Artificial Intelligence in Healthcare: Today, and Tomorrow

It’s a typical February day, and flu season is in full swing. Not to mention the seemingly never-ending pandemic that has been wreaking havoc on the world. And it got me thinking: can technology aid in the fight against all of these debilitating diseases and improve patient outcomes? And, perhaps most importantly, will artificial intelligence play a role? That appears to be the case.

We’ve passed another milestone in Artificial Intelligence adoption in 2021, with market size of $6.9 billion and counting. By 2027, the intelligent healthcare market will be worth 67.4 billion dollars. As a result, the future of AI in healthcare appears bright, but not serene.

Today, I’ll take you through the current state of artificial intelligence in healthcare, as well as its main application areas and limitations. All of this will assist you in forming a comprehensive picture of this technology’s role in medical services.

The Current State of AI in Healthcare

Artificial Intelligence (AI) is now widely regarded as one of the most important areas of IT research for promoting industrial development. AI is being heralded as a source of breakthrough, just as the transformation of power technology ushered in the Industrial Revolution.

COVID-19 has accelerated AI investments across the healthcare continuum. In the coming years, more than half of healthcare executives expect artificial intelligence (AI) to drive innovation in their organizations. Simultaneously, approximately 90% of hospitals have AI strategies in place.

Let’s take a look at some of the most significant effects of intelligent algorithms in medicine.

Current Medical Technological Impacts

Artificial intelligence is currently only being used in a limited number of clinical settings.

Patients have been waiting for augmented medicine to be implemented because it allows for more autonomy and individualized care. Clinicians, on the other hand, are less optimistic because augmented medicine necessitates significant changes in clinical practice.

Nonetheless, there are enough AI use cases to assess the technology’s potential.

Early Detection of Disease

In the majority of critical cases, the prognosis for treatment is determined by how early the disease is detected. Artificial Intelligence technology is currently being used to improve the early detection of diseases such as cancer.

Patient data from ECG, EEG, or X-ray images can also be processed by machine learning algorithms to prevent symptoms from worsening.

According to the American Cancer Society, due to a high rate of erroneous mammography results, one out of every two women is misdiagnosed with cancer. As a result, there is unquestionably a pressing need for more precise and effective disease detection. Artificial intelligence examines and interprets mammograms 30 times faster and with up to 99 percent accuracy, reducing the need for biopsies.

Drug Discovery in Less Time

This year, Alphabet launched a drug discovery company that uses AI. It will rely on DeepMind’s work, which is another Alphabet unit that pioneered the use of artificial intelligence to predict protein structure.

This isn’t the only example of AI-assisted clinical research.

According to a Deloitte survey, 40% of drug discovery start-ups used artificial intelligence (AI) to monitor chemical repositories for potential drug candidates in 2019. Intelligent computing is used by more than 20% of companies to find new drug targets. Finally, it’s used for computer-assisted molecular design by 17% of respondents.

Data Analytics in Healthcare

In recent years, there has been a surge in the amount of data being collected in the healthcare industry. The massive digitalization of the healthcare industry, as well as the proliferation of wearables, are to blame for the sudden surge in data.

The compound annual growth rate of data is expected to reach 36 percent by 2025, with a single patient accounting for around 80 megabytes of data per year in imaging and EMH data.

As a result, physicians require a quick and effective tool to interpret this data flow and generate industry-changing insights. Predictive analytics is an example of such a tool. AI-enabled data analytics, in particular, aids in the discovery of hidden trends in the spread of disease. This enables proactive and preventative treatment, which improves patient outcomes even more.

The Centers for Disease Control and Prevention (CDC) uses analytics to forecast the next flu outbreak, for example. They predict the severity of future flu seasons based on historical data, allowing them to make strategic decisions ahead of time.

The global pandemic was also no exception. As a result, the COVID-19 Index has been launched by the National Minority Quality Forum. The latter is a forecasting tool that will aid leaders in preparing for future coronavirus outbreaks.

Intelligence in Clinical Practice

Over 2800 clinical trials for life-saving coronavirus medications and vaccines were conducted in the last year. However, this large clinical trial field was ineffective and led to false expectations. However, this is old news.

Preclinical investigation and planning have long been ineffective in the $52 billion clinical trials market. Finding patients is one of the most difficult aspects of conducting clinical research. However, many of these clinical trials, particularly oncology trials, have become more sophisticated, making finding patients in a short period of time even more difficult.

Artificial intelligence has a lot of promise for speeding up the selection process. It has the ability to improve patient selection by:

Unification of patients to the greatest extent possible. This could be accomplished by harmonizing large amounts of EMR and EHR data from various formats and precision levels, as well as using electronic phenotyping.
Providing clinical outcomes that are prognostic. This refers to choosing patients who are more likely to have a clinical goal that can be measured.
Identifying a group of people who will benefit from the treatment.

Personalized Treatment

As artificial intelligence enters the precision medicine landscape, it has the potential to assist organizations in a variety of ways. To begin with, personalized medicine could take the form of digital solutions that allow one-on-one interaction with specialists without having to leave the house.

According to Google Play statistics, there are currently over 53K healthcare apps. Why are they so well-liked? Patients appreciate the ease of use that healthcare apps provide. Thanks to advancements in mobile healthcare technology, patients can save money, get immediate access to tailored care, and have more control over their health.

Here are some encouraging figures to demonstrate the significance of this technological breakthrough:

The market for mobile health apps is expected to grow to $149 billion by 2028, from $47.7 billion in 2021.
As a result of the pandemic, the market grew by 14.3 percent in 2020. In addition, in the next five years, this market is expected to grow at a rate of 17-18% year over year.
The main economic benefit of mHealth apps is that they reduce hospital costs by lowering readmission rates and length of stay, as well as assisting patients with medication adherence.
Precision medicine is another facet of personalization in healthcare. It’s a cutting-edge medical service delivery model that provides individualized healthcare customization through medical solutions, treatments, practices, or products tailored to a specific group of patients. Molecular diagnostics, imaging, and analytics are some of the tools used in precision medicine.

Precision medicine, on the other hand, is impossible to achieve using traditional medical methods. Rather, it necessitates access to vast amounts of data as well as cutting-edge functionality. This information includes health records, personal devices, and family history, among other things. AI then analyzes the data and generates insights, allowing the system to learn and allowing clinicians to make better decisions.

What Are the Obstacles to AI Transformation in Healthcare?

Machine intelligence’s clinical impact has the potential to disrupt healthcare and make it more accessible and affordable. However, because of a slew of industry constraints, AI adoption is still in its early stages. Among them are the following:

One of the major roadblocks to automation is fragmented medical data. Effective data capture is further complicated by a difficult combination of unstructured and structured output. As a result, roughly 80% of all data is sent to unstructured siloed pieces scattered throughout medical systems.
The adoption of AI is also influenced by a complex web of economic and ethical considerations. There are currently no standards for AI systems in healthcare, which have doctors and patients concerned. Also, intelligent systems cannot be deployed into resource-poor settings, thus calling for significant investments.
Privacy is another limitation linked with digital transformation, Because smart algorithms consume a large amount of data, cybercriminals have a larger attack surface. Furthermore, the prevalence of sensitive data necessitates stringent security measures and adherence to federal regulations such as HIPAA.

The Last Word

Artificial intelligence in healthcare has been ripening for quite some time. Its applications are practically endless, ranging from faster drug discovery to at-home diagnostics. Due to the pandemic-induced crisis and urgent need for automation, AI has seen significant growth in 2021. We’ll see more AI revolutionizing our healthcare sector, despite the fact that it’s still in its early stages.