AI-based applications and chatbots help care providers in delivering nursing aid after being discharged from the hospital. This aspect helps shorten the provision of outpatient services and boosts the accuracy of examining patient compliance post-discharge.
FREMONT, CA: Artificial intelligence (AI)–the smart and cognitive technology of present day has breakthrough across all probable verticals–from manufacturing to financial services and healthcare is no exclusion. With the interest in AI booming exponentially, its capacity for application in care-based applications has broadened beyond imagination.
Reports signify that the AI-driven healthcare market will see a tremendous expansion of about 40 percent by the end of 2020. From delivering advanced care-related data to doctors to make informed decisions to tailored real-time treatment, advanced applications of AI are revolutionizing care.
Below are some of the exceptional applications of AI in today’s care ecosystem.
One advanced applications of AI in healthcare is in disease diagnosis. With AI, tools are supercharged with the capability to investigate voluminous information from medical images, prompting early analysis of many disorders. AI offers an easy solution through smart diagnostic imaging. This approach has many applications in a proactive diagnosis of the possibility of tumor growth, stroke, and particular types of cancer, giving the physician a chance to gain comprehensive treatment plans for patients promptly.
Biomarkers automatically offer accurate visual and audio information of patients’ vital health parameters that designate the occurrence of specific medical conditions. Additionally, they also help in choosing the ideal medications or evaluate treatment sensitivity. Biomarkers precisely capture symptoms, as alongside the guesswork of symptoms professed by patients. The precision and speed of biomarkers have made them the favored tools of diagnosis, swiftly highlighting possibilities of any disorders.
3. Virtual Nursing Assistance
AI-based applications and chatbots help care providers in delivering nursing aid after being discharged from the hospital. This aspect helps shorten the provision of outpatient services and boosts the accuracy of examining patient compliance post-discharge. Available even as effortless wearable and on smartphones, these AI-enabled tools also act as virtual health assistants. They remind patients about their medications, hearten them to follow their exercise routines, answer medical clarifications sought by patients, and caution care providers about any untoward incidents like a sudden increase in blood pressure or a fall.
4. Remote Monitoring of Patients
This includes round-the-clock remote control of patients, regular assessment of their vital signs, and real-time alerts to care providers and caretakers. This remote evaluation of fundamental health parameters helps doctors identify core symptoms of disorders and diseases in patients and react accordingly. This approach prevents pointless visits to the doctor to a high degree.
5. AI and Drug Discovery
AI-driven computing can precisely and promptly examine structures of different drug molecules and predict their pharmacological potency, activity, and adverse effects. This prospect opens up a fast and cost-efficient way of drug discovery. It also has the chance of considerably reducing the price of medications. Employed across pharmaceutical businesses, AI-based drug discovery has supplied to supporting the treatment of neurodegenerative disorders and cancer.
6. AI-Enabled Hospital Care
AI simplifies care delivery in hospitals with the help of a wide range of solutions, including patient medication tracking, smart monitoring of IV solutions, patient alert systems, nursing staff’s performance evaluation systems, and patient movement tracking in hospitals. Robot-assisted surgeries and AI tools in routine phlebotomy procedures are other potentially helpful applications. AI has been found to significantly reduce dosage errors and augment nursing staff efficiency in hospitals.
With large investments driving in for AI applications, there is still a lot to be done with the technology, despite its presence in the healthcare industry for quite many years now. The main reasons for its slow implementation are the cost of research, the safety concerns involved in opening up comprehensive databases, and errors or misconceptions in coming to quick conclusions. But the expedition for ideal AI solutions looks promising with AI supplementing healthcare and enhancing the quality of care from diagnosis to prognosis.