and Google Health partners, and the data gained from those users has led to the development and implementation of the MedLM family of AI models. These models are far more widely available through the Google Cloud Vertex AI Platform.
Google is just one of many well-regarded tech companies that are building and improving AI technology for use in the medical field. The advantages of this tech are broad and can be leveraged in a number of ways to make both the patient’s and the healthcare provider’s experience better, simpler, and more effective. These are some of the different kinds of tech that are currently being utilized.
Natural Language Processing
NLP gives computers the ability to interpret human language and use it in the same way humans do. This tech has reshaped a number of fields and is currently used in medicine to improve patient care by improving the accuracy of medical diagnoses and personalizing medical services for patient-centric care.
Applying NLP to medical records extracts valuable data and helps with accurate diagnoses and selecting relevant treatments. This tech can even help to predict other potential conditions that may arise for the patient in question and do it far faster than a human doctor would be able to.
Rule-Based Expert Systems
This kind of tech was prevalent in AI in the 80s and has become a mainstay in the healthcare industry since then. It usually begins with human experts and human engineers who create a selection of rules around a certain subject. This system can conflict if it becomes too large, and if the knowledge in that sphere changes, it can become time-consuming for human experts to correct and curtail the rules. Machine learning is taking over from human experts and creating rule systems for medical data.
Treatment and Diagnosis Systems
Diagnosing and treating illnesses and conditions has been at the heart of the medical profession since it first began and at the heart of medical AI since it was first developed. Since integrating algorithmic or rules-based into clinical workflows can be a challenge, many of the AI systems currently in use are standalone. Some software vendors are working on creating limited healthcare analytics systems, but this functionality is still in the earliest stages. Software is going to have to change for professionals to take full advantage of the potential of AI, and integration is likely going to be improved in the near future.
Administration
The sheer volume of administration that takes place in the healthcare industry would boggle the minds of most modern professionals in almost all other industries. That kind of admin takes a lot of valuable time out of the days of trained professionals who would do better to spend that time interacting with patients or working on diagnostic and clinical issues. AI is taking that pressure from professionals and handing it over to machines. Not only does this free up a huge amount of valuable time, but it also eliminates human errors that occur when human professionals deal with administration. The process is faster for medical professionals, and it often becomes more environmentally friendly by using less physical paper. Claims can be processed more quickly, thus putting money back into the pockets of medical aid patients.
The Challenges
There are a number of challenges that present themselves where AI meets the medical profession. Some of the most prominent include the security and privacy of the vast amounts of data entrusted to these AI programs. Unscrupulous individuals looking to obtain the data of patents for nefarious uses will target locations like hospitals for the sheer amount of data available in one place, so security must be kept current and maintained at the highest level. Patient safety and accuracy are also big concerns. AI systems need to be rigorously trained to recognize important patterns and understand how different diagnoses and treatments relate to each other, to keep the health and welfare of patients as well-maintained as possible.
The integration of AI tech into existing medical systems is, as mentioned earlier, also a challenge facing the industry today. Medical professionals need to be able to relax and trust that all the systems they have in place will work well together and help the process rather than hinder it. There is a lot of training and development required on the medical professional’s end, and a lot of refining is still to be done on AI tech to reach its optimal functionality.
AI Is Part of The Future
Implementing AI in the medical profession has become increasingly viable as the years have gone by, and it will become increasingly helpful as we move into the future. AI can be life-changing for both medical professionals and patients if applied correctly and reshaped to suit the medical industry in the right ways.
