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Artificial Intelligence (AI) in Healthcare

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Shuvankar Pramanick, CIO, Sir Ganga Ram Hospital Sir Ganga Ram Hospital is a multispeciality state-of-the-art healthcare provider, which specializes in Adolescent Clinic, Colorectal Clinic, High Risk Pregnancy, Child Development Clinic, Paediatrics Gastroenterology Clinic, Tobacco Cessation Clinic, Diabetic Foot Care Centre and many more.

Artificial intelligence(AI)is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
• Speech recognition
• Learning
• Planning
• Problem solving
In recent years, artificial intelligence has been one of the most talked about and popular technology trends in healthcare.

Why?
The implementation proved that AI could provide valuable insights into accurately matching patients with specific needs to treatment plans that match their conditions.

Another use, which adopted Microsoft AI technology to help determine highrisk patients in their ICU. The process included the use of machine learning and advanced analytics to determine, based on different indicators, who needed attention and would be considered high risk.

Applications in AI for Healthcare
Medical Imaging
Cardiology and radiology are two disciplines where the amount of data to analyze can be over whelming and time consuming. Cardiologists and radiologists in the future should only look at the most complicated cases where human supervision is useful.

Analyzing tests, X-rays, CT scans, data entry, and other mundane tasks can all be done faster and more accurately.

Treatment Design
Artificial intelligence systems have been created to analyze data - notes and reports from a patient’s file, external research, and clinical expertise - to help select the correct, individually customized treatment path.

Digital Consultations
App based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses.

Virtual Nurses
A digital nurse to help people monitor patient’s condition and follow up with treatments, between doctor visits. The program uses machine learning to support patients, specializing in chronic illnesses.

Drug Creation
Developing pharmaceuticals through clinical trials can take more than a decade and cost
billions of dollars. Making this process faster and cheaper could change the world. Amidst the recent Ebola virus scare, a program was used to scan existing medicines that could be redesigned to fight the disease. The program found two medications that may reduce Ebola infectivity in one day, when analysis of this type generally takes months or years–a difference that could mean saving thousands of lives.

"Artificial intelligence systems have been created to analyze data - notes and reports from a patient’s file, external research, and clinical expertise - to help select the correct, individually customized treatment path"

Healthcare System Analysis
A Dutch company uses AI to sift through the data to highlight mistakes in treatments, workflow inefficiencies, and helps area healthcare systems avoid unnecessary patient hospitalisations. These are just a sample of the solutions AI is offering the healthcare industry. As innovation pushes the capabilities of automation and digital workforces, from providers like Novatio, more solutions to save time, lower costs, and increase accuracy will be possible.

Healthcare System Analysis
A Dutch company uses AI to sift through the data to highlight mistakes in treatments, workflow inefficiencies, and helps area healthcare systems avoid unnecessary patient hospitalisations. These are just a sample of the solutions AI is offering the healthcare industry. As innovation pushes the capabilities of automation and digital workforces, from providers like Novatio, more solutions to save time, lower costs, and increase accuracy will be possible.

Benefits of AI in Healthcare
Advancement in Treatment
AI has the ability to quickly and more accurately identify signs of disease in medical images, like MRI, CT scans, ultrasound and X-rays, and therefore allows faster diagnostics reducing the time patients wait for a diagnosis from weeks to mere hours and accelerating the introduction of treatment options.

Virtual Assistance
In this day and age when people expect to get answers instantly, virtual assistants enable patients to get answers in real time. Patients can ask medical questions and receive answers, get more information and reminders about taking medications, report information to physicians, and gain other medical support.

Physicians can also take advantage of healthcare virtual assistants by tracking and following through with orders and making sure they are ordering the correct medication for patients.

Treatment Plan
Another benefit of AI in healthcare is the ability to design treatment plans. Doctors can now search a database, such as Modernizing Medicine, a medical assistant used to collect patient information, record diagnoses, order tests and prescriptions and prepare billing information. Moreover, the ability to search public databases with information from thousands of doctors and patient cases can help physicians administer better personalized treatments or find comparable cases.

Reduce Cost
AI has the potential to improve outcomes by 30-40 percent and reduce the cost of treatment by as much as 50 percent. Improvements in precision and efficiency mean fewer human errors, leading to a decrease in doctor visits. Doctors are also able to get information from data for patients who are at risk of certain diseases to prevent hospital re-admissions.

Risks
Take a system trained to learn which patients with pneumonia had a higher risk of death, so that they might be admitted to hospital. It inadvertently classified patients with asthma as being at lower risk. This was because in normal situations, people with pneumonia and a history of asthma go straight to intensive care and therefore get the kind of treatment that significantly reduces their risk of dying. The machine learning took this to mean that asthma + pneumonia = lower risk of death. Very Risky Indeed!!

Challenges
• Lack of cloud-based adoption slows down AI adoption.
• Shortage of AI talent causes more delays in adoption.
• Limited knowledge and understanding of AI.
• Lack of Advanced analytics and research.