Impact of AI and ML on Healthcare Industry

The healthcare industry has undergone major changes in last two decade and that has been proved beneficial in decreasing the mortality rate. Apart from the reduction in the mortality rate, the lives of people have also improved for good. However, there are still several areas which need more technical exposure.
Such areas include risk assessment, radiology, chronic diseases, patient care and cancer.

There are ample opportunities to deploy technologies to make the health care industry more efficient, impactful and precise. AI and ML are ruling the world. One simple example of implementation of AI is Chatbot and the technology is utilized for online doctor consultation by docprime.
When it comes to automatic machines, Artificial intelligence (AI) has done wonders in overcoming time constraints, manual mistakes and helped in finding precise deformity or allocating disorder in a specific angle of a particular organ.

Machine Learning (ML) has been proved effective in healthcare industrial breakthrough because of its efficiency in clinical trials, research and development of instruments, etc. Machine learning when combined with health care yield a wide range of data which can be helpful in the analysis, prevention and treatment of individuals in a better way.

Some of the applications of AI and ML in the world of healthcare industry which may bring revolutionary changes are discussed below:

1. Radiotherapy and Radiology: In the upcoming next 20 years, there will be no radiologists as they exist now and they will be most probably replaced with supervising algorithms going through a number of studies in a minute.

University College London Hospital (UCLH) is working together with DeepMind Health of Google with aim of creating a machine which would be able to read algorithms and differentiate between healthy tissues and cancerous tissues. This will help in the improvement of radiation treatments.

This is possible by applying ML to increase the speed of the segmentation process and accuracy while planning radiotherapy. Also, no healthy structures or tissues are damaged in any manner during the process of radiotherapy.

2. Reducing the risk of antibiotic resistance: Multi-drug resistant organisms and antibiotic resistance is a new danger emerging quite fast and endangering lives of many individuals. Overuse of critical drugs promotes the growth of superbugs that hardly or do not respond to the treatments.

Electronic health record data can assist in identifying patterns of infection and patients who are more susceptible to these infections before the onset of any symptoms.
Artificial incorporated machines help in driving these analytics and can improve their accuracy and speed by providing more accurate alerts for healthcare providers.

According to a report by the Associate Chief of the Infection Control Unit at MGH, “AI tools can live up to the expectations for infection control and antibiotic resistance”.

3. Prior prediction of epidemic outbreak: Artificial Intelligence (AI) and Machine Learning (ML) technologies are also being involved in predicting the outbreak of certain epidemic and monitoring its pattern around the world.

The prediction is done on the basis of historical information available on the web, data collected from satellites, real-time social media updates and other sources.
In recent past, artificial neural networks and support vector machines have been used to predict the outbreak of malaria and later on positive cases of affected individuals. The prediction technology is also helpful in predicting average monthly rainfall in a particular area, the temperature in particular month and other beneficial data points.

Prediction of outbreak or severity of certain diseases and its effect has yet not reached in under-developed countries due to lack of educational avenues, proper access to data, medical infrastructure and reach to treatments.

ProMED-mail is a reporting program which uses the internet and provides reports on emerging diseases and upcoming outbreak, in real-time. Using this technology, some organizations like HelathMap provide alerts for any kind of epidemic outbreak in any country

4. Electronic Health record: Document classification by optical character recognition i.e. converting sketched handwriting or cursive handwriting into digitized characters; and support vector machines. Both the technologies are ML-based and essential in the advancement of accumulation and digitization of health information in electronic form. The document classification also includes sorting queries of patients on the basis email, complaints, etc.

In the field of optical character recognition, Google’s Cloud vision API and MATLAB’s ML handwriting recognitions are emerging innovations.

AI and ML are incorporated in next-generation intelligent electronic health records which help in process of clinical decisions, diagnosis and personalized treatment advice. This technology is flexible in terms of language, training data and generalizes well in different medical conditions and institutions.

5. Electronic health record into a risk calculator: Electronic health record has been proved a beneficial collection of data of patients and related history of diseases, medical conditions, surgeries, queries, etc. However, extracting the desired data from EHR and analyzing that information according to the demand is still challenged in terms of accuracy and timing. The challenge is being faced on both the ends that include developers and users.

The main issues faced in the extracting the data include a mismatch in the formats of data, incomplete records, unstructured inputs. This intervenes with predictive analysis, clinical decision support and risk stratification.

The main part is integration all the data in a single folder. However, the difficult part is getting deviated while predicting disease. For example, while a medical professional is predicting heart disease or a mental disorder and meanwhile entering into the section of billing of the heart disease, which is an entirely different arena.

Relying on MRI results can offer a more concrete dataset. However, MRI is not affordable for everyone. Hence, a data analytics enter into the section of billing to get an overview that who can pay for the diagnostic.

Although EHR analytics have come up with varieties of useful risk scoring stratification tools, especially for the field where researchers have to employ techniques to identify a relation between data which appears to be unrelated.

The main challenge revolves around making sure exactly what we need to analyze even before we enter into the black box of information and thinking of the way to predict it.
Implementing Artificial Intelligence (AI) and Machine learning (ML) for risk analysis, medical decisional support and early alerting of the epidemic outbreak have been proved to be beneficial and yet development is going on in the field.

This technical development is revolutionary and appears promising in the enhancement of infrastructure of healthcare.

By powering a new generation of systems and tools enables the medical professional to become more aware of nuances, more likely to get ahead of developing problems, more efficient while delivering care. This way AI will bring a new era of clinical quality and exciting innovations in patient care.