Smart Healthcare Write For Us
Smart Healthcare Write For Us: The era of the Internet of Things (IoT) promises hundreds of billions of plans or physical objects connected to the Internet. These objects comprise sensors, actuators, and processors that help us collect data, make informed decisions, and optimize processes.
The potential economic impact of the IoT is expected to exceed US$10 trillion annually by 2025, of which US$2 trillion (the majority) will come from smart healthcare applications.
Smart healthcare encompasses the application of various artificial intelligence and machine learning technologies in healthcare.
Artificial Intelligence and Machine Learning Technologies in Healthcare.
Several technologies will enable these applications:
(i) Neural network-based disease diagnosis: using wearable medical sensors installed on smartwatches and smartphones that communicate with a medical server, allowing the doctor to monitor the patient’s health. These sensors include galvanic skin response, heart rate, skin temperature, blood oxygen level, blood pressure, an accelerometer, a gyroscope, etc. These neural networks can be integrated into a smartwatch or smartphone application. A single neural network can diagnose multiple diseases using multiple sensors, one for each disease. This can be achieved very energy-efficiently without draining the battery of these devices. They can be customise by selecting the diseases the user wishes to monitor and connecting the corresponding heads to the body of the neural network. Once a disease is detect, the same models can be used to maintain well-being by signaling its extremes.
(ii) Interpretability: Decisions made by machine learning models must be interpretable to increase confidence in their decision-making. A new type of machine learning model called differentiable logic networks.can provide such interpretability, as they are based on Boolean logic.
although they are train in the same way as neural networks. Interpretability is a very important criterion in medicine, as it helps ensure that the patient does not suffer harm from the outset, based on the predictions made by the machine learning model.
(iii) Personalized medical decision-making: For many diseases, there are often multiple treatments or interventions.
- The choice of method to use for a particular patient is usually determine by trial and error.
- With the advent of converters that capture the spatiotemporal characteristics of data obtain from groups of patients.
- it is now possible to predict which therapy will be most effective for a particular patient.
- This opens a new era in personalize medical decision-making.
- Genomic information: DNA data helps predict disease risk, response to drug therapy, or inherited diseases.
- Clinical data: Previous diagnoses, laboratory tests, imaging, and treatment history.
- Patient preferences: Values, beliefs, and treatment goals.
- AI and decision support tools: Algorithms analyze complex data to develop optimal treatments.
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