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6 Applications of Machine Learning in the Pharmaceutical and Healthcare Industry

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6 Applications of Machine Learning in the Pharmaceutical and Healthcare Industry

Today, when the population is rapidly rising and the world is trying to recover from the damage caused by the pandemic, internet-enabled devices are keeping the world in order. We saw how technology, such as machine learning and AI, helped doctors and the healthcare staff to provide timely and affordable treatment to hundreds of people during the heights of the pandemic. Machine learning applications allow the healthcare industry to allocate resources judiciously, monitor the patients’ vital stats 24/7 and reduce operational costs. Recently Google developed a machine learning application that locates cancerous tumors in mammograms. AI in healthcare is a boon as it facilitates remote medical treatments and allows patients living in tier 2 and 3 cities to get the best treatment. 

 

How machine learning and AI are transforming the healthcare and pharmaceutical industry.

 

1. ML and AI help to detect and diagnose diseases.

Several life-threatening diseases, such as cancers, heart ailments, and other genetic disorders, can be quickly diagnosed if detected earlier. ML and AI web applications detect the first signs of such diseases. For example, IBM Watson Genomics uses genome-based tumor sequencing and cognitive computing to make fast diagnoses. P1vital PReDict (Predicting Response to Depression Treatment) provides an affordable way to treat routine clinical conditions.

 

2. ML and AI help in the drug development process.

ML and AI applications have numerous clinical applications such as:

  • Identifying which drug is more suitable for a particular person based on their vital stats and genetic structure.
  • Researching and developing technologies that can find alternative paths for treating multifactorial diseases.
  • Identifying patterns in data to make accurate forecasts regarding the growth and spread of disease.

 

3. Developing personalized treatment.

Today, patients get treatment based on the data stored in the hospital’s CRM software, symptomatic history, and genetic information. This limits the types of diagnoses that can be administered to the patient. Recent research in ML and AI allows doctors to provide personalized treatments by combining predictive analytics and individual health. IBM Watson is leading this field by generating multiple treatment options by leveraging a patient’s medical history.

 

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4. ML-based behavioral modification.

Behavioral modification is an essential component of preventive medicine. Since ML and AI applications showed positive results in this field, numerous startups have cropped up that are trying to:

  • Identify and prevent cancer 
  • Maintain smart health records
  • Predict outbreaks of epidemics and pandemics
  • Find microscopic deformities in the scanned images, such as CT scans and X-rays.

For example, Somatix has developed an ML-based application that identifies the gestures a person makes throughout the day. This helps understand the patient’s unconscious behavior and make the required changes.

 

5. ML-based smart health records

Today’s data technology, which includes ERP and CRM systems, has played a significant role in collecting, organizing, and deriving useful insights from factual data. But there exist numerous pain points that require more time, effort, and resources to complete. MATLAB’s ML-based handwriting identification technology and Google’s Cloud Vision API use machine learning-based OCR recognition techniques and document classification methods using vector machines. 

 

6. ML and AI help conduct clinical trials and research.

Clinical trials take several years to pass through all the necessary stages mandated by international healthcare standards. Also, most researches are too expensive and can lead to the loss of hundreds of millions of dollars if a step-by-step methodology isn’t followed. To avoid such unforeseen circumstances, it’s essential to use ML-based applications that create various virtual scenarios to distinguish the favorable from the unfavorable ones. The other uses of ML and AI in this field are as follows:

  • Selecting the potential candidates based on numerous data points such as social media, previous doctor visits, and genome sequence.
  • Real-time monitoring of trial participants.
  • Analyzing the right sample size that can be tested for a particular trial.
  • Reducing/eliminating data-based errors by making use of electronic records. 

 

Conclusion

It’s high time that the healthcare industry begins implementing ML and AI-based applications. These applications will speed up operations, cut down operational costs, and allow allocating resources judiciously. Healthcare institutes will be able to take initiatives quickly and cheaply. With all these benefits, revamping operations using ML-based applications makes sense.

 

Author Bio:

Nishant likes to read and write on technologies that form the bedrock of modern-day and age like Web Apps, machine learning, data science, AI, and robotics. His expertise in content marketing has helped grow countless business opportunities. Nishant works for Sage Software Solutions Pvt. Ltd., a leading provider of CRM and ERP software for Pharmaceutical Industry to small and mid-sized businesses in India.

 

 

 

Read more Technology related stories in The Weekly Trends magazine.

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