AI applications in healthcare can ultimately augment almost all human activities related to this domain, taking over tasks that range from medical imaging to risk analysis to diagnosing health conditions, but also addressing a significant portion of unmet clinical demand. This will result in a paradigmatic improvement of quality of delivered patient care, as well as its accessibility.
According to Accenture analysis, when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the United States healthcare economy by 2026. This can be multiplied by a number of countries with similarly developed healthcare systems. Moreover, AI offers a great possibility for countries that have not been able to develop their healthcare systems properly and suffer from lack of trained medical and caretaking staff.
The key problem that healthcare systems are facing on almost all levels can be phrased as “too much information, too little time”. AI is the right approach for addressing this problem. AI systems can help medical doctors to efficiently process huge amounts of information coming from medical data streams (medical history, X-ray and other images, lab results, pathology reports, genomics and other omics data) combined with patients’ health data (subjective reports, caretaker reports, health diaries, auxiliary devices and apps) and up-to-date medical guidelines and research reports in order to propose the most accurate diagnoses and the most efficient and personalised treatment plans.
AI can be utilized in several levels of automation in the healthcare system (courtesy of Thomas Riisgaard Hansen and his PhD thesis) :
- Augment. Example: scan through all patients in the hospital and tag cases that might require a second review.
- Find and Present. Example: when displaying a patient case, the algorithm suggests relevant literature, guidelines or similar patient cases.
- Assist. Example: a system such as Watson Oncology helps the doctors by suggesting a number of treatment options. However, the doctor is the one who performs the final diagnosis and creates the plan.
- Automate. Example: a system that detects a specific condition and automatically orders blood tests.
AI will thus help make quicker diagnoses and more accurate prognoses, create better treatment plans, speed up development of new drugs, enable new approaches to insurance, assist doctors with routine, repetitive and administrative tasks, and streamline patients’ entry to the healthcare system. There is an enormous potential in its ability to draw inferences and recognize patterns in large volumes of patient histories, medical images, epidemiological statistics, and other data. AI combined with healthcare digitization can allow providers to monitor or diagnose patients remotely as well as transform the way we treat the chronic diseases that account for a large share of health-care budgets.
Moreover, the healthtech domain can be expanded also by various kinds of personal assistants, coaches and other AI-driven agents improving human fitness, quality of life and wellbeing, which do not have a direct involvement in the healthcare process but can dramatically increase population’s health span.