Flatworld Solutions offers a gamut of services for small, medium & large organisations.
While machines and robots may not completely replace doctors and nurses in the hospital, machine/deep learning and artificial intelligence are constantly transforming the healthcare industry. This technology not only has a huge potential, but also offers a whole fleet of applications, which can be implemented in the healthcare sector. The way in which healthcare operates has seen a dramatic change over the last couple of decades, and this change is expected to continue in the years to come.
Evolving from pattern recognition and computational learning theory of artificial intelligence, ML helps in building automated models that explore and analyze the available data sets to identify hidden patterns and make useful predictions, using recognition algorithms. Today, ML is increasingly being implemented in the field of healthcare, especially in image registration, medical image processing, image-guided therapy, computer-aided diagnosis, image database retrieval, image annotation, etc. In this article, we have listed some of the key healthcare applications of ML and the ways in which ML has benefited this industry.
Technological developments are slowly taking over healthcare domain, which were initially considered to be completely human operated. The developments in the field of artificial intelligence and ML has reduced the burden of the doctors and physicians and aided them in making better diagnoses and provide the best possible treatments to their patients. Some of the key applications of machine learning in healthcare include -
Computer vision has been one of the most revolutionary inventions and has changed the way medical diagnosis is being carried out. Thanks to ML and artificial intelligence, medical image diagnosis has been one of the most active applications of machine learning in healthcare. Today, there are a lot of companies that are working on using ML to diagnose illnesses based on the images taken from various medical imaging instruments.
Drug discovery is one of the most straightforward and fast-growing applications of ML in healthcare. Several new drugs are constantly being discovered to treat various medical conditions. ML accelerates this process of drug discovery and minimizes the time required to develop new medicines. This application is beneficial to pharmaceutical companies which are under a constant pressure of outrunning their competitors, as ML helps them in quickly developing effective drugs before anybody else in the market does.
Robotic surgery is one of the bleeding-edge ML applications in healthcare and is turning out to be one of the most reliable options in this field. This technology enables doctors to perform different types of complex procedures with greater precision. Sometimes surgeries can get extremely complicated as there could be injuries in small and tight areas, which are difficult for the surgeons to access. Robotic surgery offers better visualization and greater access to such areas, making it easy for the surgeon involved. Although this technique is generally used for minimally invasive procedures, occasionally they are used in traditional open surgical procedures. ML is not directly used in such cases, however its applications, such as robotic and robot-assisted surgeries are proving to be highly beneficial in steadying the movement and motion of robotic arms, when being controlled by surgeons.
Personalized medicine, also known as precision medicine, is a procedure that distinguishes patients into different groups, where the medications and treatment plans will be tailored to an individual patient depending on their risk of disease or predicted response. As every human being is different, even two people suffering from identical illness may require different dosages of medicines. ML can be used to personalize each person's medicine dosage, based on various factors such as medical history, genetic lineage, age, weight, diet, past conditions, stress levels, etc. This application also helps patients to decide whether they should undergo certain complicated treatments, such as chemotherapy, surgery, etc., based on factors such as medical history age, etc.
Machine learning can be used in the future to recommend a treatment or medication depending on the condition of the patient. Modern, supervised ML methods are being designed, which autonomously monitor various patient parameters, such as blood glucose levels, stress levels, diet, sleep, etc. and then recommend suitable medicinal dosage. Therefore, physicians will no longer have to depend on distractible patients to precisely remember their medicinal dosage. This type of treatment automation can also detect if the patient's condition goes out of control and immediately call a doctor or a physician for further help. ML in dentistry is one of the key examples of this application.
Machine learning is being increasingly used in patient monitoring systems and in helping healthcare providers keep a track of the patient's condition in real time. The machine can identify patterns related to the patient's condition, follow-up with health status, detect improvements, and recommend treatments based on the patient's condition. Furthermore, these systems equipped with ML algorithms can call for help in case of any emergencies.
Data science is one of the most trending multi-disciplinary fields, gaining prominence in the healthcare industry. This industry generates tons of patient-related data daily. The data generated can be used by machine learning algorithms to provide an efficient digital medical assistance using an app.
ML can be used to build an application which can be used by doctors, friends, or family members of the patient to keep a track of the patient's condition. The app is a great way to keep care givers updated about the patient's treatments and medication. Using artificial intelligence and ML, this app guides the care givers to take the necessary actions in case of emergencies.
ML helps in ensuring that the patients strictly adhere to the treatment plan, by taking right medicine at the right time. Using mobile phone and face recognition technologies, the app determines whether the patient is taking the correct medicines or not. Automated ML algorithms identify the patient, advise the medication and aid in the way the prescribed medicines need to be administered. This data is then sent to a centralized database and to the clinicians who can check whether the patient is taking the medicines correctly.
Using ML, devices can be programmed to promote superior after care and healthy lifestyle among patients by providing timely guidance about the measures to be followed post treatment. The device can analyze the nature of the patients and the activities performed by them and then suggest appropriate measures to improve their health condition. Therefore, personalized care can be provided to patients and a healthy lifestyle can be promoted using the ML technology.
Machine learning is a technology which is reaching numerous domains in healthcare and is proving to be highly beneficial. Apart from these, there are few other healthcare applications where ML can be used. They are listed here -
Flatworld Solutions has been providing top-quality healthcare services to clients across the globe for over a decade now. Our rich, multi-domain experience of over 11 years has helped us understand different technologies and stay updated with the latest trends in the healthcare industry. Our cost-effective services help clients to save a considerable amount of money, which can be invested in other core activities to generate better revenues.
Our services are 100% HIPAA compliant, so you can be assured that all the services we offer are of high quality and accurate. We have a highly-experienced team of healthcare BPO specialists who understand the impact of ML in healthcare and help you in implementing top-quality ML applications for your healthcare BPO needs. Therefore, if you are looking for a highly effective, reliable, efficient, and cost-effective healthcare BPO service provider, then look no further.
Get in touch with us today!
Pricing is a critical factor to consider before outsourcing. Our pricing model allows you to keep your costs in control.
Read Case Studies to find out how we helped our clients with Healthcare BPO.
Tell us your requirements and get a free quote.