Can AI (LLMs) help create designer medicine to treat any disease?
Introduction to AI in Healthcare
It is no surprise that AI has transformed lives and integrated well as a part of humanity. This statement will become truer in the future especially with the rise of Large Language Models (LLMs) and the technologies it will bring. However, what is often under-looked is the role of AI in healthcare.
The role of AI in saving lives is paramount. Some of the few examples of how AI is being used in the following:
- Detecting diseases such as cancer better than humans
- AI image recognition software can help analyse CT scans and MRIs better than humans. This can lead to earlier and accurate diagnosis of diseases such as tumour.
- Robotic surgery
- AI powered robotic equipment can help perform intricate procedures with greater precision.
- Telemedicine
- Telemedicine is the use of telecommunications in medicine. For example, remote consultations can provide convenient healthcare to patients without the need to visit a medical centre.
What is Precision Medicine
Designer medicine otherwise known as precision medicine is a new approach of tailoring medicine based on biological data such as the genetic composition of the patient. While traditional medicine tends to be more general, precision medicine can be uniquely targeted to individuals. For example, one advantage is that doctors can now choose medicine that reduce adverse side effects while curing the patient. At present time, one example is the use of precision medicine in monoclonal antibodies, in which this treatment uses precision medicine to target and block cancer cell growth.
How LLMs Can Work in Analysing Patient Data for Custom Treatments
The ability of Large Language Models to comprehend human written text opens up limitless possibilities in healthcare. Below are the few examples of how LLMs.
- Sifting through medical notes
- LLMs can be used to sift through vast amounts of doctor notes and latest research findings to come up with the best solutions and latest research findings for patient medical treatments. This would speed up the time to assist a doctor's diagnosis .
- Monitoring Wearable Device Data
- LLMs can assist in analysing data from the patient's wearable devices such as the technologies available in the Apple Watch. This can make possible a more efficient and accurate diagnosis.
- Predictive analysis
- By analysing historical patient data, LLMs can be used to predict future diseases and suggest possible treatments that may have been overlooked by human physicians.
How can AI help in drug discovery and precision treatment
There are a few ways how AI can help in drug discovery and precision treatment, below are the following:
- Analysing vast quantities of biological data for predictive modelling
- AI can help speed up and identify biological data such as genetic data using predictive modelling techniques. This can help target drugs for specific proteins or genes uniquely tailored for a person's biological composition to treat diseases with little side effects.
- Drug design
- Generative AI can help model and generate a near infinite number of new designs of drugs for precision treatment.
- Repurposing existing drugs for different diseases
- AI can help repurpose by mining data on existing drugs such as in scientific literatures, genetic data and more to repurpose it for new applications.
Conclusion
In conclusion, it is no doubt that with the rise of Large Language Models, the state of healthcare will leap into a stage in which no one can predict. However, the question remains, as ethics and regulation come into play, will we be responsible enough to used in the most correct way possible for the betterment of humanity. Only time will tell.
Below are the books I used to learn on precision medicine. If you don't have the time to read a 300 page book, you can aggregate the content using my new AI Book Summarizer/Console web app.
- Precision Medicine: A Primer by Robert C. Green, Mark J. Daly
- Precision Medicine: The Future of Healthcare by Eric Topol
