Machine learning (ML) and artificial intelligence (AI) continuously transform the delivery of quality healthcare worldwide, and more companies are investing in these technologies.

AI investment has recorded its most significant year-on-year growth for at least two decades. Stanford's 2022 AI Index Report showed that the total private investment in AI in 2021 reached $93.5 billion, which is more than double the 2020 figures.

AI and ML drive business growth in various ways, which makes them worthy and continuous investments for companies. They accelerate business processes and innovations, allowing organizations to keep their lead in a competitive market.  

AI's benefits and breakthroughs in healthcare

AI contributes to better patient care, improved detection of illnesses, and optimized medical research in the healthcare industry.

"Machine Learning is being used in healthcare industries to predict diseases, develop medications, AI-assisted surgeries, medical imaging, and many more," says Senior AI/MLOps engineer Phani Teja Nallamothu.

"Healthcare organizations can solve big data problems by using scalable MLOps platforms and derive value which was impractical before," he adds.

The accuracy of clean data proves how AI technology reduces costs in healthcare. For example, using AI in electronic health records (EHR) improves the service provider's ability to monitor patients, leading to increased productivity. 

Before using AI, healthcare facilities kept track of patients' information using Word and Excel, but this process is labor-intensive, error-prone, and time-consuming. Such inefficiencies can result in the loss of documents, duplicate records, and incorrect diagnoses.

AI-enabled systems streamline repetitive tasks and produce more accurate diagnoses. In addition, comprehensive data eliminates the need for additional diagnostic tests. An improvement in care quality may even lead to lower rates of readmission.

The value of hiring AI/MLOps experts

Phani Teja believes that healthcare organizations can effectively implement AI/MLOps to ensure quality patient care with the help of experts like himself. "I build MLOps platforms using open-source technologies which can save organizations millions of dollars," he explains.

An open-source technology grants healthcare providers access to use, study and modify the software as they see fit. Moreover, this solves scaling issues for large organizations, with the platform supporting a large user base with minimal delay in deployment.

Centene, a healthcare technology company in Missouri, benefited from having AI/MLOps platforms in-house. "I helped them build the Centene Data Science Platform, which is currently utilized by many data scientists daily and deploys hundreds of models in production using CI/CD practices," Phani Teja shares.

By building the MLOps platform in-house, Centene saved millions of dollars in licensing fees.

Developers like Phani Teja can build an end-to-end MLOps data science platform and release it as open source so that healthcare organizations can use it for free. Implementing this technology generates cost savings, which healthcare facilities could allocate to patients and other healthcare initiatives.