Artificial Intelligence in Medicine: New Horizons in Diagnosis and Treatment

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In recent years, artificial intelligence (AI) has significantly transformed the process of diagnosis and treatment worldwide. It continues to advance, improving the quality of medical care, making it more accessible and efficient.

According to CB Insights, one of the largest analytical platforms, AI sector funding more than doubled in the past year.

The Association of American Medical Colleges (AAMC) predicts a severe shortage of physicians, expected to peak by 2034. In a situation of staff shortage, artificial intelligence can not only support but also increase the productivity level of medical professionals. Additionally, it can improve patient outcomes by identifying serious risks and diseases at early stages.

Innovations in Diagnosis

Diagnosis plays a crucial role in determining treatment protocols. AI-based technologies can analyze medical histories, instrumental, and laboratory studies—tasks traditionally performed by physicians. AI is widely used in oncology, cardiology, ophthalmology, and the detection of rare genetic diseases. Below we look at key innovations and perspectives on the use of AI in performing diagnostic procedures.

Oncology

Malignant tumors remain one of the leading causes of morbidity and mortality worldwide. According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), approximately 15 million new cancer cases and 10 million cancer-related deaths are recorded annually. In 2020, newly diagnosed cancer cases reached 19.3 million.

Artificial intelligence has become an essential tool in oncology diagnostics, significantly improving accuracy and speed. AI can detect early cancer manifestations in X-rays, computed tomography (CT), and magnetic resonance imaging (MRI) scans, as well as differentiate between tumors and healthy tissues.

Statistical Data and Recent Studies

• Breast Cancer: A study published in Nature demonstrated that AI algorithms detected breast cancer in mammograms with 94.6% accuracy, compared to 88% by radiologists.
• Lung Cancer: Research by Google Health revealed that an AI-based CT image analysis system detects early-stage lung cancer with 97% accuracy, far exceeding the results of traditional methods.
• Dermatology (Melanoma): Annals of Oncology reported that AI diagnosed melanoma from dermoscopic images with 91% accuracy, comparable to expert oncologists (89%).

Successful AI Systems

• Google DeepMind has enhanced breast cancer diagnostics. A study published in Nature showed that AI identifies tumors more accurately than physician screenings, reducing false-positive and false-negative results by nearly 10% in the U.S. and 3% in the U.K. The American Society of Clinical Oncology reports that radiologists may miss up to 20% of malignant breast tumors.
• IBM Watson is a powerful AI system used in oncology to improve cancer diagnosis and treatment. Its primary role is supporting physicians in clinical decision-making. The system can quickly process and correlate information about tumour types and individual patient characteristics.
• IBM Watson Health has launched the latest service for analysing the genomic composition of tumours – IBM Watson Genomics. In this way, doctors now have a tool to identify mutations and genetic markers that influence the choice of therapy.

Cardiology

According to WHO, cardiovascular diseases remain the leading cause of mortality worldwide. AI in this field enhances diagnostics and medical care efficiency. AI can identify patients at high risk of heart attack or stroke and immediately alert physicians. Experts estimate that AI-based predictive analytics can reduce cardiovascular disease risk by 20-30%.

Research published in Frontiers in Medicine indicates that AI systems can analyze patient genotypes, enabling a more precise assessment of long-term complications from prescribed therapies and reducing recurrence and complications.

Electrocardiogram (ECG) Analysis

ECGs reflect the heart’s electrical activity. It can detect signs of myocardial infarction, atrial and ventricular hypertrophy indicating increased muscle work, rhythm disturbance and many other pathological changes. Due to physician workload, early warning signs often go unnoticed. In this case, AI is able to detect hidden signs of heart disorders by analysing not only the shape of waves, their duration and amplitude, but also the frequency of signal oscillations at each point.

A newly developed AI system identifies myocardial dysfunction before severe heart changes occur. With 94% accuracy, it detects impaired myocardial relaxation, a predictor of hypertension-induced overload or early coronary artery disease (CAD), which may lead to heart attacks. AI can also assess physical or emotional cardiac stress based on ECG data.

Ophthalmology

Retinopathy, including diabetic and age-related macular degeneration, is a leading cause of blindness worldwide. WHO reports that around 422 million people have diabetes, with diabetic retinopathy cases rising annually.

AI algorithms have revolutionized the interpretation of diagnostic imaging techniques like ophthalmoscopy and fluorescein angiography, traditionally requiring highly skilled specialists. AI achieves up to 95% diagnostic accuracy based on retinal images, demonstrating its potential in ophthalmology. A study published in JAMA Ophthalmology showed that AI successfully classified diabetic retinopathy severity.

Retina.AI, a special software system based on automatic segmentation algorithms, is now able to assess the severity of retinopathy and other retinal diseases. The advanced AI system determines the stage of retinopathy according to the International Classification and provides a report on the results of analysing the ocular fundus image in a format convenient for the doctor.

Rare Diseases

According to Rare Diseases International, 1 in 2,000 people has a rare disease. Diagnosing such conditions is challenging, often taking years to reach an accurate diagnosis. AI accelerates this process by analyzing genetic markers and comparing them with known diseases.

• British developers have created the latest AlphaMissense tool for predicting gene disorders based on mutation analysis, namely, how the replacement of one link in the genetic code will affect the entire DNA. In 89% of cases, the AI correctly recognises inaccuracies in the DNA strand sequence and determines the risk of a particular rare pathology.
• Peking Union Medical College Hospital (PUMCH) and the Institute of Automation of the Chinese Academy of Sciences have introduced an AI language model for diagnosing rare diseases. It is able to scan a patient’s symptoms and diagnose genetic diseases including Rett and Angelman syndromes in seconds. The language model is also capable of creating a treatment plan and suggesting necessary tests according to the results.

Innovations in Treatment

Machine learning algorithms can not only make the right diagnosis, sometimes better than doctors, but also prescribe personalised therapy. Leading global clinics are using top AI systems for more effective and targeted therapies.

Hospitality MedService experts provide insights into which clinics worldwide offer cutting-edge AI-driven diagnostic and therapeutic technologies. AI innovations have touched all areas of disease treatment, but the most recent activity has been in robotic surgery, targeted therapy and rehabilitation. Let us discuss these areas below.

Robotic Surgery

Surgical interventions using robotic systems like Da Vinci are increasingly popular. However, even advanced robots require skilled surgeons. AI-based safety systems monitor surgical procedures remotely, protecting both patients and physicians from emergencies such as system failures. It will stop the operation or continue it without the doctor’s involvement. The AI system is self-contained, with no network access, allowing it to be used even during power outages.

The world’s leading clinics use AI for remote neurosurgical and endovascular operations. It simulates vessels in 3D, senses and compensates for the surgeon’s finger tremors, and reacts instantly to critical situations.

Targeted Therapy

Scientists at the international Ludwig Cancer Research have developed an AI system to identify the immune cells that are most effective at destroying a cancerous tumour. The system is called TRTpred, and can be used for personalised treatment by tailoring therapy to each patient’s cellular composition.

AI algorithms in this case do not just look for mutations in genes, but compare them with data preloaded into the system on the selection of effective drugs for the treatment of cancer pathologies. For example, if a tumour has been found to have a mutation in the EGFR gene, the algorithm recommends targeted drugs that have already shown their effectiveness in clinical trials.

Rehabilitation

Rehabilitation is a complex, multi-stage process that requires an individualised approach to each patient. AI systems make an invaluable contribution to the personalisation of rehabilitation measures: they analyse data on the course of treatment, physical activity and adjust loads. Often, the final process of rehabilitation takes place at home, where there is no medical staff, so AI is particularly relevant at this stage as well.

AI Applications in Rehabilitation

• Post-Stroke Recovery: Exoskeletons and robotic devices help patients regain motor functions. AI monitors movements in real time and provides feedback for exercise correction.
• Chronic Pain Management: AI analyzes pain patterns and recommends personalized treatments (physiotherapy or medication). Pain management becomes more effective.
• Spinal Cord Injury Rehabilitation: Patients with such injuries are complex, sometimes requiring long-term supportive therapy. AI creates personalised programmes for them, monitoring their condition and progress in treatment.
• Psychological Rehabilitation: Virtual reality is becoming an important tool for working with anxiety, depression and post-traumatic stress disorder. AI-based systems can create virtual therapy scenarios, allowing patients to safely work through their fears and experiences.

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The text has been verified

Verified by: Marianna Sharipova, Certified Physician, Medical Director of HMS, with 20 years of experience in medical tourism.

Author: Anna Kukarkina, MD