Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Artificial Intelligence is transforming the healthcare practice. It is helping doctors provide more accurate diagnosis, make better predictions about a patient’s future health and recommend better treatment. Providing several advantages over traditional methods of analytics and in making clinical decisions.
As AI gets more sophisticated, the potential application it has on healthcare is also increasing. It is expected to continue to radically change the way the industry shapes and delivery services to patients. As per the reports of Acumen Research, the global market of AI in the healthcare industry is expected to rise to US$8 billion by the year 2026.
Here are the applications and uses of artificial intelligence in healthcare.
Diagnosis
Artificial Intelligence helps in medical decisions, increase speed and accuracy spotting anomalies in x-rays, scans and imaging as well as audio signals (e.g. smart stethoscopes).
It can be used to triage critical findings in medical imaging, flag acute abnormalities, diagnose cancer as well as assist in prioritizing life threatening cases. It has also found purpose in diagnosing cardiac arrhythmias, predict stroke outcomes, and help with the management of chronic diseases.
Treatment
Physicians now have access to AI to help improve patient treatment. Treatment plans, follow-up checks and other actions can now be tailored more effectively as AI analyze response to medicine and provide clinical decision support systems and early warning systems.
Precision Medicine through Machine Learning
Using supervised machine learning it is now possible to predict treatment protocols that are likely to succeed on a patient based on various patient attributes and the treatment context. Treatment can then be tailored to the genetic and clinical qualities of the patient which is likely to improve effectivity of treatment.
Predicting Future Disease based on Lifestyle Factors, Attributes and Medical Histories
One of AI’s main benefit is to help people stay healthy and encourage to adopt a healthier behavior and lifestyle. This also leads to AI-based systems that help predict illness and diseases caused by an individual’s lifestyle, genetic attributes, medical history and other environmental factors. Allowing medical practitioners to better manage and prescribe preventive measures.
Text Analytics and NLP
NLP and Text analytics categorise, classify, understand and extract key data from large amounts of healthcare free text including clinical notes, research, reports, patient interactions and more.
This enable extraction and labelling of relevant medical information from unstructured texts such as doctor’s notes, discharge summaries, clinical documents, and electronic health records.
RPA
Robotic process automation (RPA) digitise, process and automate repetitive processes and data collections/transformations in the medical industry usually based on rules, algorithms and workflow charts.
Through automation of redundant and time-consuming healthcare tasks, medical administrators can do more important tasks. AI-based platform can automate processes such as checking of medical claims, transfer of medical data to respective professionals and patient scheduling and more.
Robots
Robots have a wide range of uses in healthcare in including robot assisted surgeries. Robotics is implemented to assist surgeons in completing tasks that require precision, control, and flexibility such as open-heart surgeries, exceeding human capabilities.
Another application gaining popularity now are care robots that claim to deliver the benefits of human connection addressing the shortage of care givers for aging population. These bots are designed to help elderly in their daily tasks to social and emotional support. Providing efficient end of life care. Helping people to remain independent for longer, reducing the need for hospitalisation and care homes.
Patient Engagement and Care
Patients now appreciate the availability of health care information, time saving, and personalised insights AI provide. Many health care providers are taking advantage to this technology advancement to encouraging patents to participate and adhere to positive health outcomes including reminders, reports on treatment plans, automated interventions, automated monitoring of symptoms, and proactive management of a healthy lifestyle.
Health Care Analytics
Analytics improve the quality of clinical care by offering health care providers better access to patient data and allowing health systems to unlock important patterns and trends around diagnosis, treatment, and continued care. Access actionable insights for future interactions with patients and personalize the patient journey for better medical care.
Administrative Applications, Optimising Process and Cost
AI promises to improve the overall experience by anticipating patient needs in advance and accelerating the overall workflow of healthcare delivery. This is also beneficial to health care providers as AI assists in automating administrative and repetitive tasks, improve processes to save time and avoid redundancy. All providing cost-efficiency for both patients and providers.
Chatbots & Conversational AI
Chatbots resolve patient’s accessibility challenges and play a crucial role in driving a seamless and satisfactory patient experience. Providing patient information more efficiently in effect improving workflow and facilitating self-service to boost patient engagement and drive better health outcomes. Because of this, a number of healthcare organizations have incorporated chatbots in their customer engagement strategies.
