How Nanotech and AI Will Change Healthcare
Applications of AI in healthcare are many. Some methods are only ideas, and some have already been implemented. Nanotechnology itself represents the physical manipulation on the atomic level. Nanomaterials often have exciting characteristics, and they’re used for technological purposes.
You’ll often hear about nanotechnology in the context of electronics. The combination of nanotechnology and AI in healthcare leads to interesting new concepts. Nanotechnology promises to revolutionize drug delivery, diagnostics, gene therapy, and many other applications.
Nanotechnology in Medicine
A nanometer is one-billionth of a meter (0.000000001 m), which amounts to the width of three-to-five atoms. Nanoscale structures can do things that no other technology is capable of doing.
DNA Manipulation and Gene Therapy
Doctors can't cure genetic diseases. They lack the tools that can operate on individual genes. These diseases, which are a result of genetic mutations, would become easier to cure. Scientists just need to examine and work on DNA like it was at their fingertips.
Drug delivery to precise areas in a human body is challenging. Also, many drugs simply break down in the body before they can reach the target location. The proposed solution is to create medicine in the body, right on the designated spot. It sounds futuristic, but researchers at MIT have shown that it is possible.
Nanofibers are thin fibers, with a thickness under 1,000 nm. Such fibers have a special place in medicine. They are used for treating wounds and artificial implants tissue. It sounds like a simple concept, but creating custom-sized nanofibers is a challenging task.
Credits: Taking the technology for nanofibers to the next level - International Filtration News
Artificial intelligence has gotten involved in every notable industry in the world. Data science is here to help in every domain of our lives, and healthcare is not an exception!
How Can Applications of AI Enhance Nanotech?
The concept of nanotechnology is clear, but the execution can get tricky. First of all, operating at an atomic level is much more challenging than conventional operating. Laws of physics are different, and tiny mistakes can have a disastrous impact. The environment influences the accuracy of the result and introduces error rates. It’s crucial to use the right tools for the best result in a surrounding such as a fragile human body. Of course, we are talking about machine learning algorithms.
When you lose accuracy, you must turn to approximation and statistics. Artificial intelligence models can play a significant role in analyzing noisy data. Here, an algorithm needs to provide the best approximation. Data scientists work with medical scientists to incorporate artificial intelligence in healthcare.
How Can AI Help Healthcare?
Data scientists are working hard to integrate artificial intelligence in healthcare. If you’re wondering how can data science help healthcare, check out several examples below.
AI and Microscopy
The biggest issue with AFM (atomic force microscopy) is that the resulting signals are not high-quality. Atom interactions that these microscopes analyze are complex and unpredictable. Analyzing tip-to-sample interactions and the resulting signal are meant for machine learning algorithms.
The data scientist approach looks to address such problems. It’s called FR-SPM or functional recognition scanning probe microscopy. It’s a prime example of computer vision applications in healthcare. Artificial neural networks (ANNs) analyze the behavior of imaged material to simplify data. AFM works best when the tip-to-sample interactions are simple. In medical imaging, it’s never easy, but ANNs work to make the data more intelligible and take various variables into account. The flow of this process is given in the figure below:
Applications of AI are many, and it will be tough to imagine a world without AI and deep learning in healthcare a couple of years from now. The deep learning in healthcare will need plenty of data scientists, that’s for sure!
AI and Chemical Modelling
When coming down to the nanoscale, many seemingly simple problems show their complexity. Modeling atoms and molecules is challenging on the nanoscale. Generating a detailed model of a chemical system is a complex job, even for artificial intelligence in healthcare.
Data science methods can cut error rates to an acceptable margin. Machine learning algorithms have been used to determine the structural qualities of nanoelements. These elements are used in critical applications, and all calculations must be exact. Conventional measurements are not enough, and scientists often turn to machine learning.
Nanocomputing in Medicine
Nanoscale mechatronics and robotics are exciting fields. Nano-sized robots are applicable in other scenarios. Still, medicine is the most crucial target industry. Nanobots are currently around 0.1 - 10 micrometers in diameter. Hence, they’re not exactly nano-scaled.
Credits: Application of Nanorobotics (Nanobots) - AZoNano
Nanobots can navigate precisely to specific cells and deliver medicine. In cancer treatments, nanobots can be used to perform chemotherapy precisely on the cancer cells. Such delivery would decrease the negative effect of chemotherapy on healthy cells. It would improve patients’ well-being throughout and after the therapy. Of course, data scientists should create computer vision algorithms and use deep learning to achieve the accuracy of the nanorobots.
Future Outlook of Nanotech and AI in Healthcare
Here is how can data science help healthcare! The combination of nanotechnology and AI in healthcare sounds cool in practice, but many aspects are only theoretical. The discipline is often referred to as nanomedicine. We’ve already mentioned how nanobots are still a thing of the future, especially for crucial applications such as medicine.
The future looks bright, but it doesn’t mean there will be no problems with the implementation of these technologies. Research in the field of nanotechnology must prove that there won’t be negative implications on the patient’s health. Many materials and elements that are used for nanotech, like cadmium, arsenic, and lead, are harmful to humans.
In medical schools, doctors learn about the human body based on their books. In reality, each patient is different. Nanotech offers a personalized experience to everyone. Whether it’s locating a tumor, drug delivery, or organ transplant, doctors now have a new set of tools. These tools will have eyes, hands, and, with the help of machine learning, a brain! Artificial intelligence in healthcare will continue providing personalized care even after the operation. The patient’s well-being is tracked, and it can be used to improve the process for other patients.
Nanotechnology might lead to innovations in healthcare that would be unimaginable otherwise. Drug delivery, microscopy, and remote surgeries. But, nanotech would probably never become as significant if it wasn’t powered by data science.
Nanotechnology works on an atomic level. The laws of physics are different, and data that needs to be analyzed is often noisy and incomplete. For example, atomic force microscopy produces an image that is unusable. Then, data scientists filter it using computer vision methods.
Nanobots sound even more exciting. They are equipped with nanosensors and powered by artificial intelligence. These tiny machines can deliver drugs and tackle tumors without affecting the patient. Surgeons are skilled and precise, but they can’t get down to the atomic scale. In the future, we might be able to change genes and prevent genetic diseases, all with data science!