Machine learning is the next step of artificial intelligence (AI). On the one hand, AI focuses on giving machines the smart intellect that can make them react and work like humans. Whereas on the other hand, machine learning believes in providing intelligent systems the power to access data and learn from it. So, together these technologies can bring drastic transformation in any sector. Hence, healthcare industry too cannot refrain from the transformative touch of technological advancement.
With latest developments brought by the application of machine learning and AI, the healthcare industry is continuously evolving. Technological assistance is helping healthcare providers and medical facility centers improve quality of care for the aging population. From data collection to its storage, the role of AI in has been quite noticeable in the medical sector.
With the increase in healthcare data, more and more researchers are coming forward to explore the advantages of machine learning. Even the physicians are training themselves to be able to leverage new technology in their practice.
With deep learning coming into use in the diagnosis of a patient, the process of detecting health issues in an individual is getting faster and accurate. The credit for which goes to the development of deep neural networks that gives computers the power to think and understand the way we humans do. This system works on the principle of probability concluded out of the data fed from HER into them so that like humans they too can make decisions or predict.
Like the way a physician learns from his past patient cases and uses that understanding to diagnose in the present. Similarly, the neural network uses pre-filled algorithms to learn from and use that information to diagnose a patient.
Here are some machine learning applications that are already in use the field of medical diagnosis.
Diagnosis of Rare Diseases
There are various facial recognition software and systems available that uses machine learning to diagnose rare diseases in humans. Using these latest technological advancements, clinicians can make efficient conclusions. They need to implement facial analysis and deep learning techniques to find out the symptoms and correlate it with rare diseases.
Use of Chatbots
Chatbots are revolutionizing the overall customer service experience. It gives the visitor a feeling that they are speaking to a real human and not a program. These are computer programs that run on artificial intelligence. They simulate a human conversation naturally in the form of spoken or written language.
AI supported Chatbots uses speech recognition capability to understand patterns in a patient’s speech and identify their health symptoms. Later the system uses this data against a database of diseases and compares it to diagnose the disease. It also makes necessary recommendations for the proper treatment of the patient. For example, the app will recommend a patient with flu-like symptoms for over-the-counter medicine.
Modern medicine and advanced technologies have transformed the way patients receive care and treatment. With technological intervention, now specialists can peer into the human body with the help of diagnostic tools. One such innovative tool is radiological imaging in which radiologists make use of medical techniques such as X-ray, MRI, etc. to diagnose and treat diseases.
Before AI coming into the field of radiology, it was the responsibility of the radiologist to make radiological interpretations of the medical images. But with such diagnosis there remained a chance of manual errors. However, the application of deep learning to radiology brought tremendous change in the radiological operation. Now, with deep learning algorithms, specialists can extract valuable information from the images to come to a faster conclusion.
In the recent years, the implementation of AI and deep learning technologies are in full swing. From controlling image quality to dynamic image creation and analysis, the application of deep learning algorithms is widespread in the radiological diagnosis of diseases.
For instance, to sort out which patient needs immediate attention, radiologists make use of deep learning. This technology leverages algorithms to filter all the incoming images and identify the patient with a severe issue. If the patient has a brain hemorrhage or a stroke symptom, then he will top the priority list. His case will be attained first than the others with a less critical issue.
Several developments have already started doing rounds in the surgery space. Thanks to the machine learning capabilities that are adding a lot of innovation in the way doctors are performing surgeries. Based on latest changes, we can soon expect robots to replace humans in the operation theatre.
The da Vinci surgical robot is a recent advancement in the field. By manipulating the dexterous robotic limbs of da Vinci surgical system, surgeons can perform surgeries with precision and even within a small space without many tremors. This robot has a magnified 3D vision and twistable tiny hands that can rotate to adjust accordingly. The same surgery when done by a human hand takes more time and needs more incisions. Surgeons can completely control this robotic system to perform operations. Surgeons use da Vinci mainly for cardiac valve repair, prostatectomies, and gynaecological surgeries.
Moreover, machine learning is making it possible for surgeons to analyze burn depths and predict healing time in a patient with burn injuries. Using algorithms, predicting the percentage of burn in a body has become more convenient than before. Such critical information can help with valuable insights essential for immediate surgical planning of emergency patients. Hence, it won’t be wise to say that cooperation between human and technology can elevate surgery efficiencies that wouldn’t have been otherwise possible.
4. Personalized Medicine
Earlier, the healthcare providers worked towards finding generalized solutions that will benefit the population as a whole. Treatments were mostly symptom-specific rather than being patient-specific. People will similar symptoms were treated in the same way and were provided with the similar medicine.
But as technology evolved, medical methods along with diagnostic tools and medicines underwent a massive transformation. Now, physicians no longer prescribe generic medication to all with the same disease. Even though the condition may be generic but patients are not identical. Every individual has different needs and health structure. Hence, today patients receive personalized care based on their specific problem. And this has been possible only with the intervention of AI into the medicine space.
Using algorithms AI can interpret, transforms and evaluate patient data to draw insights and accordingly come up with treatment recommendations for a patient. Such detailed understanding helps a doctor to prescribe personalized medicine according to the individual need of every patient for a faster cure.