AI/ML Transforming Health-Sector

Artificial Intelligence and Machine Learning are essential takeaways of the modern world. Studies estimate that almost 38% of the total businesses under research are using artificial intelligence and machine learning algorithms to detect patterns from their vast volumes of data. It is no secret that this transformation is being, to a large extent, fueled by the powerful Machine Learning (ML) tools and techniques such as Deep Convolutional Networks, Generative Adversarial Networks (GAN), Deep Reinforcement Learning (DRL), etc.

However, traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is also one of them i.e. highly impacted by this technology and suitable for its applications. Mandatory practices such as Electronic Medical Records(EMR) have already primed healthcare systems for applying Big Data tools for next-generation data analytics. AI/ML tools are destined to add further value to this flow. They are expected to enhance the quality of automation and intelligent decision-making in primary/tertiary patient care and public healthcare systems. This could be the biggest impact of AI tools as it can potentially transform the quality of life for billions of people around the world.

Indispensable examples of the application of ML in healthcare

• Pregnancy Management: Machine Learning can be used to monitor mother and fetes health for pregnancy management and uncovering health risks and complications quickly. Use of AI and ML have proven to show lower rates of miscarriage and pregnancy-related diseases.

• Patient Data Analytics: Predictive data analytics uncover 3rd party data to discover intelligent insights and suggestive action. ML algorithms built on diagnostic data have a great potential to lower the mortality rates and increase patient satisfaction levels.

• Customized Medications: Patient data comprises of their genetic profile and medical history records assisting to create a care plan. This data acts as indispensable inputs to Artificial Intelligence models to find the best-customized treatment plans for a patient.

• Assisting radiology: These days, electronically-stored medical imaging data is plentiful and DL algorithms can be fed with this kind of dataset, to detect and discover patterns and anomalies. Machines and algorithms can interpret the imaging data much like a highly trained radiologist could — identifying suspicious spots on the skin, lesions, tumors, and brain bleeds. The usage of AI/ML tools/platforms for assisting radiologists is, therefore, primed to expand exponentially. This approach solves a critical problem in the healthcare domain because, throughout the world, well-trained radiologists are becoming hard to come by. In most circumstances, such skilled workers are under enormous strain due to the deluge of digital medical data.

• Breast Cancer: It is one of the famous and most use case of AI/ML in healthcare sector. The following Nature article describes how ML techniques are applied to perform advanced image analyses such as prostate segmentation and fusion of multiple imaging data sources (e.g. Ultrasonography, CT, and MRI). A lots of competitions for making a perfect model is held every year for this use case.ML tools are also adding significant value by augmenting the surgeon’s display with information such as cancer localization during robotic procedures and other image-guided interventions.

• Robots for Surgery: Surgical robots can provide unique assistance to human surgeons, enhancing the ability to see and navigate in a procedure, creating precise and minimally invasive incisions. causing less pain with optimal stitch geometry and wound. In the United States, the cost and difficulty of receiving proper health care, by the common public, have been a subject of long and bitter debate. AI and associated data-driven techniques are uniquely poised to tackle some of the problems, identified as the root causes — long queue, fear of unreasonable bills, the long-drawn and overly complex appointment process, not getting access to the right healthcare professional. Those same sets of problems have been plaguing traditional businesses for many decades and AI/ML techniques are already part of the solution. This is because, huge databases and intelligent search algorithms, which are a forte of AI systems, excel at such pattern matching or optimization problems. Therefore, advanced AI/ML tools and techniques must be leveraged by hospitals and public health organizations in their everyday operational aspects.

• Drug Discovery: AI and ML techniques are increasingly being chosen by big names in the pharma industry to solve the hellishly difficult problem of successful drug discovery. Some prominent examples involving Sanofi, Genentech, Pfizer. All kinds of therapeutic domains metabolic diseases, cancer treatments, immuno-oncology drugs are covered in these case-studies. Many start-up firms are also working on using AI-systems to analyze multi-channel data (research papers, patents, clinical trials, and patient records) by utilizing the latest techniques in Bayesian inference, Markov chain models, reinforcement learning, and natural language processing (NLP). Finding patterns and constructing high-dimensional representations, to be stored in the cloud and used in the drug-discovery process, are the key goals.

In today’s world, exabytes size of medical data are being generated at various healthcare institutions (public hospitals, nursing homes, doctors’ clinics, pathology labs, etc.). Unfortunately, this data is often messy and unstructured. Unlike standard transactional type business data, patient data is not particularly amenable to simple statistical modeling and analytics.

Robust and agile AI-enabled platforms, able to connect to a multitude of patient databases and to analyze a complex mixture of data types (e.g. blood pathology, genomics, radiology images, medical history) are the need of the hour. Furthermore, these systems should be able to sift through the analyses in a deep manner and discover the hidden patterns.

Additionally, they should be able to translate and visualize their finding to human-intelligible forms so that doctors and other healthcare professionals can work on their output with high confidence and complete transparency.

Firm benefitted by AI/ML transformation:

Google Health/Deep Mind:

Google Health was established in 2006 to provide Personal Health Record(PHR) services by connecting to doctors and hospitals as well as pharmacies directly. It was one of the Artificial Intelligence companies in healthcare to provide information about medical conditions, directions to hospitals, medicine reminders and fitness progress.

It used AI to assist in diagnosing cancer, predicting patient outcomes, preventing blindness etc., and the ways to explore patient-care by using tools those were used by physicians The project was discontinued in 2012 and attempts were made to reopen in 2018,November. Unfortunately, it failed to re-establish.

Due to its obscurity and lack of capabilities, Google Health had failed in it’s online PHR services. Deep Mind, an advance AI company had merged with Google Health lately. It’s a team with collaboration of scientists, engineers, machine learning experts. They have joined Google Health to solve and tackle some most complicated healthcare’s problems.

By developing AI research and mobile tools, they create a positive impact on patient and hospitals. Under the leadership of Dr.David Feinberg, they are now able to contribute towards improvement in the areas like app development, data security, cloud storage and support care.

Cure Forward Corp.

There are many innovations in cancer treatments which happen in clinical trials at present, but people may find it quite difficult to discover and leverage those options. Cure Forward is a US-based healthcare company which helps patients find trial labs which match their genetic mutation. Patients who are seeking experimental treatment can publish their DNA sequencing results on a website. Leading clinical trials can search patient forms looking for a match. Cure Forward also helps patients to retrieve more information about their disease and get support and advice from cancer-survivors.

At the core of the functionality of Cure Forward service is creating robust patient profiles. A patient has to fill in the form providing all their disease-pertinent medical records and info, such as DNA sequencing results, information on whether a patient has undergone chemotherapy or not, has been operated on or not, what stage of cancer a person is undergoing, etc. Cure Forward takes every effort to ensure a patient’s form is filled in correctly so that it provides exhaustive information to enable the highest possible matching rate. Thus there are patient guides (Patient Support Professionals and Patient Advocates) who make sure patient profiles contain all the essential data and provide assistance in the matching process.

Cure Forward was born as a healthcare startup and has matured into an established, research-driven business. The company is focused on a specific segment of the healthcare industry, and it has created a top notch quality service to satisfy its users’ demands. Cure Forward is dedicating a lot of effort to research and data analysis to increase profile matching rate to a maximum.

N-iX has been delivering software development services to Cure Forward since February 2015. To ensure solid results of our mutual work, we have been using the latest and the most appropriate tools and technologies to meet the demands of the project in the most effective way (for instance, latest Java and integration with Sales Force). Also, to make our collaboration more flexible, reliable, and transparent, Dedicated Development Team and pure Scrum Methodology are employed. N-iX has delivered a wide range of engineering expertise on this project including High Load Systems, DevOps, Cloud Solutions, UI/UX, Solution architecture, Data Science, Bioinformatics, and more.

BETA BIONICS:

How it’s using machine learning in healthcare: Beta Bionics is developing a wearable “bionic” pancreas it calls the iLet, which manages blood sugar levels around the clock in those with Type 1 diabetes.” Industry impact: The company was recently awarded an SBOR grant valued at up to $2 million by the NIH-affiliated National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

BIOSYMETRICS

How it’s using machine learning in healthcare: Via its machine learning platform Augusta, Biosymetrics “enables customers to perform automated ML and data pre-processing,” which improves accuracy and eliminates a time-consuming task that’s typically done by humans in different sectors of the healthcare realm, including biopharmaceuticals, precision medicine, technology, hospitals and health systems. Industry impact: Biosymetrics’s recently announced Strategic Advisory Board will work with company leadership team to advance healthcare and R&D innovation via machine learning and integrated analytics.

Hope this article would help in understanding the importance of AI/Ml in health sector likewise it is taking over each and every industry by somehow and other. Thanks for reading and query or suggestion is most welcome.