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Why do we need AI in Healthcare?

The accuracy of an AI fed device in a task like this would be below 10 per cent. Therefore, this limits the capacity of AI in healthcare.

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Ashok Pandey
New Update
AI in Healthcare

Globally the healthcare industry is at an inflection point. While the industry continues to evolve at a rapid pace, there are related aspects to be taken care so as to ensure adequate consideration to the overall administration of accessible healthcare. Such aspects include regulatory norms that keep on changing at frequent intervals particularly in a globalised economy, lack of integration and data security as well as analytics.

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AI is already in use, there are various successful applications of AI in healthcare. But do we really require such AI applications? Let's find out where and how AI is helping healthcare.

More accurate clinical decisions

Despite advances in AI and ML, there will always be some differences between human and technological execution. While healthcare providers need to adapt to AI, the technology can complement them in making more accurate clinical decisions or even replace human judgement in some functional areas.

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A technology-driven bot or machine can perform in a constrained environment but currently is not receptive to emotional quotients such as feeling, understanding, con¬sciousness, and creativity.

AI is the mimic of human cognitive functions that further require the input of raw data to be able to perform a function.

Zoya Brar, Founder and Managing Director, CORE Diagnostics Zoya Brar, Founder and Managing Director, CORE Diagnostics

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For instance, if you had to locate someone by looking at the back of their head in a picture taken from behind a crowd of people, you would still be able to do this most of the time.

The accuracy of an AI fed device in a task like this would be below 10 per cent. Providing that amount of data is not always feasible. Therefore, this limits the capacity of AI in healthcare that requires identifying patterns.

Radiologists and pathologists did not just learn to analyse images—like other humans, they learned to recognize patterns through 50 million years of evolution, four years of residency, years of fellowship, and maybe some work-life experiences.

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Before AI systems are deployed in healthcare applications, they need to be trained through the data generated during clinical procedures such as screening, diagnosing, testing, etc. which is further available in limited forms and formats (Demographics, medical notes, images, etc.).

Despite these limitations, AI is going to change the way we diagnose and treat diseases.

AI is already in use to address pathology exclusively, in areas ranging from molecular profile analysis to personalized treatment planning to image analysis for breast, brain, and prostate cancer. There are also going to be some successful applications of AI in healthcare from managing records, doing routine jobs, surgeries, health tracking to aiding in the precise diagnosis of a critical disease like cancer etc.

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However, with time, trust on AI and ML will become more strong in independently diagnosing disease and deriving clinical treatment decisions.

Critical considerations

Akansh Khurana, CEO, THB (Technology| Healthcare| Big Data Analytics) Akansh Khurana, CEO, THB (Technology| Healthcare| Big Data Analytics)

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Global health data is increasing at an enormous rate. It will be more than 2,300 exabytes (one exabyte = one billion gigabytes) by 2020. Artificial intelligence (AI) has been successfully used to play chess with computers and run driverless cars, it is now required in healthcare to handle this mammoth data in an intelligent manner. With AI, machines can automatically learn and improve as more data is generated.

The breadth of data availability is limited in India, given that a lot of health data is predominantly on paper. Majority of the healthcare industry still relies on the conventional data streams, i.e. internal sales/ops data and external data from the audit of sales and prescriptions.

Detailed clinical datasets on the longitudinal patient journey are rarely available. Logistics data w.r.t. movement of drugs from manufacturing unit to retailers is also limited.

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Dr. Keshab Panda, CEO & MD, L&T Technology Services Dr. Keshab Panda, CEO & MD, L&T Technology Services

AI and healthcare data in its longitudinal form is a powerful partnership. AI technologies like natural language processing and deep learning can integrate data from patient records and recognize patterns in it enabling better decision-making in healthcare and clinical settings.

It is in such a scenario that AI plays a key role. The use of AI ensures an advanced and fool-proof mechanism in place that takes care of all the critical considerations.

Furthermore, AI coupled with complementing technologies such as IoT and Machine Learning enables enhanced level of business excellence particularly across areas such as business intelligence, operational efficiency and customer experience.

Improve efficiencies

Prashant Gupta, Head of solutions South East Asia & India, Verizon Prashant Gupta, Head of solutions South East Asia & India, Verizon

Hospital errors lead to significant deaths, it has been observed that preventable hospital deaths due to hospital error are 11-20% higher than road accidents in a few countries.

As per HIMSS ( Healthcare Information and Management Systems Society), 86 per cent of mistakes in the healthcare industry are purely administrative and preventable. According to WHO, the doctor-to-patient ratio in India is just 0.2% for 1000 Indians which is indeed less than the standard set by WHO.

If AI is used for clinical automation, doctors will be able to use their time more effectively and efficiently. Using AI in diagnostics will improve efficiencies as AI can processes data from many data feeds, including historical data of similar cases. From providing personalized data and medical history to patients to remote delivery of clinical services, AI, ML, VR and Robotics, patients can have virtual access to surgeons and specialists.

Early detection of diseases

Venky Ananth, Senior Vice President, Head of Healthcare, Infosys Venky Ananth, Senior Vice President, Head of Healthcare, Infosys

Technology like AI in healthcare is rapidly evolving. In the last few years, there has been an increased shift in data digitization of health records which has moved up 90-95% compared to previous years.

Data in healthcare can be combined with affordable computational power which will enable early detection of diseases. This will amplify the physician’s capabilities to accurately diagnose diseases and will enable them to streamline and simplify operations.

Neha Rastogi, Co-Founder and COO-Agatsa Neha Rastogi, Co-Founder and COO-Agatsa

With the rise in population, the demand for quality healthcare has also increased rapidly.

Apart from this, with the increase in life expectancy, there is also a need for more resources, doctors and medical staff to cater to an ever-increasing segment of senior citizens and those suffering from chronic ailments.

AI works as a resource multiplier in such scenarios. AI-powered devices assist healthcare professionals to better understand the health conditions of their patients, advise course correction, and monitor their health status on a real-time basis. It won’t be wrong to say that AI has given people the power to control and monitor their health and wellness.

Round the clock digital assistance

Dr. Ashutosh Tiwari, Chairman & Managing Director, VBRI (Vinoba Bhave Research Institute) Dr. Ashutosh Tiwari, Chairman & Managing Director, VBRI (Vinoba Bhave Research Institute)

Digital assistance would be a 24x7 companion in the healthcare sector. It can help in monitoring the condition of the patient, transmitting results to medical experts, and to arrange virtual face-to-face appointments.

Reduced Cost

AI can reduce costs in the medical process. AI can assist in precisely assessing scans, reduce surgeries that are not necessary to perform, recommend treatments and can monitor medication and dosage, and a lot more.

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