Artificial intelligence (AI) techniques are increasingly being used in telemedicine to expand its capabilities and improve the existing telemedicine practices to effectively address specific problems. The flexibility and versatility of AI can assist in overcoming several telemedicine implementation challenges.
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This article discusses the growing importance of AI in telemedicine and the integration of AI into telemedicine for the management of diseases, specifically renal, neurological, gastrointestinal, and cardiac diseases.
Importance of AI in Telemedicine
Recent telepresence robot designs primarily focus on realizing autonomous movement of telepresence robots around rooms and hallways. These robots can be remote-controlled using a software interface that connects the user with the robot through a Wi-Fi connection.
This concept has been developed based on the integration of AI and vision systems for obstacle detection and navigation. For instance, the Dr Rho medical telepresence robot consists of a mobile body and screen for doctor-patient communication and features an intuitive vision system that instructs cameras to follow the gestures and movements of the doctor and a micro-projector for collaborative procedures and examinations.
An ankle rehabilitation system with feedback from a smartphone wireless gyroscope platform and machine learning classification is another example of combining AI and telemedicine for gathering data and monitoring a patient’s progress without using video communication/consultation.
In this approach, a device containing a wireless gyroscope platform on a three-dimensional (3D) printed frame connected through a smartphone is used to record the number of uses and effects of ankle therapy, and the efficacy of the rehabilitation strategy is measured using an ML algorithm.
Similarly, a web-based management system with ML techniques has been developed for faster and real-time data collection directly from patients with sickle cell disease. The system can be utilized for monitoring and management of the patient, and the AI technique can predict the amount of medication based on previous data.
Self-diagnosing applications and devices are increasingly gaining attention for quick evaluation of vital health indicators, such as breathing, heart rate, and pulse. AI can play a crucial role in these applications/devices. For instance, Carbon Health has developed a triage examination process using a chatbot interface. This process can summarize the patient’s information and schedule the consultation based on requirements.
AI can be applied to clinical data to determine the treatment outcomes and the value of products in pharmacy. Specifically, the pattern detection feature of AI can be used to effectively analyze healthcare information. For instance, the predictive model for assistive technology adoption for people with dementia utilizes neural networks to determine dementia patients’ ability to adapt to technology by analyzing and considering their behaviors and characteristics. A k-nearest neighbor (KNN) and data mining algorithms are utilized for patient behavior analysis.
Integration of AI into Telemedicine
AI-based Telemedicine for Renal Disease Management: Effective management of renal diseases, such as end-stage renal disease (ESRD) and chronic kidney disease (CKD), requires accurate diagnosis, disease progression monitoring, and personalized treatment plans. The integration of AI into telemedicine can revolutionize the management of renal diseases.
AI can improve the efficiency and accuracy of diagnosing renal diseases significantly. ML algorithms can analyze large patient information datasets, including imaging studies, laboratory results, and medical records, to identify indicators and patterns of renal diseases, enabling healthcare providers to make more informed diagnosis and treatment decisions.
AI-powered decision support systems can provide real-time recommendations to clinicians based on expert knowledge and evidence-based guidelines. Telemedicine combined with AI allows remote monitoring of patients with renal diseases. Sensors and wearable devices can collect data on biochemical markers, fluid balance, and vital signs continuously.
This data can be analyzed using AI algorithms to detect small changes in patterns and identify early signs of disease progression/complications. Timely medical intervention based on AI-derived insights can reduce the need for hospitalizations and prevent adverse outcomes.
AI-powered teleconsultation platforms can facilitate remote collaboration and communication between healthcare providers, including primary care physicians, specialists, and nephrologists. Experts can offer guidance in real time, review patient cases, and provide consultations through secure data sharing and video conferencing, which can promote interdisciplinary collaboration and improve access to specialized care for patients in underserved areas.
AI techniques such as predictive analytics can assist in risk stratification for patients with renal diseases. AI algorithms can prioritize interventions, predict the possibility of disease progression, and identify high-risk individuals, enabling healthcare providers to effectively allocate resources and provide targeted interventions to needy patients.
Moreover, AI techniques can play a critical role in improving medication management for patients with renal diseases. AI algorithms can analyze patient data, including medication history and comorbidities, to prevent adverse drug events, optimize medication regimens, and identify potential drug interactions.
AI-powered systems can provide medication alerts and reminders to patients to ensure complete adherence to prescribed medications and reduce the medication-related complication risk. AI can also assist healthcare providers in adjusting dosages and monitoring medication efficacy based on real-time patient data, leading to better treatment outcomes.
AI-powered tele-rehabilitation platforms can provide remote guidance and support to patients to enable them to monitor their progress and perform prescribed exercises, which improve patient convenience and functional outcomes. Virtual rehabilitation sessions, coupled with motion-tracking technology and wearable devices, can facilitate real-time feedback and ensure proper adherence to rehabilitation protocols.
AI-basedTelemedicine for Neurological Disease Management: AI can be used in telestroke applications to facilitate prompt stroke diagnosis and decision-making. AI algorithms can detect ischemic stroke effectively in electroencephalography (EEG) and computed tomography (CT) images, which can accelerate the diagnostic process and improve its accuracy.
Additionally, AI-powered devices can be employed to monitor heart rate variability and heart rate and screen asymptomatic atrial fibrillation using advanced methods, such as handheld electrocardiograph recorders and photoplethysmography. These devices can deliver higher accuracy in detecting potential heart function irregularities and prevent embolic stroke.
Wearable devices using AI can identify seizures and alert caregivers, while AI-powered mobile applications can enable patients to monitor their medication and symptoms. In Parkinson's disease, patient data, such as medication usage and demographics, can be obtained using smartphone-based questionnaires, and wearable devices can be used to analyze body tremors.
These data can then be fed into a deep learning (DL) model to diagnose Parkinson's disease. Moreover, AI-powered mobile applications can collect and analyze patient data, manage and monitor motor symptoms from a remote location, and evaluate the effectiveness of the treatment responses.
AI technology can be employed to remotely assess the disability level of individuals with multiple sclerosis. Additionally, AI-powered wearable devices can provide immediate notifications to individuals with Alzheimer's disease to improve the quality of patient care and tailor individualized treatment plans, resulting in better health outcomes.
AI-based Telemedicine for Gastrointestinal and Cardiac Disease Management: AI-assisted endoscopy based on DL algorithms can classify and identify lesions in real-time, which reduces the need for biopsies and improve diagnostic accuracy. AI can also interpret and analyze medical images, such as endoscopic images, colonoscopy, magnetic resonance imaging (MRI), sonography, CT scans, and positron emission tomography (PET) scans, to assist in the management and diagnosis of gastrointestinal diseases.
Additionally, AI technology in telemedicine can personalize treatment and care based on every patient's plans. AI algorithms can analyze electronic health records (EHRs) of patients to detect patterns and predict outcomes to tailor treatment plans for individual patients. Early detection of complications and timely intervention can be ensured by remotely monitoring patients using AI.
Telemedicine combined with AI allows continuous assessment of cardiac rhythms and vital signs in cardiac patients through remote monitoring. Wearable devices with AI algorithms and sensors can analyze real-time data to detect changes or abnormalities that indicate cardiac health deterioration, which enables timely intervention, prevents adverse events, and improves patient outcomes. Cardiologists can review patient data, including diagnostic test results, imaging studies, and medical history, through secure video conferencing using AI-powered teleconsultation platforms.
In conclusion, the integration of AI into telemedicine holds immense promise for the future of healthcare. AI can help automate routine tasks, predict and track patient outcomes, facilitate remote collaboration, and standardize best practices. While there are challenges in implementation, AI can augment the possibilities of telemedicine and has the potential to revolutionize medicine by improving patient safety and optimizing healthcare delivery globally.
References and Further Reading
Rezaei, T., Khouzani, P. J., Khouzani, S. J., Fard, A. M., Rashidi, S. et al (2023). Integrating Artificial Intelligence into Telemedicine: Revolutionizing Healthcare Delivery. https://www.researchgate.net/publication/374509826_Integrating_Artificial_Intelligence_into_Telemedicine_Revolutionizing_Healthcare_Delivery
Jheng, C., Kao, L., Yarmishyn, A. A., Chou, B., Hsu, C., Lin, C., Hu, K., Ho, K., Chen, Y., Kao, K., Chen, J., Hwang, K. (2020). The era of artificial intelligence–based individualized telemedicine is coming. Journal of the Chinese Medical Association, 83(11), 981-983. https://doi.org/10.1097/JCMA.0000000000000374
Pacis, D. M., Subido, E. D. C., Bugtai, N. T. (2018). Trends in telemedicine utilizing artificial intelligence. AIP Conference Proceedings. https://doi.org/10.1063/1.5023979