Since the advent of the COVID-19 pandemic, a serious shortfall in medical and healthcare systems has been exposed. Problems ranging from mismanagement and inaccessibility of resources, delay in deployment of measures to tackle backlogs in cases, etc., have brought the world to a standstill. In this devastating global event, there arises the need to invest in systems that can mitigate the pressures of medical emergencies like an outbreak or a pandemic and alternatively aid in the development of the healthcare sector. And to the effect of betterment, Artificial intelligence (AI) and Machine Learning (ML) can be deployed. Even though the medical and healthcare system is akin to a complex organism that lives and breathes, it is possible to create seamless and efficient solutions for already existing issues.
Transformational effect of AI on Medtech
Applications of AI in medical and healthcare systems
From the year 2019, various cracks in the system and the disparities in the dispersion of services have caused damage and loss of life. If dealt with through the use of ML, these discrepancies can alleviate the existing burden on doctors and associated professionals. Some of such uses of AI and ML are as follows:
Medical records and oversight – A study was done at Harvard University, suggests that annually, there are nearly 5 million deaths in India due to negligence and oversight. This number can be significantly reduced if helpful measures were to be implemented in time. AI systems that interpret physician’s speech and store it as detailed electronic records can be useful in eliminating human error, need to generate printed or written case files of patients, prepare easily accessible medical records, etc. Medical records and data can, in turn, be used by an AI algorithm to suggest a personalized treatment plan based on past medical history concerning factors such as dietary restrictions, types of medication, exercise, etc. Furthermore, the employment of deep learning software to study test results and scan images to provide diagnosis can help eliminate medical oversight. This will create a faster seamless process from diagnosis to recovery.
Equipment and inventory management – Creating a concise inventory of equipment and medicines can be challenging. The use of an ML system to study usage and consumption patterns and provide crucial recommendations can reduce wastage and shortages of materials. Thus, reducing overall costs incurred.
The use of ML can be further extended to determine the life of the equipment and the need for repair eliminating failure during life-saving medical procedures.
Predictive analysis – Given the sheer destructive impact of the COVID-19 pandemic on our economy and lives as a whole, a solution must be created to predict further outbreaks. Predictive modeling can use public health data, historic mapping, small outbreak reports, social media posts, scientific data to predict if an outbreak can give rise to a pandemic. Beyond mere prediction, AI can be used to recommend solutions to tackle outbreaks and steps in its effective handling. such recommendations can include plans for patient housing, effective therapies, vaccine development, etc. This will help in the management of a global medical crisis.
Challenges for AI in Medtech
The use of big data and deep learning techniques in the development of healthcare and MedTech, although promising, is still in its infancy. Such a massive undertaking and overhaul of traditional protocols of working comes with its pitfalls. For instance, even if there is a surplus of medical data available, it isn’t being stored in a standardized and easily accessible format. Given that most records follow proprietary formats it creates copyright and legal challenges as well.
Medical records are generally private. Hence the concern of cybersecurity arises. The sustainability of an AI system can be compromised if computer privacy and security attached to the systems are attacked.
Even if the technical challenges can be dealt with there is social stigma and a general lack of knowledge of AI and machine learning that can cause fear and panic in the general populace. This is a crucial linchpin in the implementation of a technology that has a varied number of stakeholders. Even if there is a lack of understanding in one such sector, it can derail the whole operation causing loss of investment and property.
The use of AI in medical and healthcare systems is just beginning. There are a multitude of issues that need to be tackled effectively if AI systems are to be employed. At this juncture, it would be prudent to acknowledge that medical technology companies and application developers have made a modest start in healthcare and monitoring. The data collected can help in providing a significant boost in deep learning and the development of technologies that will pave the way for medical security.
