Impact of AI on Medical Education and Employment

By Deepit Mudaliar,

(Image source: Medical Xpress)

Artificial intelligence (referred as AI) is the intelligence which tries to perform the same activities as the human cognitive abilities. The technology can be introduced into any device, be it a cell phone or any machine equipment in almost every kind of industry. It has now jumped out of sci-fi movies and novels and has engulfed our lives.

AI in healthcare has begun assisting doctors. Some of the common uses are for monitoring patient health efficiently using intelligent assistant devices like the Intelligent Virtual Assistant (IVA) and Medical Virtual Assistant (MVA). Chatbots too assist in advising and alleviating stress. Huge amounts of medical data can be analysed with speed and accuracy. It is used in complex surgeries to achieve precision. Predictive analysis can be done using electronic medical records and electronic health records.

For example, a patient with a symptom of high blood pressure can be assessed using earlier medical history, genetic tendencies as well as all other medical records. A treatment plan as well as preventive measures can also be instantly suggested. 

Some more compelling cases where AI is trying to be increasingly used are to identify genes that cause antibiotic resistance, brain computer interfaces to help the disabled communicate, implantable defibrillators for cardiac patients and improved electronic health record with audio and video image support.

Medical education remains based on information acquisition and application. This is where AI fits in. Machine learning algorithms introduced as a part of the medical curriculum will help young doctors analyse medical cases based on the knowledge of AI and can also devise new ways in future. However, the crunch will be felt among the professors who may also have to equip themselves with the latest technology required in this area.

At the Boston University Medical Campus, a machine learning introductory course related to biological and life sciences is taught, which will prepare the students for the data science and AI revolution.

The following three areas in healthcare education need to be worked upon:

• The knowledge has to be gathered, segregated and analysed. AI will step in and handle such “big data”.
• Students and doctors should have a robust knowledge of which AI applications are available and when to use them for effective treatment.
• Caregivers have to offload clinical assessments to the AI devices and have to enhance their capacity to respond with empathy and compassion in the course of the treatment.

Computers and digitisation created newer and more jobs. Similarly, AI which is an empowerment tool will not wipe out but create newer roles in the healthcare for surgeons, doctors, nurses and pharmacists.

It can help nurses with the routine tasks, and allow them to focus more on the abstract things that can truly impact patient care. This can be a boon to fact-based clinical observation.

AI can be viewed as an augmentative tool for a doctor who will transition into someone who understands how to wield data science and AI tools. This will take over the repetitive tasks and will provide accuracy and speed in decision making. However, it cannot yet replace the human qualities of empathy, creativity and innovation where the medicos will help.

In developed countries where there is dearth of doctors, AI could assist in providing the expertise and remote diagnosis required. For example, Google recently has put its DeepMind AI system in the area of recognising eye diseases.

A few years ago a rare form of leukaemia was treated by the University of Tokyo as suggested by Watson, a cognitive supercomputer by IBM within 10 minutes of analysing about 20 million papers based on cancer research.

Traditional medical treatment was reactive in nature; but with AI, past medical history can be analysed and there are apps like PeerWell that will proactively suggest patients the line of treatment even before the symptoms are recognised.

The Monarch, a surgical robotics platform by Auris Health, incorporates flexible robotics, micro-instrumentation, data science for therapeutic and diagnostic bronchoscopy procedures into a single platform, allowing physicians to better conduct endoscopic interventions.

AI will also help in lowering medical costs by more stream-lined and cost-effective healthcare solution. AI applied to structured and unstructured data, with techniques including machine learning and natural language processing is used in cardiology, neurology and oncology to provide more accurate service and impactful interventions to patients. AI healthcare start-up companies are working with predictive and preventive medicine boosted by the ongoing research with AI and the huge data available. The popularity of AI is exploding with immense power to unleash improvements in cost, quality and access. The AI health market is expected to reach $6.6 billion by 2021 from just $600 million in 2014, a compound annual growth rate of 40 per cent.

Some of the challenges that AI will pose are the limitations in the built-in algorithms which cannot handle undiagnosed cases. Expertise building with the pace of rapidly progressing technology and threats to data security are other challenges. 

(Deepit is a Research Intern at Centre for Public Policy Research. Views expressed by the author are personal and need not reflect or represent the views of Centre for Public Policy Research)