6 Best Commercial Ways of Incorporating AI in the Healthcare Industry


Artificial Intelligence, or AI, is a field of computer science where we study to infuse intelligence in machines. Many arguments are floating around the internet regarding its ethical use and the pros and cons, but we can’t deny the inevitable that AI is the future.

Though AI impacts multiple domains in research and commercial, the healthcare industry has numerous ventures available to be explored. 

What is AI in healthcare?

Healthcare involves vast amounts of data and the logical assimilation of that particular data. This is where AI comes to the rescue. Applying machine learning and cognitive technologies to analyze medical data and predict a specific outcome defines the umbrella term of artificial intelligence in healthcare. 

Though AI encompasses research and commercial segments in the medical field, this article only entails commercial aspects. Commercial aspects regarding AI forging out healthcare solutions focus mainly on the following:

  • Reducing operational delays in healthcare systems
  • Improving patient engagement with healthcare service providers

1. Automatic physical accident detection

Numerous IoT devices and fitness bands have flooded the market. These devices can be used to monitor accidental physical events and notify the partner healthcare service providers. Leveraging motion sensors embedded in these devices, we can use AI to classify whether the user has got into any physical accidents like falling from high altitude, car crashes, drowning, etc.

To start with, we need a dataset. We can collect data through volunteers and then use this to add a feedback loop to make the data ‘rich’ from real-life users. This set will contain blood pressure, oxygen level, and heartbeat information, along with dynamic real-time inputs from the accelerometer, gyroscope, etc. Combining all this information will help us build a classification model that will affirm whether there’s been an actual physical accident or a false alarm. Using the feedback mechanism mentioned above, we can fine-tune our model from time to time and collect data organically.

2. Solving mental health problems

Awareness of mental health is a topic that’s gained momentum in recent ages. However, availing of mental help is costly and demands time. 

And as we know, one of the critical uses of AI in the medical field is to find patterns in data with a valid correlation of causes. KPIs from our body- like sleep pattern, oxygen level, heartbeat rate, and blood pressure- can be continuously monitored to detect patterns similar to mental issues like depression, insomnia, etc. 

If any such anomaly is detected, it will nudge the user beforehand to bring necessary changes to their lifestyle, such as nullifying future possible mental health issues. In this case, we have to use a clustering algorithm. 

3. Automatic symptom detection

This use case is similar to the one we discussed earlier. But the model will be used explicitly for chronic disease detection at a very early stage. This will help the users to take timely pre-emptive measures to cure themselves in case of any possible detection.

4. Report generation from medical tests and checkups

We have all gone through multiple tests and checkups at least once in our lifetime. Report generation is a time-consuming process, and the delay is due to two fundamental reasons: complex reports and operational delays.

AI and healthcare can work together to curb both issues. Textual report summary generation can be automated using the latest NLG techniques, reducing the time to type a report summary manually.

5. Automated prescription generation

The pandemic accelerated the shift from traditional to digital worldwide. Services like telemedicine and teledoctor are popular alternatives being regularly availed nowadays. 

Incorporating artificial intelligence and healthcare has been crucial to this shift. We can reduce the doctor consultation time by using speech synthesis technology to convert voice descriptions into symptoms and medicine referred by the doctor. Thus, automatically generating a standard print prescription. This will remove the ambiguity of doctors’ handwriting and enable the data to be stored digitally in EHR systems.

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6. Customized fitness care

The fitness industry is a lucrative business. Fitness depends on lifestyle, food habits, and other health-related factors. Thus, it is a profoundly personal journey. This compels people to desire a personal fitness trainer to help them with their fitness goals. But, personal trainers can be costly, and not everyone can afford them.

AI has been trying to resolve this issue. Many fitness apps are available that use AI to understand user behavior, sleep cycle, food intake, metabolism, body fat content, etc., to create a customized fitness path for the user.

We can help resolve this issue using rule-based and clustering algorithms with a continuous feedback mechanism. Specific thumb rules based on situations can be suggested to the user. Additionally, a clustering algorithm can be used to create clusters of users with similar configurations in terms of fitness goals, diet, metabolism, exercise hours, food habit, alcohol consumption, smoking, sleep cycle, etc.

A continuous feedback mechanism will gather daily information on whether a user is moving up or down toward his goals in the fitness journey. The data will be processed in the backend, and the most successful fitness path will be recommended to the other users in the same cluster.

Moreover, some fitness experts will double-check the system-suggested customized approach and make changes if necessary. This whole pipeline creates a unique approach to fitness for each individual. But it’s essential to note that to create a good clustering, we need users in the platform: more users, more accurate clusters, and better suggestions of fitness paths.

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Way Forward

To make digital health accessible to the masses, more private players should be in this field. The government should prepare the infrastructure for the private players. 

One giant step initiated by GOI is the launch of the NDHM Sandbox under the Ayushman Bharat Digital Mission. It creates an entirely centralized ecosystem of digital health for private players. The cross-sharing of information between these players will increase digital healthcare penetration in the Indian market.

As an advocator of private party involvement, TechVariable has worked extensively with both AI and Healthcare. Know someone who is interested in developing an AI healthcare application, drop us a line.

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