‘ML applications can solve most pressing problems of healthcare sector’

  • The author of this article is Sanjeev Dahiwadkar, Founder & CEO of Cognota Healthcare

Global healthcare system has gone through horrid time in the last two years amid the COVID pandemic. It is still not out of woods. However, only silver lining amid this pandemic is the rising intervention of technology in all facets of healthcare ecosystem. From telehealth to pharmaceutical research for vaccine discovery, digital technologies such as artificial intelligence (AI), machine learning (ML), data analytics have made deep inroads into medical science. Especially, use cases for ML-based algorithm have increased in recent years. The trend is underway for quite some time for now and the pandemic has accelerated it further.

Machine learning-powered product makes the healthcare device or solution smarter. Machine learning algorithms quickly process huge datasets and provide useful insights that allow superior healthcare services. That is the reason that it is rapidly catching up, to provide successful preventive and prescriptive healthcare solutions.

Diagnosis and disease identification:

The cognitive ability of ML-powered algorithm is enabling healthcare providers in disease diagnosis. This has emerged as one of the most effective areas of applications. For instance, there are types of cancer and genetic diseases that are hard to detect. However, ML could diagnose those diseases in the initial stages, which lead to greater chances of cure. Similarly, cognitive computing for genome-based diseases helps doctors to know who are vulnerable to such disease incidence and prescribe preventive care through lifestyle changes among others. Not only diagnosis of critical diseases, but routine work of pathologists in examining organic fluids of patients such as blood, urine, and also tissues can be aided by ML-powered solutions. This can lead to more automation with regard to diagnosis of samples, resulting in both cost and time saving.

Improvement in functioning of hospitals:

“Artificial intelligence and its first and second cousins, machine learning and robotic process automation, will fundamentally change how almost everyone working in hospitals and health systems will do their jobs in the future,” American Hospital Association has said. Reflection of these interventions are already visible not only in Western economies but also in India. ML processes are creating new efficiencies in areas such as hospital bed management and processing of insurance claims.

Similarly, telehealth has emerged as the biggest growth area during this pandemic. Many healthtech companies are integrating ML applications into these platforms. Leveraging ML applications, scheduling of virtual appointments, and even to match patients with doctors who have had the best outcomes for other patients are also being done. Similarly, epharmacy apps integrate ML to procure medicine for users.

Medical research and clinical trial for drug discovery:

Drug and vaccine discovery sees the most important aspect of ML applications, which is predictive analytics. It is a widely known fact that clinical trials take years to complete, with significant investments required. The chances of success are also less. Against this backdrop, ML applications offer predictive analytics to spot the best candidates for clinical trials, which are most likely to succeed. Such elimination saves a lot of time and cost for researchers apart from enhancing the chances of success. The technology also lowers the number of data-based errors and suggest the best sample sizes for testing.

Public policy making & epidemic control:

Technology has played a large role in predicting the peak of various waves of COVID pandemic in the last two years. Such forecasting has enabled government agencies to take preventive measures to lessen the spread of the virus. ML-based predictive models, therefore, provide an additional tool in the hands of the healthcare agencies to frame their policy actions in an epidemic situation. Similarly, such prediction is also helping the governments to take preventive action in case of vector-borne diseases.

Man behind the machine matters:

Despite the rising interventions of AI, ML, data analytics in the healthcare sector, it is important to note that the accuracy of any computer application depends on the human resources designing it. No application is infallible. Therefore, it is important to take utmost care to design and train a ML-based algorithm with right data sets for getting optimum outcome. In a sensitive sector like healthcare, it is also important to do backtesting before real life implementation. With right set of applications, healthcare sector can see many more solutions to its most pressing problems.

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