{"id":17214,"date":"2022-02-27T16:40:06","date_gmt":"2022-02-27T16:40:06","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/27\/artificial-intelligence-in-healthcare-tomorrow-and-today\/"},"modified":"2022-02-27T16:40:06","modified_gmt":"2022-02-27T16:40:06","slug":"artificial-intelligence-in-healthcare-tomorrow-and-today","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/27\/artificial-intelligence-in-healthcare-tomorrow-and-today\/","title":{"rendered":"Artificial Intelligence in Healthcare: Tomorrow and Today"},"content":{"rendered":"

<\/a>\n<\/p>\n

\n

It is a typically cold day in February and the peak of the flu season. Let alone the never-ending pandemic that seems to have been haunting this world forever. And it got me thinking \u2013 can technology help battle all these nasty diseases and improve patient outcomes? And most importantly, will artificial intelligence have a hand in it? It seems so.<\/p>\n

In 2021, we\u2019ve reached another milestone in Artificial Intelligence adoption \u2013 $6.9 billion<\/a> of market size and counting. By 2027, the intelligent market in healthcare will grow to 67.4 billion. Hence, the future of AI in healthcare certainly looks bright, yet not serene.<\/p>\n

Today, I\u2019ll walk you through the state of artificial intelligence in healthcare, its main application areas, and its current limitations. All these will help you build a holistic image of this technology in medical services.<\/p>\n

The State of AI in Healthcare Today<\/h2>\n

Artificial Intelligence is now considered one of the most important IT research areas, promoting industrial growth. Just like the transformation of power technology led to the Industrial Revolution, AI is heralded today as the source of breakthrough.<\/p>\n

Within the healthcare continuum, COVID-19 has accelerated investments in AI. Over half of healthcare leaders<\/a> expect artificial intelligence (AI) to drive innovation in their organizations in the coming years. At the same time, around 90% of hospitals<\/a> have AI strategies in place.<\/p>\n

Now let\u2019s have a look at the top impacts of intelligent algorithms in medicine.<\/p>\n

Current Technological Impacts in Medicine<\/h2>\n

Today, only specific settings in clinical practice have welcomed the application of artificial intelligence<\/a>.<\/p>\n

Patients have been waiting for the deployment of augmented medicine since it allows for greater autonomy and more individualized care. However, clinicians are less encouraged because augmented medicine requires fundamental shifts in clinical practice.<\/p>\n

Nevertheless, we already have enough AI use cases to assess its potential.<\/p>\n

Early Disease Detection<\/h2>\n

In most critical cases, the treatment prognosis depends on how early the disease is detected. AI-driven technology is currently used to amplify the accurate diagnosis of a disease like cancer in its earliest stages.<\/p>\n

Machine learning algorithms can also process patient data from ECG, EEG, or X-ray images to prevent the aggravation of symptoms.<\/p>\n

According to the American Cancer Society, 1 in every 2 women is misdiagnosed with cancer due to a high rate of erroneous mammography results. Hence, there is certainly an acute need for more accurate and effective disease identification. Mammograms are examined and interpreted 30 times faster<\/a> with up to 99 percent accuracy with AI, reducing the need for biopsies.<\/p>\n

Faster Drug Discovery<\/h2>\n

This year, Alphabet has launched a company that uses AI for drug discovery. It will rely on the work of DeepMind, another Alphabet unit that has pioneered the use of artificial intelligence to predict the structure of proteins.<\/p>\n

And it\u2019s not the only instance of AI-enabled clinical research.<\/p>\n

According to a Deloitte survey<\/a>, 40% of drug discovery start-ups already used AI in 2019 to monitor chemical repositories for potential drug candidates. Over 20% leverage intelligent computing to identify new drug targets. Finally, 17%<\/a> use it for computer-assisted molecular design.<\/p>\n

Healthcare Data Analytics<\/h2>\n

The healthcare data explosion<\/a> is something that has gained momentum in recent years. This sudden spike of data can be attributed to the massive digitalization of the healthcare industry and the proliferation of wearables.<\/p>\n

With a single patient accounting for around 80 megabytes of data per year in imaging and EMH data, the compound annual growth rate of data is estimated to hit 36% by 2025.<\/p>\n

Therefore, physicians need a fast and effective tool to make sense of this data flow to produce industry-changing insights. Predictive analytics is exactly one of those tools. In particular, AI-enabled data analytics<\/a> helps uncover hidden trends in the spread of illness. This allows for proactive and preventive treatment, which further improves patient outcomes.<\/p>\n

For example, the Centers for Disease Control and Prevention (CDC) implements analytics to predict<\/a> the next flu outbreak. Using historical data, they assess the severity of future flu seasons which allows them to make strategic decisions beforehand.<\/p>\n

The global pandemic wasn\u2019t an exception as well. Thus, The National Minority Quality Forum has launched its COVID-19 Index<\/a>. The latter is a predictive tool that will help leaders prepare for future waves of coronavirus.<\/p>\n

Clinical Intelligence<\/h2>\n

In the past year, labs performed over 2800 clinical trials<\/a> to test life-saving medications and vaccines for the coronavirus. However, this large clinical trial field wasn\u2019t fruitful and has generated misleading expectations. But it\u2019s old news.<\/p>\n

The $52B clinical trials market has been long suffering from ineffective preclinical investigation and planning. One of the most difficult components of running clinical research is finding patients. However, many of these clinical trials \u2013 particularly oncology trials<\/a> \u2013 have become more sophisticated, making it even more challenging to find the patients in a short window of time.<\/p>\n

Artificial intelligence holds great potential for making the selection process faster. It can amplify the patient selection by:<\/p>\n