Disruptive Applications of Artificial Intelligence in Healthcare, Finance and Media

According to Gartner, the number of businesses using Artificial Intelligence (AI) increased by 270% from 2015 to 2019. Moreover, the market size is expected to reach $267 billion for this highly disruptive technology. 

This massive growth stems from AI’s standing as the most disruptive technology amid digital transformation. AI technology is rapidly evolving, as are its uses and advantages in an abundance of spaces and sectors. Already, there are a myriad of applications of artificial intelligence today; any organization leveraging AI separates itself from the pack and often catalyzes a drastic shift in their given industry. 

Innovation is occurring rapidly everywhere you look. Through the examples of industries such as healthcare, finance, and media, it’s evident that AI disruption is the present instead of the future.  

AI Disruption in Healthcare

John Gikopolous is the Global Head of AI and Automation at Infosys, and he points out several ways the healthcare industry is being disrupted:

Intelligent Process Automation

With intelligent process automation (IPA) comes the following benefits for companies paying for an administered healthcare service (also known as payers):

  • Collecting information
  • Registering people 
  • Allowing one-point access to past information 

As a general tool, IPA handles non-routine tasks and processes that necessitate judgment and problem-solving. Thus, employees have time to work on more useful work.

Providers can automate the entire hospital experience, and pharma companies can do the same for their functions, including manufacturing and support. In fact, it can even be critical to clinical trials and research and development. 

IPA hurdles over the human interface while sidestepping critical errors and streamlining the value chain. 

Finding a Centralized Data Source

Another AI-based disruption in healthcare mentioned by Gikopolous has to do with standardized data. 

Moving over to a central informational source ensures that every decision can be made with the utmost attention to detail. 

Matters such as the active agents used in drugs and when to dispense them can be more precise and informed. Stay times for patients at hospitals can be calculated appropriately. Furthermore, healthcare professionals can further grasp the impact they have on the public healthcare systems. 

With this information standardized, AI can come in and further streamline the processes by identifying the questions immersed deeply in the data. 

Leveraging Machine Learning

Machine learning is currently analyzing industry data for payer/patient or hospital/patient interfaces. In short, this AI tool automates the analytical model building. It takes the numbers gathered and identifies patterns, making decisions without much human involvement.

Generally, patients provide the data. Machine learning is then used to give professionals critical insights to help inform treatments. It creates a more 3-dimensional patient experience that can weigh factors such as:

  • Underlying conditions
  • Demographic 
  • Social category

This approach helps healthcare providers look at the full picture instead of being stifled by a more one-dimensional patient/doctor journey. 

Current Use Cases

Recently, Google Health innovated an AI solution to identify breast cancer. The algorithm actually out-performed human radiologists by 11.5%. 

Within Google’s parent company Alphabet is a life sciences arm known as Verily, which has started a data-collecting initiative called the Baseline Study.  Using some of Google’s search button algorithms, the project’s leaders intend to analyze what makes people healthy. 

The above initiative entails trials with disease monitoring technology (e.g., a digital contact lens that tracks blood sugar levels). 

Furthermore, IBM’s Watson Health has bolstered the Mayo Clinic’s breast cancer clinical trial. It’s also helped Biorasi bring drugs to the market faster while slashing costs by over 50%.

InterKnowlogy has also been driving new innovations in the space, helping researchers leverage AI and Machine Learning to detect COVID-19 in chest X-Rays, as well as identifying other medical conditions in ultrasound images using Microsoft Custom Vision.

AI Disruption in Finance

Industry experts believe that AI is at the forefront of the financial services industry, and there are a few ways this disruption is taking form:

Informing Big Picture Decisions

The amount of consumer data that banks possess is seemingly endless insights that can inform their decisions. AI-based data science and visualization methods can help pinpoint trends and behaviors to inform big-picture decisions. 

Offsetting Customer Churn

Almost all industries are familiar with customer churn, but financial services companies feel the impact seemingly more than other spaces. The predictive analytics provided by AI allows these companies to be more proactive with customers who display behaviors suggesting they’ll leave. This results in offering people something enticing to keep them on board. 

Personalizing the Consumer Experience

Machine learning has become an integral facet of personalized customer experiences on financial websites and apps. Data is leveraged to make user-specific recommendations that speak to a given client’s needs. 

There are Robo-Advisors making investment suggestions custom-fit for the user’s circumstances throughout an array of investment portals. 

Projections for AI in Financial Services

Really, the above information only scratches the surface of how much AI is being leveraged in financing. 

A recent survey called Transforming Paradigms: A Global AI in Financial Services Survey puts this shift into perspective. 85% of the respondents are currently using some form of AI. Moreover, 50% predict a legitimate competitive threat from tech companies leveraging this tech to enter the financial space.

AI Disruption in Media

In the media and entertainment industry, AI solutions offer a triumphant path for optimizing the user experience.

The data science and AI being leveraged throughout the industry has led to the following advantages:

  • Catering to customers on a granular level
  • Executing and delivering resonant, personalized content, user experience, and storytelling
  • Establishing a revolutionary, innovative, and sustainable business model

Here’s how the AI disruption is playing out in this industry:

Metadata Tagging

According to Lorenzo Zanni, the leading research analyst at IABM, metadata tagging is one of the most successful AI use cases in the media industry. 

Metadata tagging involves image recognition, speech-to-text transcription, and other similarly impressive techniques. The AI algorithm automatically creates metadata that helps direct monetization strategies.

Making Accurate Predictions

AI tools focused on predictive analyses (e.g., machine learning) help with accurately adjusting resources to demand. This tech plays a critical role in on-demand cloud models. 

Such analysis can also predict content supply chain disruptions (e.g., missed deadlines by a content supplier). As such, media companies can use these tools to save a significant amount of money with more insightful budget planning. 

What’s the Next AI Disruption in Media?

Zanni points out that deep learning algorithms are the future of AI for media companies. 

In short, deep learning technology is a facet of machine learning. It involves algorithms inspired by a brain function called the artificial neural network.

These algorithms guarantee the most accurate results—but the caveat is that millions of observations are required to generate such an outcome. Thus, media companies must commit to technologies that gather data at scale.

Right now, only industry giants such as Netflix have moved over to the cloud and taken a data-first approach. 

Conclusion: AI Will Impact All Industries

AI disruptions in healthcare, finance, and media are only a microcosm for the vast transformation in the professional world. 

Evident across the three industries discussed is that they’re using many of the same tools. However, they all have multiple uses, often based on predictive capabilities and analysis. Furthermore, AI is used to make operations more streamlined and efficient than ever before. 

Those who are already leveraging these tools are getting in on the ground floor and reaping the most benefits. Speaking to that notion is how AI might increase profitability rates by an average of 38% by 2035. This would result in an economic boost of US$14 trillion across 16 industries in 12 economies by the same year. 

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