AI use cases are expanding and evolving in healthcare

• The world is increasingly seeing tangible impacts of AI across various industries, including healthcare. 
• Using AI, healthcare providers can analyze and interpret available patient data more precisely for early diagnosis and better treatment.
• As interest in AI in healthcare grows, more current applications and use cases will soon emerge 

Artificial intelligence (AI) is getting increasingly sophisticated at doing what humans do — but more efficiently, more quickly, and at a lower cost.

The potential for AI in healthcare is vast, and PwC estimates the global market for AI healthcare applications will erupt from US$663.8 million in 2014 to US$6.7 billion in 2021. This increased demand correlates with a substantial rise in the complexity and abundance of data.

There are myriad use cases for AI in the healthcare industry and it is often structured around typical processes that are used in the healthcare industry.

Let’s take a look at how AI is helping key stakeholders like hospitals, diagnostic labs, and pharmaceutical companies in various ways.

Healthcare “Data Mining” to predict diseases

In an era of technological ubiquity, data fuels innovation.

Data mining is being deployed to find insights and patterns from large databases.

The healthcare industry captures large volumes of patient records and with appropriate analysis of this data. Currently, the sector employs data mining to develop early detection systems by using clinical and diagnosis data.

Using machine learning tools, the healthcare sector can address a plethora of diseases prior to their occurrence. 

Tech giants, such as Google and IBM are using AI to unearth patient data which are structured and unstructured. The data is extracted by mining medical records or by deciphering physician-patient interactions (voice and non-voice-based interactions).

AI in medical imaging and diagnostics

According to Minds Field Global’s report, AI has expanded substantially in the fields of medical imaging and diagnostics over the past couple of years, thereby enabling medical researchers and doctors to deliver flawless clinical practice.

Paving the way for quantification and standardization, deep learning is aiding in the prevention of errors in diagnostics and improving the test outcome, the report said.

“Furthermore, AI is improving the assessment in medical imaging to detect cases such as malignancy and Diabetic Retinopathy (DR). It is also assisting with quantifying blood flow and providing visualization,” it added.

AI in the emergency room and surgery

The Da Vinci Surgery System was the first surgical robot that was approved by the FDA for general laparoscopic surgery 15 years ago.

Since then, many other surgical robots have been introduced. Including the current generation of robots that are integrating AI in surgery, the next generation will be powered by machine learning.

In the near future, we may witness AI platforms such as DeepMind, IBM Watson, and other advanced AI tools enabling physicians and hospitals to deliver promising surgical interventions.

Currently, IBM Watson has advanced medical cognitive and NLP capabilities to respond to queries by surgeons.

Furthermore, similar AI platforms aid in monitoring blood in real-time, detect physiological response to pain, and provide navigation support in arthroscopy and open surgery.

AI in pharma

Inevitably, AI is revolutionizing the way pharmaceutical companies develop medicines. In fact, AI and ML have been playing a critical role in the industry and consumer healthcare business.

The McKinsey Global Institute estimates that AI and machine learning in the pharmaceutical industry could generate nearly US$100 billion annually across the US healthcare system.  

From augmented intelligence applications such as disease identification and diagnosis, helping identify patients for clinical trials, drug manufacturing, and predictive forecasting, these technologies have proven critical to the sector.

Top pharmaceutical companies, including Roche, Pfizer, Merck, AstraZeneca, GSK, Sanofi, AbbVie, Bristol-Myers Squibb, and Johnson & Johnson have already collaborated with or acquired AI technologies. 

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