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Artificial Intelligence in Healthcare: Tomorrow and Today

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 – 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.

In 2021, we’ve reached another milestone in Artificial Intelligence adoption – $6.9 billion 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.

Today, I’ll 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.

The State of AI in Healthcare Today

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.

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

Now let’s have a look at the top impacts of intelligent algorithms in medicine.

Current Technological Impacts in Medicine

Today, only specific settings in clinical practice have welcomed the application of artificial intelligence.

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.

Nevertheless, we already have enough AI use cases to assess its potential.

Early Disease Detection

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.

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

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 with up to 99 percent accuracy with AI, reducing the need for biopsies.

Faster Drug Discovery

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.

And it’s not the only instance of AI-enabled clinical research.

According to a Deloitte survey, 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% use it for computer-assisted molecular design.

Healthcare Data Analytics

The healthcare data explosion 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.

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.

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 helps uncover hidden trends in the spread of illness. This allows for proactive and preventive treatment, which further improves patient outcomes.

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

The global pandemic wasn’t an exception as well. Thus, The National Minority Quality Forum has launched its COVID-19 Index. The latter is a predictive tool that will help leaders prepare for future waves of coronavirus.

Clinical Intelligence

In the past year, labs performed over 2800 clinical trials to test life-saving medications and vaccines for the coronavirus. However, this large clinical trial field wasn’t fruitful and has generated misleading expectations. But it’s old news.

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 – particularly oncology trials – have become more sophisticated, making it even more challenging to find the patients in a short window of time.

Artificial intelligence holds great potential for making the selection process faster. It can amplify the patient selection by:

  • Maximizing patient unification. This might be accomplished by the harmonization of huge EMR and EHR data from various formats and levels of precision, as well as the use of electronic phenotyping.
  • Providing prognostic clinical outcomes. This refers to selecting patients who are more likely to have a measurable clinical objective.
  • Predicting a population that will benefit from the treatment.

Personalized Care

As artificial intelligence enters the precision medicine landscape, it can help organizations benefit from precision medicine in multiple ways. First of all, personalized medicine may come in the form of digital solutions that allow one-to-one interaction with specialists without leaving the house.

According to statistics, there are currently over 53K healthcare apps on Google Play. Why are they so popular? Patients like the convenience that healthcare apps give. Patients can save money, get immediate access to tailored care, and have greater control over their health thanks to advancements in mobile healthcare technology.

Here are some encouraging statistics to demonstrate the importance of this tech boon:

  • The mHealth apps market stands at $47.7 billion in 2021 and is estimated to grow to $149 billion by 2028.
  • The market saw a growth of 14.3% during 2020, promoted by the pandemic. Also, this market is estimated to see year-over-year growth of 17-18% in the next five years.
  • The major economic benefit from mHealth apps lies in cutting hospital costs by decreasing readmission rates and length of stay, and by assisting with patient compliance to medication plans.

Another face of personalization in healthcare is precision medicine. It is an innovative model of medical services that offers individualized healthcare customization through medical solutions, treatments, practices, or products tailored to a subset of patients. The tools underpinning precision medicine can include molecular diagnostics, imaging, and analytics.

However, precision medicine is impossible within the traditional medical approach. Instead, it requires access to massive amounts of data coupled with cutting-edge functionality. This data includes a wide span of patient data, including health records, personal devices, and family history. AI then computes this data and generates insights, enables the system to learn, and empowers clinician decision-making.

What Hinders AI Transformation in Healthcare?

The clinical impact of machine intelligence holds great potential for disrupting healthcare, making it more accessible and affordable. However, the adoption of AI is currently at its early stages due to a great number of industry limitations. Some of them include:

  • Fragmented medical data is one of the major challenges on the way to automation. A difficult combination of unstructured and structured output further aggravates effective data capture. Thus, around 80% of all data goes to unstructured siloed pieces scattered across medical systems.
  • A complex web of economic factors and ethical considerations also impacts the speed of AI adoption. Currently, there are no standards for AI systems in healthcare, which raises concerns among doctors and patients. Also, intelligent systems cannot be deployed into resource-poor settings, thus calling for significant investments.
  • Privacy is another limitation linked with digital transformation, Since smart algorithms feed on a huge amount of data, it enlarges the attack surface for cybercriminals. Besides, the predominance of sensitive information means the need for supreme security measures and compliance with federal regulations like HIPAA.

The Final Word

Artificial intelligence in healthcare is a long-awaited disruption that has been ripening for quite a while. Its possibilities are virtually limitless and stretch from faster drug discovery to at-home diagnostics. In 2021, AI has seen significant growth due to the pandemic-induced crisis and acute need for automation. Although in its early stages, we’ll see more of AI revolutionizing our healthcare sector.

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