Anton Chekov practiced medicine in a time when diseases are just phenotypes. Today, the healthcare system has progressed significantly, and big data has the biggest impact on it. Data-driven thinking and methods have now opened the doors for personalized healthcare systems.

 Tailored experiences have become the standard in today’s environment. From the music we listen to the TV episodes we watch to the purchases we make, these are frequent suggestions based on data acquired about us, such as our purchasing and streaming histories.

The goal of a personalized healthcare system is to enable patients to have a personalized assessment of their health including a list of lifestyles change recommendations based on predicted health risks.

We haven’t completely reached the realm of personalized medicine yet, but it’s a few years ahead.AI powered Big data analytic tools will be the driving force for achieving this. IDC research predicts the global data sphere will grow to 175 zettabytes by 2025. Medical practices leverage the power of machine learning techniques to identify trends, patterns, and anomalies in the data to help experts make better diagnoses and treatment decisions. Complete personalized healthcare will only be possible when clinicians can understand which gene mutations or differences may cause specific illnesses (Since each one of us has a unique variation of the human genome)

To accomplish the comprehensive perspective of each patient, we must collect data from health records and lifestyle habits from many sources regularly and combine it into a unified perspective. This data is critical for understanding and analyzing each patient’s demands, which may be used to guide both medication development and the sort of treatment that a patient receives.

AI and machine learning help pharmaceutical companies gather, store, and analyze massive data sets easier than traditional procedures. It enables quick research based on genetic variation data from different patients and develops personalized medicines.

In a NutShell

In an ideal world, we would like to eliminate diseases. We may introduce preventative measures and therapies far earlier, sometimes even before a patient begins to exhibit symptoms, if we have a greater understanding of why, how, and in which people’s illnesses develop. Personalized medicine has the potential to enhance and possibly save many people’s lives, and artificial intelligence and machine learning will be the driving forces behind future discoveries.

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A number of research studies have already found that AI can perform as well as or better than humans at key healthcare tasks such as disease diagnosis. Algorithms are already outperforming radiologists in detecting malignant tumors and advising researchers on how to build cohorts for expensive clinical trials. However, we believe that it will be many years before AI replaces humans in broad medical process domains for a variety of reasons. In this article, we discuss the potential for AI to automate aspects of care as well as some of the barriers to rapid AI implementation in healthcare.

Artificial intelligence is not one technology, but rather a collection of them. Most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support vary widely. Some particular AI technologies of high importance to healthcare are defined and described below.

Machine learning has the potential to provide physicians and hospital staff with data-driven clinical decision support (CDS), paving the way for increased revenue. Deep learning, a subset of AI designed to identify patterns, employs algorithms and data to provide healthcare providers with automated insights.

Examples of AI in Medicine and Healthcare

AI can improve healthcare by promoting preventative medicine and the discovery of new drugs. IBM Watson’s ability to pinpoint treatments for cancer patients is one example of how AI is influencing healthcare, as is Google Cloud’s Healthcare app, which makes it easier for health organizations to collect, store, and access data.

Researchers at the University of North Carolina Lineberger Comprehensive Cancer Center used IBM Watson’s Genomic product to identify specific treatments for over 1,000 patients, according to Insider Intelligence. The product used big data analysis to determine treatment options for patients with tumors that had genetic abnormalities.

Google’s Cloud Healthcare application programming interface (API) includes CDS offerings as well as other AI solutions that assist physicians in making more informed clinical decisions about patients. AI in Google Cloud uses machine learning to extract insights from users’ electronic health records, allowing healthcare providers to make better clinical decisions.

Google teamed up with the Universities of California, Stanford, and Chicago to develop an AI system that predicts the outcomes of hospital visits. This helps to reduce readmissions and the length of time patients spend in hospitals.

Benefits of AI in Healthcare & Medicine

Integrating AI into the healthcare ecosystem provides numerous benefits, including the ability to automate tasks and analyze large patient data sets to provide better healthcare faster and at a lower cost.

Administrative tasks account for 30% of healthcare costs, according to Insider Intelligence. Some of these tasks, such as pre-authorizing insurance, following up on unpaid bills, and maintaining records, can be automated by AI to reduce the workload of healthcare professionals and ultimately save them money.

AI has the ability to analyze large data sets, resulting in patient insights and predictive analysis. Obtaining patient insights quickly assists the healthcare ecosystem in identifying key areas of patient care that require improvement.

AI is also used in wearable healthcare technology to better serve patients. AI-powered software, such as FitBits and smartwatches, can analyze data and alert users and their healthcare providers to potential health issues and risks. The ability to assess one’s own health through technology reduces professionals’ workload and prevents unnecessary hospital visits or remissions.

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