Artificial Intelligence has become an integral part of our lives. AI is capable of simulating human-level judgement with arguably better accuracy and consistency. The Internet of Things, Big Data Analysis and AI gave rise to the fourth industrial revolution. Shopping, manufacturing goods, government administration and daily living etc are all influenced by these advancements. AI includes machine learning, deep learning, neural networks, natural language processing and robotics.
The healthcare sector is no stranger to AI. Teleradiology, Telepathology, Teledermatology and Telepsychiatry have been upscaled by AI. The applications of AI in telemedicine are vast and varied. Many of the smart tracking devices and medical equipment use the principles of AI. Through machine learning and deep learning, AI can be trained to make correct predictions and analyses which can greatly benefit the healthcare industry.
The major applications of AI in telemedicine are:
- Optimising personalised medicine: Via smart devices and real-time tracking programs AI can relay the information to the corresponding doctor. An apple watch can track heart rate and generate an echocardiogram without even having to go to the hospital. AI can also help with automated updates of medical prescriptions. AI screens the diagnostic needs of the patients through detailed questionnaires and interactive chatbots, providing them with the best and not just the available doctors.
- Remote Patient Monitoring: Keeping a check on the health of patients at home is made easy thanks to AI advancements, especially in times of a pandemic. AI mediated telehealth platforms can also extend the healthcare reach to remote and rural areas. Monitoring the health of the elderly as well as those suffering from chronic health issues is critical. AI chatbots can provide reminders and assess the current health conditions of these patients. Assistive robots with inbuilt motion sensors are able to cater to the needs of the elderly.
- Offering better diagnosis: The data from the patients is sent to the doctor for further evaluation. AI can help physicians in decision-making and formulating better diagnoses. Through deep learning, AI has also enabled MRI and CT machines to collect, categorize, analyse and interpret the scans, thus providing a “second” opinion. Researchers also use augmented reality and AI to diagnose patients virtually. FDNA, one such AI platform, uses facial recognition to detect genetic disorders in patients. AI also inputs data quickly and efficiently so that the doctor can spend more time interacting with the patients.
- Organising healthcare information: In a hospital, a lot of data regarding reports, timings, visitations, scans, appointments etc. are generated. Along with Big Data Analysis, AI offers logistical solutions to categorise and access the data thus lowering the number of errors and administrative work hours as compared to traditional hospital methods. AI optimises and stores the data in the form of Electronic Health Records (EHR) using cloud computing. These records can be easily accessed by doctors and other healthcare professionals.
- Consumer Health Informatics: The lifestyle patterns of various people can be analysed with the help of smart devices, chatbot interfaces, voice assistants, and robots. From this data, possible disorders can be predicted well in advance and help to provide suitable preventative measures.
Concerns regarding AI in healthcare and telemedicine.
Using AI in healthcare brings with it an array of ethical and legal issues.
- There is the fear of unlicensed and unauthorised medical practice through these AI platforms. Since AI, via deep learning, is constantly relearning and updating itself based on new patient information, monitoring the legalities of AI from time to time by respective government authorities is quite tedious.
- Informed consent and the personal information of the patient can pose a lot of privacy and security threats.
- Bandwidth availability in rural areas can make it difficult to establish the proper functioning of AI.
- Sometimes, machine learning algorithms cannot accurately analyse the data across people of all races, genders or socio-economic statuses.
- Medical malpractice can arise due to the use of black-box algorithms.
- Many experts even express their concerns about AI replacing doctors in the long run.
Doctors are empathetic and they actively listen to the problems of their patients which is irreplaceable. It is necessary to eliminate all the risks associated with using AI. Professionals should constantly look out for possible black-box algorithms. Telemedicine based AI chatbots should introduce themselves as bots and not as people before asking for informed consent from the patient. Monitoring the regulations of various AI platforms can allow for its authorised use. Many companies are even developing smartphone AI-based applications to make healthcare accessible to remote locations.
The ever-growing population and the concomitant increase of health issues call for efficient healthcare solutions. AI-enabled telehealth facilities aim to provide fitting diagnostic measures and optimal patient care. Combining artificial intelligence and machine learning with telehealth platforms will see a surge in usage in the days to come.