The “Most likely” Approach
Anyone who has been to a doctor’s appointment and had them prescribe medications unique to them knows that medicine has always been a personal matter to some degree. Regardless, regimens and diagnostics are largely dependent on population-based averages regardless of vast human diversities. As a result, medicine somewhat remains a game of probability. This “most likely” approach to treatment leads to variations in responses due to the distinctive genetic make-ups of individuals. Hence, only one in four patients responds to cancer chemotherapy treatment, just above 50% of liver transplant patients survive for 7 years, and major depressive disorder (MDD) can only be treated by trialing different antidepressants. Where AI meets medicine, personalized healthcare emerges to solve these issues.
The development of personalized medicine attempts to address the need for a more tailored approach to treatment. This is done by directing attention towards the less than 1% of DNA that differentiates one person from another. Thus, at the intersection of medicine and AI, personalized medicine presents a revolutionary breakthrough in changing disease care as we know.
Obstacles for AI in Medicine
If personalized medicine holds so much promise, why is it not commonplace in the healthcare industry? This is due to the barriers of increasing and disorganized healthcare data, a lack of specialists, and the long and expensive road to drug development. Here, artificial intelligence (AI) meets medicine to make personalized healthcare an avenue available for organizations and to the masses. For healthcare data growth (predicted to increase by 43% in 2020), AI’s deep learning abilities makes medicine more evidence-based. Furthermore, AI can also function as assistants in a hospital or clinic settings to maximize efficiency. Lastly, AI addresses the fundamental challenges of determining drug composition and drug dosage yielding the highest effectiveness. Similarly, the viability of applying these doses through weight or by determining the maximum tolerated dose is explored.
The traditional drug development process is the farthest from being optimal. To rectify this, a feedback system control based on AI and search algorithms allowed the Ho-Systems Laboratory to use an approach named “artificial intelligence-parabolic response surface”. This pinpointed which individual drugs worked together for a patient out of billions of possible combinations. From a broad perspective, AI accelerates the drug development process and cuts costs substantially while producing more accurate results.
AI Personalized Solutions for the New Era of Medicine
The concept of personalization has dominated much of modern life; from curated ads on social media to the most inconsequential objects like mugs or stationary to suit individual needs. Where healthcare is concerned, the prospect of providing tailored treatments to acknowledge the differences between one person and the next makes perfect sense; an adolescent boy and an elderly woman would not even buy the same shoes much less receive the same medical care. To conclude, by unlearning the ‘one-size-fits-all’ stance, the future of AI and personalized medicine can propel to great heights. Wearable devices, digitized healthcare data, and superior patient history knowledge are all viable solutions in the near future to improve and save lives.