There is no doubt regarding the potential of AI and machine learning in the world today. It has already found major applications in a wide array of fields including medicine and healthcare. According to a report by Accenture Analysis, key clinical health AI applications have the potential to make $150 billion in annual savings for the economy of US healthcare by the year 2026.
Within healthcare, AI can be leveraged in a number of ways to improve quality of life. By entering the patient’s past data into AI models, it will be possible to identify and foresee future events such as the likelihood of a relapse. AI algorithms can help find complex patterns in medical imaging which can be used to design treatment plans and as well as optimize healthcare operations. AI-powered tools empower clinicians by reducing time and effort required to maintain manual records. In a more sophisticated approach, robots can be developed to assist with surgical procedures while assistive technologies can reduce the burden on clinical workflows. On the other hand, health bots such as Microsoft’s Healthbot enables the patient to converse with an AI-run health agent which can identify suitable nurses or physicians in a very cost-effective and efficient manner.
When implemented properly, AI in healthcare can enhance efficiency in organizations and help doctors as well as patients to make informed choices which could prove to be critical in saving lives. However, with the proliferation of data generated from images such as x-rays and CT scans as well as continuous, real-time monitors of physiology, AI systems need to be equipped with more capabilities to sustain the demands of healthcare.