AI has been involved in medicine since as early as the 1950s when physicians made the first attempts to improve their diagnoses using computer-aided programs.
In 2018, studies investigated AI and natural language processes as possible tools to manage patients and administrative elements.
The USA tops the list of countries with the maximum number of articles (215), followed by China (83), the UK (54), India (51), Australia (54), and Canada (32). It is immediately evident that the theme has developed on different continents, highlighting a growing interest in AI in healthcare.
Here are a few applications of AI in healthcare:
Health Service Management:
One of the notable aspects of AI techniques is potential support for comprehensive health services management. These applications can support doctors, nurses, and administrators in their work.
AI can optimize logistics processes, for instance, recognizing drugs and equipment in a just-in-time supply system based totally on predictive algorithms. Interesting applications can also support the training of personnel working in health services. This evidence could help bridge the gap between urban and rural health services.
Health services management could benefit from AI to leverage the multiplicity of data in electronic health records by predicting data heterogeneity across hospitals and outpatient clinics, checking for outliers, performing clinical tests on the data, unifying patient representation, improving future models that can predict diagnostic tests and analyses, and creating transparency with benchmark data for analyzing services delivered.
Predictive Medicine
We need more efficient AI applications for disease prediction, diagnosis treatment, outcome prediction, and prognosis evaluation.
AI techniques can also help design and develop new drugs, monitor patients, and personalize patient treatment plans.
Doctors benefit from having more time and concise data to make better patient decisions.
Clinical decision-making
AI applications could support doctors and medical researchers in the clinical decision-making process.
Algorithmic platforms can provide virtual assistance to help doctors understand the semantics of language and learn to solve business process queries as a human being would.
Patient data and diagnostics
AI systems can manage data generated from clinical activities, such as screening, diagnosis, and treatment assignment. In this way, health personnel can learn similar subjects and associations between subject features and outcomes of interest.
These technologies can analyze raw data and provide helpful insights, beneficial for inpatient treatments. They can help doctors in the diagnostic process; for example, it will be simpler to have an overall patient condition image to realize a high-speed body scan. Then, AI technology can recreate a 3D mapping solution of a patient’s body.
For Surgery, AI has a vast opportunity to transform surgical robotics through devices that can perform semi-automated surgical tasks with increasing efficiency. The final aim of this technology is to automate procedures to negate human error while maintaining a high level of accuracy and precision.
AI techniques can make a difference in rehabilitation therapy and surgery for diagnostics. Numerous robots are there to support and manage such tasks. Rehabilitation robots physically support and guide, for example, a patient’s limb during motor therapy.
Finally, the COVID -19 period has led to increased remote patient diagnostics through telemedicine that enables remote observation of patients and provides physicians and nurses with support tools.
Secinaro, S., Calandra, D., Secinaro, A. et al. (04/10/2021):
