Innovation in technology always comes with a promise of future development possibilities. A new technology launched today, is old news tomorrow. The healthcare sector is no exception. Technologies like the Internet of Things, Artificial Intelligence, or Additive Manufacturing, have had tremendous disruptive effects on how we treat patients. They have also influenced the ways we keep track of our health daily.
AI has brought about a paradigm shift in the healthcare sector, powered by the increased availability of medical data & rapid development of analytics techniques. AI can be useful to process various types of healthcare data, structured and unstructured. Main disease areas that use AI tools consist of cancer, cardiology, and neurology. Diagnostic tools, genetics, electro-diagnosis, monitoring, physiology, disability evaluation, mass screening, and others are some of the prominent subsections that can deploy AI effectively. This technology is largely being used in diagnosis, since its early days.
“We should think about AI the way we think about patient care- as a continuum, spanning care areas and disease states.”- Dr. Mark Michalski, MD, Executive Director of the Massachusetts General Hospital.
AI can use erudite algorithms to ‘learn’ features from an enormous volume of healthcare data, and then use the acquired insights to assist clinical practice. This ability of AI can be further enhanced by equipping it with learning and self-correcting, so as to increase the accuracy of the feedback. An AI based system can assist the physicians; by providing informed medical information from previous literature, and clinical practices, to offer proper patient care. Additionally these systems can help reduce diagnostic and therapeutic errors. All these advantages and assistance is provided in real time, thus providing fast and calculated decisions to the physicians.
In spite of AI attracting substantial attention in medical research and healthcare sector, the technology has been facing tangible issues while its implementation. The two major hurdles that AI is currently facing are the regulations that bind the technology, security concerns, and data exchange. The efficacy and safety of AI systems require standards for their assessment, which current regulations lack. AI based systems require a continuous training or upgrading by clinical studies that are published daily, in order for them to work well. However due to the deficit in data supply and sharing, the initially deployed training with historical data is all the systems have to offer. Tackling these issues is a must before the healthcare sector can use and unleash the full potential of Artificial Intelligence in providing state-of-the-art patient care!