How AI Can Be Used to Tackle Diseases
- Written by NewsServices.com
Technology is rapidly changing the world as we know it for the better across all sectors. Owing to the internet, for instance, you don’t have to leave your house to shop for household items. Instead, it’s as simple as going to an online shopping site and adding items to your cart. The same also goes for entertainment: no more going to the DVD store because of on-demand streaming services. Gaming fans also don’t need to go to brick-and-mortar casinos, thanks to immersive online gaming sites like Vulkan Vegas casino.
Medical tech has also accelerated the health sector in recent years, especially in breakthrough research that has improved the quality of life. And speaking of research, hospitals and medical institutions produce more data than any other institutions around the globe, given that most of them receive hundreds of patients per day. This is the data that is usually analyzed to find medical solutions that will assist future generations.
In the past, the patient data collected would be destroyed after a couple of years, but with technology, millions of records can be stored indefinitely. With AI and machine learning, this data will no longer be gathering dust on a shelf somewhere, only to be discarded later. Instead, it can all be analyzed and used to better understand conditions in patients by establishing predictive patterns from millions of records collected over time. Read on to discover how AI and machine learning can continue transforming the world of medicine.
Prediction and Diagnosis
When it comes to disease tacking, prevention is better than cure. That’s why researchers have been working to come up with AI algorithms that can effectively predict a patient’s risk for a specific future disease. Information regarding a patient’s lifestyle, location, and general habits may be vital in predicting health issues in the future that may include heart diseases, diabetes, peptic ulcers, and so on. This may be vital in helping patients make lifestyle changes to prevent impending conditions that may arise if they continue on their current course.
AI models are fed with data, sometimes in the form of tens of thousands of scanned images from different stages of a disease, compared to tens of thousands of images from healthy patients. This has been instrumental in cancer, diabetes, and coronary heart disease diagnoses by shortening the length of time doctors would take to read result sheets. Better yet, through such an approach, urgent cases can even be given more attention.
One of the most effective methods for early detection is TBM (transport-based morphometry). TBM is used in radiology to map images and detect patterns that radiologists cannot see by simply looking at scans. For example, diseases like osteoarthritis have been caught early before they could cause total bone damage, something that would not have been possible without AI.
Personalized Treatment
Research has shown that even though patients may be suffering from the same disease, variations in their genes and environment may cause the same treatment plan to work on one and not on another patient. As a result, the risk level, prognosis, and response to medicines or treatment plans may differ by a significant margin. This is where precision medicine comes in.
With the help of AI, medical professionals can conduct genome sequencing for individuals to come up with interventions tailored specifically for that patient based on genetics. This may be the contrast between disease prevention or modification for a patient. But, of course, the data sets that inform the decision-making process must be as accurate as possible. This is why scientists are developing deep learning models to filter out ‘noise’ data from essential data and create more precise datasets.
Drug Discovery and Development
Unlike in the past, when clinical trials were a matter of trial and error, we are at a time when deep learning models can be used to provide precision and fast-track the drug discovery and approval process. Scientists can now identify drug candidates using genetic data from the word go. Building drugs from scratch to target specific patients is now possible and more accelerated.
One case study from the US company BERG led to the development of a cancer-targeting molecule called coenzyme Q10 from trillions of data from samples collected from cancer cells being allowed to grow in Petri dishes. As a result, researchers can now identify and synthesize chemical compounds that target an out-of-place molecule in a disease.
AI Is the Key to Medical Revolution
With AI, patients suffering from different diseases, especially the rare ones, will be saved from the pain of lengthy trials as doctors try to determine the cure or how to manage the disease. Disease outbreaks can also be predicted before they happen, thanks to AI. Predicting the potential of a global pandemic like Coronavirus in its early stages would save many lives, and economies will as well not be caught off-guard. The main advantage of AI systems is their ability to constantly train themselves, hence improving the quality of diagnosis or disease classification once they detect a pattern over time. As the medical sector continues integrating AI into its practices, things will keep getting better for generations to come.
