Humanity is almost on the verge of the 21st century’s first quarter. Humans are growing in different technological fields. However, AI/ML has emerged as the elephant in the technology room. But here, we will discuss the latest advancements of AI/ML in the field of healthcare. Healthcare has always been a consistent profit-making industry and is changing the lives of humans for the better. Hence, it describes the interest of researchers in implementing AI/ML in healthcare.
I have listed a few AI/ML applications in healthcare below: –
- To identify and diagnose the diseases
Among the most critical uses of machine learning in healthcare is the detection and treatment of illnesses and conditions traditionally deemed challenging to treat. This may range from tumors that are difficult to detect in their early phases to certain other hereditary diseases. IBM Watson Genomics is an excellent illustration of how combining cognitive computing and genome-based tumor sequencing may aid in rapid treatment.
- Discovering and manufacturing drugs
Machine learning has several critical therapeutic uses, one of which is in the initial stages of drug development. It also comprises of research and development technologies like next-generation sequencing and precision medicine, which may aid in the discovery of novel therapeutic approaches for complex illnesses. At the moment, ML approaches use unsupervised learning to detect patterns from the data before making predictions for drug discovery.
- Personalizing Medicine
At the moment, clinicians must choose from a restricted number of diagnoses or make an educated guess about the patient’s risk based on his clinical history and accessible genetic data. However, ML is making significant breakthroughs in personal medicine. We will witness an increase in the number of devices and biosensors with advanced health measuring capacity, providing greater data availability for these kinds of tasks.
- Behavioral Modification Using Machine Learning
Cognitive and behavioral alteration is a critical component of preventative care. The expansion of deep learning in healthcare has resulted in the emergence of countless companies in cancer prevention and therapeutic detection interventions. Somatix is a business-to-business-to-consumer data analytics startup that just created an app that uses machine learning to detect the motions we perform throughout our everyday lives, enabling us to better influence our subconscious behavior by making the required changes.
- Clinical Trials and Research
Clinical studies, as anyone in the pharmaceutical industry will tell you, are time and effort intensive, and can take years to complete in many cases. By using machine learning-based statistical predictive models to find possible clinical trial applicants, researchers may create a group of applicants from various data sources, such as prior medical visits and social networks. Additionally, ML has been used to provide real-time tracking and data access for study subjects, determine the optimal sample size for testing, and use the potential of electronic records to eliminate data-related mistakes.
- Collecting Data Through Crowdsourcing
It enables engineers to access massive amounts of data supplied by individuals with their agreement. This real-time healthcare information has significant implications for how medicine is regarded in the future. Apple’s ResearchKit platform enables users to connect with interactive applications that employ machine learning-based facial recognition software to treat Asperger’s and Parkinson’s illnesses. IBM has teamed with Medtronic to understand, collect, and make diabetic and insulin data more accessible in real-time using crowdsourcing. Also, with improvements in IoT, the healthcare sector is still exploring new methods to utilize this data to handle difficult-to-diagnose situations and lead to significant diagnostic and treatment development.
An effective application for ML is indeed bot technology because it significantly simplifies the treatment process. The AI assistant is handy whenever a patient needs immediate guidance or when he can’t reach a specialist.
Artificial neural networks might analyze all of this data generated today from IOT devices, sensors, etc., and draw inferences about global pandemic breakouts in the next 10 years. Deadly illnesses might be snipped in infancy before they wreak widespread devastation. Additionally, artificial intelligence is also heavily used in food safety, assisting farmers in preventing pandemic illness.
