In lockdown era of Covid-19 outbreak, creates a recession in different working sectors and numerous people lost their jobs in different areas. After pandemic, new job opportunities have been opened up. Among them, AI in health care is considered as one of the promising one. In the context of covid-19 pandemic there exist shortage of health care personnel and this not fulfilling the diagnosis response at the emergency stage. Integration of AI in health care can be considered as a promising option to overcome the shortage of health care personnel. Now question is that how a computer Engineer can incorporate AI in health care. The applications of AI into health care have been categorized into three groups.
- Patient-oriented AI
- Clinical oriented AI
- Administration and operational Oriented AI
The Patient oriented AI system can directly improve the patient care. According to the report of UK Govt., if the AI-enabled symptoms checker is coupled with the telemedicine technology, reduced number of physicians visits in hospitals. Different Machine Learning and deep learning-based (ML/DL) algorithms have been considered to train the aforementioned AI-enabled symptoms checker system where the several symptoms of the common diseases have been considered as the training data.
Apart from this, several organizations adopted the chatbot system to improve the patient care. Chatbot is a software program that automatically chat with the patients through text or voice messages. A chatbot system, initially collects information from patients. After analyzing this information using different Computer vision techniques, provides the information regarding the present conditions of the disease as well as, what he will do. In some places, the chatbot systems are not capable of collecting the patients’ information, a wearable device can play an important role. These wearable devices sense the patient’s disease information through some sensors and AI-based methodologies provide the actual conditions of the disease. Another noteworthy fact is that AI can improve the accuracy in disease detection.
In developing countries like India, the doctor and patient are low and an individual clinician works nearly 14-18 hours in a day. Due to this extensive workload, clinicians may overlook the early sign of the disease. A computer aided diagnosis system (CAD) can assist doctors to detect these symptoms at the early stages. The researchers from University of Calcutta said that their implemented CAD system is capable of detecting lung nodules at early stages which may indicate lung cancer if it is detected at later stages.
Furthermore, AI can also increase the efficacy of the targeted therapy. AI is capable of identifying the accurate effected area of the abnormal tissue. By supervising the effected area through computers, a clinician can provide the drug to the patients.
Apart from the computer vision techniques, the natural language processing (NLP) also improves the clinical outcomes. In daily clinical practice, clinicians often required previous disease history, medications doses and the family history of the disease to prepare appropriate diagnose plan. In health sector, the data are stored in an unstructured manner i.e., the health sector-maintained paper-based work. Due to extensive workload these data may lost. The Electronic Medical Record or EMR is a software where the NLP techniques can store large number of clinical text data in a structured form. In present context, the existing EMR software is very costly. This necessitates the AI-based health care industry to implement a low cost and more accurate EMR tool for improved diagnosis procedures. The Norway based Globus.ai’s AI enabled EMR system shows that it fills clinical data 90% more faster than the human work. Another interesting application of AI in health care is robotic surgery. In this application, different computer algorithms have been automated for different surgeries. However, the general decisions are still taken by the surgeon.
Beside the clinical outcomes, AI can also improve the patient safety. It has been observed that, several patients suffer from adverse drug effects i.e., the drug is not suited for the patient body. Israel’s MedAware’s patient safety platform considered different ML algorithms to detect and reduce the risk of medication error.
This discussion reveals that to provide improved health care, participation of AI engineers in heal care industry become an inevitable option. This creates huge job opportunities to the engineers.