Applied Artificial Intelligence in Healthcare Diagnosis


If you follow the emergence of Artificial Intelligence in your daily routine, its applications in the field of healthcare should not stun you. If technology today drives cars as well as comprehends human language, it is all due to AI. In fact, machine-human conversation without human intervention is a remarkable feature of AI. Its applications in healthcare diagnosis are extraordinary too. Artificial intelligence companies in India are working towards the advancements in healthcare in terms of tensorflow development.

Since it is of absolute importance that doctors spend their time to diagnose a patient. But, due to certain paperwork and data entries, the time spent with the patient is quite less. Once AI gets into the game, doctors can avail most of the time to attend to patients. It allows automation of repetitive tasks. Be its paperwork, data collection, or diagnosis process, each receives a touch of automation. These improvements that AI renders, make our health sector more efficient.

Over the years, AI has accumulated plenty of information and experience. It has made technology not only smarter but dynamic as well. AI too can perform deep learning and track behavior, similar to the way our brain works. Think of AI-powered Eye Doctor developed by Google, it can report biological issues like flimsy and weak blood-vessels residing in the back of an eye. The data it receives undergoes AI analyses which generate reports. They reveal symptoms of the diabetic condition. Many other AI-powered systems have followed the lead. The prominent ones are Cisco and Nvidia. They have automated tomography scans, early detection of lesions and nodules through X-rays.

It is not all you need to know about healthcare applications of AI. In fact, apart from bringing precision and accuracy to the diagnosing process, it has also made possible to examine many patients in a shorter span. The capability to track and render intelligible deep patterns, patients leave is wonderful. It is one of the best applications of AI to predict in advance an outbreak of deadly disease.

As AI technology advances, its applications in the healthcare sector will only increase. It has the capability to diagnose a disease before its outbreak. It has the power to predict risks in advance, which is remarkable. In a similar way, it can detect breast cancer by analyzing mammogram reports.

These AI applications of AI-powered tools revolutionize the way doctor practice. It is now possible because the AI can identify a disease from millions of disease images with as much precision as a well trained and experienced pathologist or a radiologist.

Deep learning forms the backbone of AI. TensorFlow development employs AI deep learning to study, analyze biomedical, microscopic or macroscopic images. Finally, it reveals observations, earlier revealed by a qualified radiologist for diagnostic purposes. It aids physicians in the decision-making process about patients. Such integration of AI and deep learning by artificial intelligence companies is the reason why TensorFlow has become popular. Practitioners and businesses use their platform to deploy AI-powered healthcare diagnosis.

AI has indeed become a force to reckon with, especially when it comes to healthcare diagnostics. In fact, a report has shown that diagnostics are the focal point of AI SaaS business organizations. It is to remember that these SaaS organizations specialize in healthcare. It shows AI development companies are keen to make healthcare diagnostics emerging field of businesses. You may ask, why diagnostics? Why not any other domain of healthcare? It is because according to the recent report, it reveals that diagnostic errors contribute as many as 10% to patient deaths in the world. Not only that, 6–17% of medical complications patients undergo results because of the diagnosis.

Due to such reports, lead researchers, developers, and business owners hunt for a solution. And they settled on AI to offer them one. The outcome of such an effort is, applied artificial intelligence in healthcare diagnosis. Now that we have discussed the basics of AI and its applications in healthcare! It is time to move forward. To discuss its applications in making diagnostics more efficient, error-free and accurate!

· Oncology

The algorithm used in AI that makes it capable of deep learning has is of an advanced level. It can identify cancerous cells and tissues with a precision. If not greater yet comparable to that of an experienced and skilled physician!

· Pathology

You take body fluids like urine, saliva, blood and examine it to diagnose a disease. It is what we call pathology. It has become efficient and dynamic with the aid of AI run machine vision and deep learning.

· Rare Diseases

The combination of deep learning and face recognition technology is helpful. It enables clinicians to know diseases that usually remain unknown to them. They have, by analyzing facial features, diagnosed and revealed rare diseases. Genetic or otherwise!

So, in every aspect, from a diagnostic point of view, or time consumed in attending to a patient! Or predicting and preventing in advance an outbreak of a deadly disease. It has revolutionized the healthcare sector. It has made the process more rewarding. Not only for patients, who no longer have to bear the brunt of wrong observation and diagnosis is it helpful. But for doctors as well, it has come as a relief. All those time-consuming tasks, AI have automated them already. Hence, physicians have plenty of time at hand to attend to their patients. To communicate with the patient`s family and his clinician in a non-stop fashion has become a reality. And above all, they get to work with a technology that is well-integrated and connected.

Chatbots

U.K.-based startup Babylon Health is a health subscription-based service that has developed a chatbot for the prevention and diagnosis of disease.

Using speech recognition the chatbot will reportedly compare the symptoms that it receives from a user against a database of diseases. In response, it will recommend an appropriate course of action based on a combination of the reported symptoms, patient history, and patient circumstances.

For example, the app’s response to someone describing flu-like symptoms might be a recommendation to visit the pharmacy for over-the-counter medication. In contrast, if more serious symptoms are reported by a user, the app may recommend dialing an emergency hotline or going directly to the hospital.

In addition to its diagnosis feature, the app is also designed to integrate patient data from wearable devices to monitor vitals such as heart rate and cholesterol level.

Babylon is currently backed by $85 million in Series A and B funding from 9 investors including a mix of investment firms and individual investors such as Google’s DeepMind Learning project founders, Demis Hassabis and Mustafa Suleyman.

Building on its 150,000 registered users who currently pay £7.99 ($11.40) per month to access Babylon’s flagship one-on-one doctor video consultations from a pool of 100 doctors (available 12 hours per day, 6 days a week) the chatbot is projected to run about £4.99 ($7.10) per month.

The chatbot has been offered for testing to an eligible pool of 21,500 patients across two hospitals. The startup has received registrations from 10 percent of these patients to test the app. Reportedly, both Essex hospitals have experienced a reduction in patient waiting times since Babylon was first made available for free to their patients back in April 2015.

However, it is unclear by how much the waiting times were reduced.

Described as a “personal health companion,” Berlin-based Ada Health offers a platform that uses AI and machine learning to track patient health and offer users a better understanding of changes to their health. The platform is offered to individual users, organizations and physicians.

While the startup does not describe itself as a diagnostic service per se, the app does provide recommendations based on patient symptoms and health information.

 Hi-tech business concept . stock image

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