INTRODUCTION:

The World Health Organization (WHO) defines telemedicine as the practice of providing medical care remotely by utilizing information and communication technologies. It makes it possible for medical professionals to carry out research, diagnose, treat, and prevent diseases in addition to advancing their medical education, all of which contribute to the improvement of the health of individuals and communities. This approach is very beneficial to people with uncommon disorders, particularly those who reside distant from specialized medical facilities. Many families find it difficult to get the right treatment on a daily basis, especially if their children have unusual diseases. Access to prompt and continuous medical assistance is usually restricted, and traveling great distances to see several doctors can be emotionally, financially, and physically exhausting. The recognition of rare diseases as a worldwide health concern is growing. It is much more challenging for residents in rural locations to get the proper care because most specialized care for these disorders is located in big cities. Because rare diseases sometimes require a multidisciplinary team of professionals, including neurologists, geneticists, physiotherapists, and dietitians, they are more complex.¹Nowadays, telehealth and telemedicine are often used interchangeably. Telemedicine has been used for a long time. During the Civil War, telegraphs were first used to transmit names of wounded soldiers and solicit medical supplies. Later, the invention of the telephone, radio, and other wireless devices improved long-distance medical communication much further. Teleradiology and other remote medical image transmission and interpretation were the main uses of the term "telemedicine" in the past. But as technology developed, especially with the introduction of the internet, telehealth rapidly grew. People may now easily access and exchange information online via Web 2.0, and doctors can now interact in real time with patients or with each other thanks to Voice over Internet Protocol (VoIP). Furthermore, cell phones and tablets were essential since they allowed for remote teamwork and real-time medical talks. In India, where many people reside in remote regions and there is a shortage of doctors, nurses, and other health professionals, telemedicine is extremely beneficial. Almost 70% of Indians live in rural areas with no access to even the most basic medical care. The potential of telemedicine to deliver high-quality, cost-effective healthcare can be extremely beneficial to these underserved communities. It has the potential to reduce the disparity in healthcare access between rural and urban areas, even though its long-term viability is still uncertain.²

Fundamentally, telemedicine uses safe online channels to link patients with medical specialists. These platforms could consist of:

· Live video consultations, which allow patients to communicate with doctors in real time.

· Systems that store and forward medical records or images for later review.

· Remote patient monitoring, which uses wearable technology to track vital signs like blood pressure or glucose levels.

· Apps for mobile health (mHealth), which assist patients in tracking their health behaviors and managing chronic conditions.

Advanced Applications of Telemedicine:

· Teleradiology: Radiologist review X-rays.

· Telepsychiatry: Mental helath service delieverd.

· Telecardiology: Cancer patient receive consultation and follow-ups remotely.

· Teleoncology: Heart health can be monitered remotely using ECG and other tools.

Introduction of artificial intelligence technology in healthcare:

Artificial intelligence (AI) is the development of computer systems that can perform tasks that are often associated with human intellect. AI is transforming healthcare through data analysis, automation, and machine learning. It is enhancing medical judgment, diagnosis, and care. Better care, greater efficacy, and lower healthcare costs could result from these advancements. ³ As AI-driven technology advances and is progressively integrated into existing systems, it is fundamentally changing the way healthcare is delivered, administered, and experienced by patients.⁴In some diagnostic activities, AI systems outperform human physicians in terms of speed and accuracy. Large volumes of medical data, including genetic information, patient records, and photographs, are processed using machine learning algorithms to look for trends that could lead to a quicker and more accurate diagnosis. ⁵Customized treatment programs that target each patient's specific needs and increase treatment efficacy are made possible by AI-powered knowledge.⁵AI reduces negative outcomes and enhances treatment results by using patient data to give tailored medication.⁶ These technologies reduce hospital admissions and readmissions by remotely monitoring patients and alerting medical professionals to issues before they get worse.⁴AI's ability to manage massive amounts of data and streamline processes allows healthcare institutions to function more efficiently and focus resources where they are most needed. Artificial intelligence (AI) has the potential to drastically lower healthcare costs by increasing diagnosis accuracy, eliminating pointless procedures, and enhancing treatment plans. One significant use of AI in healthcare is predictive analytics, which aids in resource allocation, population health management, and patient demand forecasting. These characteristics are especially helpful in the management of chronic illnesses since early detection and customized therapy can avoid expensive outcomes and hospital admissions.⁷A culture change is also required for AI inclusion in the healthcare sector. Artificial intelligence (AI) improves operational competency through automating procedures like as scheduling, billing, and patient triage, as well as by efficiently allocating resources to reduce wait times and improve workflow in general.⁵

Types of Al in Healthcare:

As they facilitate it being simpler to find patterns and trends in large datasets, machine learning algorithms are crucial to the healthcare industry. Natural language processing is another aspect of AI that enables robots to comprehend and communicate with people. This makes things like voice-activated devices and language-based data processing easier. AI-powered robots are used in healthcare to automate tasks and offer direct assistance while patients are being treated.⁴The correlations highlight how specificity and complexity rise from AI to DL as general AI concepts give way to the next generation of sophisticated, data-driven learning capabilities. AI in healthcare can take many different shapes, each with a distinct purpose. For example, rule-based systems are appropriate for tasks such as symptom screening and make decisions based on predetermined criteria. However, ML algorithms are essential for personalized treatment plans and diagnostic imaging because they are able to identify patterns in data. Applications like voice recognition and Chabot’s are made possible by NLP, which makes human-computer interaction easier. Robotics is another area of AI that has applications in physical rehabilitation and surgery. This demonstrates the breadth and complexity of AI's potential applications in healthcare. AI's ability to quickly and accurately analyze complicated medical data holds promise for improving data-driven decision-making in the healthcare sector.⁵

Fig.1.Schematic illustration of the framework and relationship between AI, ML and DL ⁸

Artificial Intelligence (AI):

Artificial intelligence (AI) is the process of creating computer systems that can do tasks that usually require human intelligence, such as learning, recognizing speech, and solving problems.

Machine Learning (ML):

Machine learning is a way for computers to learn from data and get better at making decisions over time without having to be programmed for every situation.

Deep Learning (DL):

Deep Learning (DL) is a subfield of machine learning that analyzes vast amounts of data and employs artificial neural networks that are modeled after the human brain to automatically learn and make sophisticated judgments.

Supervised Learning (SL):

A machine learning technique called supervised learning uses a dataset that contains both inputs and the accurate outputs that go along with them to teach the model. The model makes predictions or correctly classifies fresh data using this information.

Unsupervised Learning (USL):

When a model is given data without any labeled responses, this is referred to as unsupervised learning. It examines the data on its own to find hidden groups, patterns, or structures.

AI in Telemedicine:

The provision of health services is being transformed by artificial intelligence (AI) and telemedicine, especially in rural areas where access to medical care is sometimes limited. The local population frequently faces issues such as poor roads, far-off hospitals, a lack of doctors, and insufficient healthcare facilities. However, by using AI and telemedicine, these problems can be mitigated, improving healthcare's effectiveness and accessibility. Through telemedicine, patients in remote areas can consult with physicians and get examinations without having to make long trips. Through phone calls or video chat, patients can interact with healthcare professionals in real time. This is especially helpful in rural towns or villages where transportation can be a big issue and hospitals are located far away. By supporting clinicians in diagnosing ailments and developing treatment strategies, artificial intelligence (AI) improves telemedicine. Artificial intelligence (AI) systems are capable of analyzing vast amounts of health data, such as scans, medical reports, and symptoms, to help doctors make better decisions. AI helps doctors quickly recognize urgent conditions and suggest the best course of action given the restricted resources available in areas with a shortage of medical resources. The combination of telemedicine and AI are significantly improving healthcare in rural areas. These technologies not only aid in resolving current problems like access concerns and delayed care, but they also assist build a stronger and more sustainable healthcare system in the future. As technology develops, cooperation between telemedicine and artificial intelligence will be essential to guaranteeing that even the most isolated villages receive the medical attention they require.⁹

Advantages:¹⁰

1. Faster Diagnosis: AI can help physicians detect diseases like cancer, stroke, or COVID-19 at far earlier stages by processing medical data (such as X-rays, CT scans, or reports) quickly.

2. 24/7 Virtual Assistance: Virtual health assistants and AI Chabot’s may also answer basic inquiries for patients at any time, which helps ease the burden on the medical staff and provide assistance after hours

3. Remote Monitoring: Through wearable technology, AI can monitor patients with chronic illnesses (such as diabetes or heart disease) and notify them if something is amiss so that early intervention can take place.

4. Personalized Treatment Plans: Through patient history and predictive analytics, AI can help structure customized care plans specific to patients and enhance treatment success.

5. Improved Access in Rural Areas: By establishing a direct virtual connection between patients in remote locations and leading specialists, AI-powered telemedicine is also bridging the urban-rural divide

6. Medical Imaging Analysis: AI is capable of effectively evaluating radiology images, which speeds up and improves diagnostic accuracy. This is especially helpful in areas with a shortage of radiologists.

Disadvantages:^11,
12

1. Insufficient Human interaction: AI cannot fully replace the human connection, empathy, and emotional intelligence that many patients need throughout treatment.

2. Diagnostic Errors: AI is far from flawless; algorithmic errors or low-quality data could lead to incorrect diagnosis or therapy suggestions.

3. Dependence on Internet and Devices: AI-powered telemedicine necessitates smart gadgets and reliable internet, both of which are frequently lacking in rural or underdeveloped locations

4. Limited Understanding in Complex Cases: AI fails when cases are rare or extremely complicated and require human experience and critical thinking.

5. Job Displacement: Automation of routine tasks may lead to job loss among administrative staff or low-level healthcare workers.

6. Bias in AI Algorithms: AI systems trained on biased or incomplete data may make unfair or inaccurate decisions especially for underrepresented groups.

How AI Fits into Telemedicine:

The technique of providing medical care remotely using technology, including video chats, apps, and online consultations, is known as telemedicine. The application of artificial intelligence (AI) improves telemedicine. AI can do more than just "listen"; it can also learn, analyze, and help make decisions more quickly and accurately. AI is being used to encourage patients, assist physicians, and speed up therapies for anything from heart problems to cancer all without the need for a hospital stay.

Integration of AI in Telemedicine in various diseases

AI TOOL’S

Fig.2 Steps to use AI tools

Dermatology: As the largest organ in the human body and a visible indicator of health, dermatological problems impact not only physical health but also emotional and social well-being. The way skin lesions look has a big impact on diagnosis and therapy in the clinical medicine specialty of dermatology. The conventional diagnosis method includes a patient's medical history, clinical symptoms, dermoscopic images, and sometimes a histological analysis.¹³

Skin Vision App¹⁵

Advantages:

· High Sensitivity for Skin Cancer Detection: Supports early diagnosis by detecting 95.1% of the most prevalent skin malignancies, including melanoma, BCC, and SCC.

· Convenient and Accessible: Enhances access to early risk assessment by enabling individuals to evaluate skin spots at any time a smartphone.

Disadvantages:

· Accuracy Limitations/False Positives and Negatives: Skin Vision over classifies benign lesions as high risk (false positives) and overlooks some cancerous lesions (false negatives).

· Requires Good Image Quality: Poor image quality; actual use may result in less accurate and poorer image capturing

Application:

The Skin Vision app is an AI-powered mobile application designed for the early detection and monitoring of skin cancer, including melanoma.

Diabetes:

Diabetes is a chronic illness characterized by improper lipid and protein metabolism as well as elevated blood glucose levels. Blood glucose levels increase when the pancreas produces insufficient insulin or when the cells cannot effectively use the insulin that is produced because the cells are unable to digest the glucose.

Diabetes comes in three main forms:

a) Type 1, in which insulin is not produced by the pancreas;

b) Type 2, wherein the body's cells are unable to withstand the effects of the insulin that is being generated, and the production of insulin gradually declines over time; and

c) Pregnancy-related gestational diabetes can lead to difficulties both before and after delivery, and it raises the mother's risk of type 2 diabetes and the offspring's chance of obesity.

Diab Trend¹⁶

Advantages:

· AI-powered Predictions: Using patterns, food consumption, insulin, and activity, it can forecast blood glucose levels up to four hours in advance.

· Smart Food Recognition: Reduces manual labor by automatically calculating the carbohydrate and calorie content and using the phone's camera and artificial intelligence to identify food items.

Disadvantages:

· Accuracy Limitations: AI estimates may be less accurate if meals are misinterpreted or glucose data is lacking. Predictions rely on the quantity and quality of logged data.

· Subscription Cost: Predictive AI and enhanced food identification are only two of the many sophisticated features that are exclusive to the premium edition.

It can be costly to use over time.

Application:

· Automatic carbohydrate calculation, portion estimation, and food recognition.

· Predicting blood glucose levels, estimating weight

· Prevent episodes of hypoglycemia.,to comprehend your previous blood glucose levels

· Keep track of everything in one location, including blood pressure, heart rate, blood glucose level, medications taken, immunizations, hypo episodes, and illnesses.

Cardiovascular Disease:

Cardiovascular diseases (CVDs) are conditions of the heart and blood vessels combined. These include peripheral arterial disease, coronary artery disease (CAD), cerebrovascular disease (stroke), rheumatic heart disease, congenital heart disease, heart failure, hypertension, and cardiomyopathies.⁹ These diseases are often caused by abnormalities in the structure or function of the cardiovascular system. Many CVDs share atherosclerosis as their common underlying pathology, which is the buildup of lipid-rich plaques in the arterial walls. It may lead to reduced blood flow, ischemia, and thrombosis. Other pathophysiologic reasons include oxidative stress, endothelial dysfunction, inflammation, and abnormalities in vascular tone (such hypertension).¹⁹

Causes of CVD:

· Age and Genetics: Risk rises with age and family history.

· Hypertension: Damages arteries and accelerates atherosclerosis.

· Dyslipidemia (High LDL, Low HDL): Leads to plaque buildup.

· Diabetes / Hyperglycemia: Causes endothelial dysfunction and vascular damage.

· Obesity and Poor Diet: Increases risk via hypertension, insulin resistance, and dyslipidemia.

· Smoking and Tobacco Use: Damages endothelium and promotes clotting.

· Physical Inactivity: Increases risk of obesity, diabetes, and hypertension.

· Excessive Alcohol Intake: Linked to cardiomyopathy and hypertension.

Circadian AI ¹⁷

Fig.3 Illustrate working of Circadian AI

Advantages:

· Fast detection: With simply a smartphone, you can record a heart sound and receive a result in roughly seven seconds.

· High reported accuracy: In tests conducted in the US and India, the app's accuracy in identifying different cardiovascular disorders has exceeded 96%.

Disadvantages:

· Data Bias and Generalizability: Artificial intelligence models may not function well in populations that are different from those utilized in development if the training data is not diverse (in terms of age groups, comorbidities, ethnicity

· Privacy and Data Security Health data (audio of heart sounds, metadata, etc.) are sensitive. Ensuring encryption, secure storage/transmission, user consent, proper anonymization is essential. Risks of breach or misuse exist.

Application:

· Heart sound analysis: Heart sound is analyzed by sophisticated AI algorithms to find minute patterns that could point to illnesses.

· Early detection: Prior to the onset of conventional symptoms, detect possible heart conditions to facilitate quicker interpretation.

· Comprehensive diagnosis: AI can use heart sound patterns to identify more than 40 distinct cardiac conditions.

Cancer:

Cancer is a broad category of diseases marked by the unchecked proliferation and dissemination of aberrant cells rather than a particular illness. In a healthy body, a mechanism known as apoptosis (programmed cell death) allows cells to grow, divide, and die in a systematic manner. The genetic blueprint of the body strictly controls this process. When this orderly mechanism malfunctions, cancer develops.¹⁴

Fig.4 Process of cancer development

Common Types of Cancer:

· Carcinoma: This type of cancer starts in the skin or tissues lining internal organs, such as the breast, colon, prostate, or lung.

· Sarcoma: Initiates in connective tissues such as bone, cartilage, muscle, or fat.

· Leukemia: Causes a high quantity of aberrant blood cells and starts in tissue that forms blood, such as bone marrow.

· Myeloma and lymphoma: Start in immune system cells.

· Cancers of the Central Nervous System: These start in the brain and spinal cord tissues.

PATH AI ¹⁸

Advantages:

· High Accuracy: Standardizes grading and lowers diagnostic errors.

· Biomarker insights: Supports research on immunotherapy and precision medicine.

Disadvantages:

· Dependency on Data: The quality of the training data and the preparation of the slides (staining, scanning) affect accuracy

· Privacy Issues: Data security concerns arise while handling patient pathology images, particularly in cloud environments

Application:

· Cancer Diagnosis and Grading: Accurately detects and classifies malignancies, including those of the breast, prostate, lung, colon, etc.

· Identification of Biomarkers: Aids in the development of targeted therapies by quantifying biomarkers such as PD-L1, HER2, MSI, and others.

CONCLUSION:

The integration of Artificial Intelligence (AI) with telemedicine signifies a groundbreaking advancement in modern healthcare. The fusion of AI-powered data analysis and telehealth technologies has enhanced diagnostic accuracy, optimized treatment processes, and enabled continuous patient monitoring, thereby making quality healthcare accessible across geographical boundaries. Through innovations such as mobile health apps, wearable devices, and intelligent diagnostic tools, patients can now obtain timely, customized medical care from their homes especially beneficial for those living in rural or remote regions with inadequate healthcare infrastructure.AI-based telemedicine systems have proven highly effective in the management of chronic and lifestyle-related diseases, including diabetes, hypertension, cardiovascular disorders, skin ailments, cancer, and infectious diseases like COVID-19. These advancements have improved patient recovery rates, reduced treatment expenses, minimized hospital dependency, and strengthened clinical decision making. Despite these advantages, several challenges persist, including issues of data privacy, algorithmic bias, insufficient digital awareness, and high implementation costs. Addressing these obstacles requires the establishment of robust ethical standards, legal guidelines, secure data management, and ongoing training for healthcare providers. In summary, the application of AI in telemedicine represents a vital step toward developing a more accessible, cost-effective, and patient-focused healthcare system. With sustained research, technological innovation, and responsible global collaboration, AI-assisted telemedicine has the potential to enhance preventive care, promote early diagnosis, and significantly elevate the overall quality of life for individuals worldwide.

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15. Skin vision:AI Based skin cancer detection application 2025.Available from https://www.skinvision.com/ (Accessed on: 18 October 2025)

16. Diab Trend: Smart Diabetes App for Diabetes Management 2025 Available from https://diabtrend.com/ (Accessed on: 18 October 2025)

17. Circadian AI: - AI Powered Heart Analysis Technology Application 2025 Available from https://www.circadian-ai.com/(Accessed on: 18 October 2025)

18. Path AI: - AI Powered Cancer Detection Pathology Application 2025 Available from https://www.pathai.com/(Accessed on: 18 October 2025)

Received on 21.10.2025 Revised on 24.11.2025

Accepted on 26.12.2025 Published on 30.01.2026

Available online from February 05, 2026

Res. J. Pharma. Dosage Forms and Tech.2026; 18(1):83-89.

DOI: 10.52711/0975-4377.2026.00014

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