Insider Perspective: TechVariable Employee Edition
The Rise of Intelligent Healthcare with Machine Learning
Rapid technological advances in the last decade are the key driving force to the rise of intelligent healthcare. The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) resulted in an unprecedented way to treat diseases.?
AI and ML created an opportunity for a predictive approach to precision medicine, improved patient care delivery, better patient outcomes, and efficient patient-based process. These results come from the complex applications of AI and ML. Some crucial applications are fraud detection, targeted treatment, robot-assisted surgery, automated image diagnosis, etc.?
By 2030, a report suggests that the market cap of AI in the healthcare sector will reach up to $188 billion. But what is the reason behind the prediction? Why will there be a gigantic boost for a market cap valued at $11 billion in 2021?
The answer: Companies focusing on health tech are boosting their functions by training ML models based on practice data and knowledge for improved care delivery and outcomes.?
Machine Learning in Health Care
In health tech, developers train ML models using medical data to enhance clinicians' decision-making, automate healthcare workflow, accelerate medical research, increase operational efficiency, and improve patient outcomes.
Artificial neural networks (ANN), logistic regression, and support vector machines are healthcare's top machine learning algorithms.?
- ANN helps in text, speech, and medical image recognition;?
- Logistic regression helps diagnose and treat at-risk patients;?
- Support vector machines to help develop drugs and segment images and texts.?
Machine Learning advancements and branches like deep learning improve patient outcomes by offering previously unavailable insights. Disease prediction, visualization of biomedical data like RNA & protein structure, accurate health records, personalized treatment, medical research, and improvement of clinical trials, etc., are fundamental ML applications in healthcare technology.
The substantial work behind classifying and assessing drug compound activity and biomedical data is rising. Machine learning methods like neural networks and random forest processes aid drug compound property & activity prediction, drug-receptor interactions, and drug reaction prediction.?
According to an estimated report by the McKinsey Global Institute, US healthcare generates up to $100 billion by optimizing innovation, improving research efficiency, and developing tools for healthcare providers using Big Data analysis and Machine Learning.?
In health tech, ML algorithms help analyze vast amounts of unstructured data from Electronic Health Records (EHR). These ML algorithms discover patterns and insights that are otherwise impractical. In clinical workflows, deep learning can identify complex patterns in medical data and provide clinical decision support within electronic health records. Besides deep learning, ML helps automate medical billing, clinical decision support, and the development of clinical practice guidelines.??
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The Rise of Intelligent Healthcare?
Healthcare technologies close the gap between the patient, doctor, and intelligent healthcare. But technologies are emerging every day to offer better healthcare facilities to everyone. Top emerging technologies that are making the lives of patients and doctors easier are -?
The Internet of Medical Things is a tracker and sensor-enabled device to monitor remote patients in real time. The data gathered by these devices are processed using dedicated machine-learning applications for the following tasks:?
In predictive analysis, ML predictive models analyze complex patient data like emergency and intensive care, surgery details, etc. This analysis helps clinicians, financial analysts, and administrative personnel about forthcoming circumstances related to the patient. Clinical predictions, disease progressions, and insurance reimbursement are a few of the use cases of predictive analysis.?
3D bioprinting combines cells, biomaterials, and growth factors to print a biomedical part. The printed biomedical part aims to imitate the characteristics of natural tissues. This process involves photolithography, magnetic 3D bioprinting, Stereolithography, and Direct cell extrusion.?
Drug discovery, computer vision, medical image recognition, augmented reality, and virtual reality are some upcoming healthcare trends. By 2030, data will be accessible to every stakeholder of health tech because of strong data security and encryption technology. This accessibility will enable smooth healthcare facilities for patients in the coming days.?
Developing intelligent systems capable of human-like thinking while being devoid of human biases presents a significant challenge, and the current state of AI/ML technology indicates that it will take several years before AI/ML systems can reach the level of decision-making proficiency expected from human physicians.
The use of machine learning in healthcare facilitates faster cure and treatment development. In the future, ML will make healthcare more accessible, affordable, and equitable.
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About the author:?Nihar holds a Master of Computer Application (MCA) from Tezpur Central University. Previously a Software Engineer at TechVariable, he brought incredible knowledge of backend frameworks and Big Data tools. Nihar is now working as Product Engineer at Loyalty Juggernaut.
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