An Artificial Intelligence Approach to Health Diagnostic System

Ajay Jatav, Head - Applied AI, Webtunix Solutions Pvt. Ltd.
Ajay Jatav, Head-Applied AI, Webtunix Solutions Pvt. Ltd.
Mohali headquartered Webtunix Solutions is an emerging technology company that provides machine learning and data science services to businesses using publically available data to the web.

With the passage of time,we are adapting new technology using new channels to digest information and use data in a different manner. Therefore, marketers are now faced with more channels, huge data and great selections for customers than ever before. Artificial Intelligence is the solution of this problem. Gone are the days, when Artificial Intelligence was considered as an elective. It is precarious for organizations to find out how humans and computers can work together and become each other’s strength to create competitive advantage. In this modern world, technical revolution has taken place thus artificial intelligence and machine learning has come into existence because of its accurate predictions. However day by day it will gain more and more attention in industries. In this blog, we will discuss about machine Learning and its role in medical diagnostic system.

There are some catchy words such as big data, Artificial Intelligence, machine learning, Deep Learning and Internet of Things in the field of agriculture, healthcare, industrial and education sectors. In fact, AI is a daily routine in numerous industries. Artificial Intelligence services promote human beings in reflecting, investigating and creating innovative technologies that bring revolution in the industry sector.

No Idea about Machine Learning, Let's Discuss it First!
Machine learning is a field of Artificial Intelligence, which is allowed to software applications for making accurate results. Algorithms are built through which input is received and after statistical analysis output value is predicted. Because the algorithms are trained from dataset and thus learn from data finally improved results are predicted. Machine learning algorithms can be supervised, unsupervised and reinforcement learning.

Challenges in the Existing Diagnostic system: Errors in diagnostic is responsible for 10% of patient deaths and also 6-17% errors occur due to hospital complications. It is significant to pen down that performance of physician is normally not reason for diagnostic errors. In fact, researchers attribute the cause of diagnostics errors to a variety of factors including:
• Incompetent co-operation and combination of health information technologies (Health IT)
• Communication gap between clinicians, patients and their families
• A healthcare work system, does not effectively care the diagnostic process

Thus to overcome these errors, a new medical diagnostic system is required based upon Artifical Intelligence so that patient data should be secured. Secondly people can check themselves at home. Work load on physicians, nurses, doctors should be decreased. To address these challenges many researchers and companies are leveraging artificial intelligence to improve medical diagnostics. is health
diagnostic application, which helps you to monitor your health status, instant health checkups, one click doctor appointment and store the previous health history for

" AI in medical diagnostics is still a comparatively new method, with many clinics still needs to improve its consistency, sensitivity and how it will be practically combined into clinical practice without undermining clinical expertise"

Why Machine Learning: When Artificial Intelligence powered, disease diagnostic applications like AIVaid that everybody can download onto their mobiles became popular and the basics of medicines, medical care and healthcare are going to change its phase. It is therefore important to evaluate-

1. Whether we are observing a surge in creativities on not only AI advantages but machine learning advantages for medical diagnosis
2. What developments are motivating the deep machine learning revolution for medicine?
3. What are the main reasons for transformation from non-human intelligence to machines in medical disease diagnosis?

Machine vision is an emerging and also common thread from number of diagnostic applications, and it should be illustrate that developments in that field will relate carefully with consistent requests in diagnostics. However, the procedure of trial and error will importantly effect the value of this knowledge in the real world and the amount to which it will be applied in the field of diagnostics.

Many of today’s machine learning diagnostic applications appear to fall under the following categories:
1. Chatbots: Now a days, industry has been revolutionized into an intelligent system and automated products. All this is achieved by AI solutions and technologies. One of the great achievements by applying Deep Learning in AI enabled system is Chatbot. These are multi-purpose bots which can be used for enormous applications like in Kiosks, Automation Processes and AI-enabled systems. This conversational AI benefit produces hand in hand performance with the industry leads to customer engagement and required fulfillment. Thus it develops customer experience and intended business engagement. It is available 24*7 and patient can interact with it anytime and any where when required. Companies are using AI chatbots with speech recognition ability to classify patterns in patient symptoms to form a potential diagnosis, avoid disease and/or recommend a suitable course of action.

2.Oncology: Artificial Intelligence Companies are using deep learning to train algorithms to recognize cancerous tissue at a level comparable to trained physicians. These are favorable results; conversely the research team recognizes that further, severe testing is required before the algorithm can be joined into clinical practice. Our research did not deliver suggestion of any clinical applications at this time.

3. Pathology: Pathology is the medical specialty that is concerned with the analysis of disease based on the research laboratory analysis of biological fluids such as blood and urine, as well as tissues. Machine vision and other machine learning skills can improve the efforts conventionally left only to pathologists with microscopes.

4. Rare Diseases: Facial recognition software merged with machine learning to support clinician’s diagnoses rare diseases. Patient photos are examined using facial examination and deep learning to detect phenotypes that correlate with infrequent genetic diseases.

AI applications in medical diagnostics are in the early acceptance phase across multiple specialties with some degree of data presently available on patient consequences. These applications have the prospective to influence how clinicians and health care systems handle diagnostics and the capability for individuals to appreciate variations to their health in real-time.

In the nutshell we can say that machine learning plays an indispensible role in the medical diagnostic system. As present rapid growth in the medical device sector, enterprises making efforts to get accurate and reliable medical diagnostics based on machine and deep learning applications to marketplace may be dignified to capture a percentage of this commercial market. AI in medical diagnostics is still a comparatively new method, with many clinics still needs to improve its consistency, sensitivity and how it will be practically combined into clinical practice without undermining clinical expertise. There is lot to store for future use. is one of the perfect application for diagnostic, maintain health history, appointment booking and monitor health status automatically.