On 16 May 2024, just 3 days after OpenAI launched their latest AI model ChatGPT-4o, Google AI announced the launch of MedGemini, a family of AI research models to aid in the field of medicine built on Gemini's advanced capabilities. Apart from marvelling open-mouthed at the speed at which AI is advancing, this has also got at least some of us thinking: what is the role of AI in the healthcare industry?
With medicine being one of the most highly respected fields, closest to human life and its fragility, the changes brought by AI have often been viewed with suspicion. A look at the small shifts we have seen around us would serve as a good starting point.
The beginning.
The primary involvement of AI thus far in the field of medicine has been mainly in clerical tasks such as scheduling appointments and reminder calls for follow-up appointments. Human labour has been replaced in these areas with virtual assistants or fully automated online booking platforms where patients themselves can book slots with the physician of their choice. Medical records and patient histories have also been entirely digitized over the years, clearing out clerical employment entirely from the field of medicine.
Prevention.
Before we move into analyzing AI in the medical setting, let’s take a quick look at where AI helps us in day-to-day healthcare (or rather in preventing entry into the medical setting altogether). Fitbits and Apple Watches, which have been all the rage for the past few years, are a great starting point for this. These apps, through a system of tracking and pattern analysis, break down complex medical statistics and provide them to users in easily understandable, bite-sized forms. These can then be used to tweak our lifestyle around better health choices.
Another major prevention tactic produced by AI during the COVID-19 pandemic was finding emerging hotspots through contact tracing and flight traveller data. Canadian company BlueDot creates outbreak risk software that navigates exposure to infectious diseases, as seen in the pandemic. This can be further used in infection-prone areas to detect possibilities for mass epidemic outbreaks.
Diagnosis.
Moving into diagnosis, AI has two major regions of advancement: clinical decision support and imaging. Clinical decision support refers to using AI tools that help doctors and other healthcare providers determine patient diagnosis through several options. Imaging, on the other hand, refers to techniques like X-rays, MRIs, CT scans, etc. An example of a combination of this lies in a deep convolutional network called KD-CNN, created by a student in San Diego, which looks for symptoms of Kawasaki disease in patients. Another such model created by IBM named Innocens uses predictive AI in detecting sepsis in infants at an early stage with about 75% accuracy.
These tools are useful in a number of ways from the point of view of a human healthcare provider. Screening and monitoring, both essential processes within a diagnosis, require consistency and are better delivered by machines that are in no need of sleep or refueling, unlike humans. This helps humans to take on more complicated jobs, including actual diagnosis as well as selecting future treatment options. Another major benefit of such technology is in prioritizing patient care, especially in an emergency setting. Understanding and sorting which patients may be at higher risk through quick screening and probability-based diagnosis will help in ensuring that the more fatal cases are dealt with first.
Treatment.
Keyword: personalization. The range of opportunities brought by AI into the field of medicine can be summarised with that one word. Personalized medication is essentially medication that best works for the patient, determined through clinical history as well as genetic data. This could even lead to the creation of an around-the-clock virtual assistant that can provide medical recommendations and follow up with patients based on their medical history and needs. BotMD is one such AI clinical chat assistant that can help doctors improve and aid personalization in the patient experience.
AI is also useful in creating new drug designs as well as combining various drugs for different uses. Businesses like Verge Genomics concentrate on using machine learning algorithms to evaluate human data and find affordable medications to treat neurological conditions including Parkinson's, Alzheimer's, and amyotrophic lateral sclerosis (ALS).
Rapid advancements have taken place in the field of surgery as well, in terms of robotic tools that have the capability to be the surgeon’s right hand. Da Vinci Robotic Surgical System introduced one such tool, which revolutionized the field of urology and gynaecology with its imitation of a surgeon’s actions and surgical precision.
Mental health.
There are multiple facets to the application of AI in the field of mental health. This mainly focusses, however, on websites and virtual assistants, which offer support during times of distress and also in diagnosing potential mental health issues. Researchers at the University of Southern California (USC) in collaboration with Defense Advanced Research Projects Agency and the U.S. Army found that more people preferred to tell their mental health troubles to virtual bots for fear of judgement from actual humans.
However AI affects the area of healthcare, nothing can replace the warmth and comfort provided by a human doctor in their care. As Carl Jung once said, “medicine cures diseases, but only doctors can cure patients.” In such a rapidly changing world, the one thing that doctors and other healthcare professionals can do to stand their ground is to believe firmly in their knowledge, remember their ethical backing and exercise as much empathy and compassion as possible.
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