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The Role of Artificial Intelligence in Enhancing Disease Diagnosis and Treatment

نقش هوش مصنوعی در ارتقاء تشخیص و درمان بیماری‌ها

Introduction

Artificial intelligence is now one of the most powerful tools for transformation in the medical industry. This technology, by combining complex machine learning and deep learning algorithms, has been able to provide innovative solutions to complex challenges in healthcare. From diagnosing diseases with high accuracy to designing personalized treatment plans, AI plays a vital role in improving the quality of patient care.
According to the US Food and Drug Administration (FDA), by August 2024, approximately 950 medical devices equipped with artificial intelligence and machine learning have been approved, most of which are designed to assist in diagnosing and identifying treatable diseases. This technology has not only improved the quality of patient care but has also helped reduce costs and increase access to healthcare services.
In this article, we comprehensively examine the role and impact of artificial intelligence in healthcare and analyze the latest developments in this field.

1. Disease Diagnosis with AI: Unprecedented Accuracy

1.1. Medical Imaging and Automated Diagnosis

One of the most prominent applications of artificial intelligence in medicine is the use of deep learning algorithms and Convolutional Neural Networks (CNN) for analyzing medical images. These systems can analyze radiography, MRI, CT scan, and other imaging methods with accuracy beyond human capability.
AI-based diagnostic tools in cancer detection have reached a 93% matching rate with expert recommendations. In the UK, all 107 stroke treatment centers use AI tools such as Brainomix 360 and RapidAI for instant CT image analysis, which has led to significant improvement in decision-making time and treatment outcomes.
A study in the UK showed that an AI tool can identify 64% of epilepsy-related brain lesions that were previously overlooked by radiologists. This tool, trained on MRI scans of more than 1,100 adults and children, can detect small or hidden lesions faster than physicians.

1.2. Medical Data Analysis and Disease Prediction

Artificial intelligence, by analyzing big medical data, can predict diseases based on patterns in the data. Using medical data from 500,000 people in the UK, an AI system was able to predict disease diagnosis with high confidence years before clinical symptoms appeared.
These systems can identify signs in individuals that are highly predictive of diseases such as Alzheimer's, chronic obstructive pulmonary disease, kidney disease, and many others. These early predictions help physicians take preventive actions sooner.
In Johns Hopkins Hospital's collaboration with Microsoft Azure AI, artificial intelligence algorithms were trained to predict patient outcomes, including disease progression, readmission risk, and response to treatments, which significantly improved patient care.

1.3. Genomic Analysis and Precise Cancer Diagnosis

Artificial intelligence in genomic analysis plays a revolutionary role. AI algorithms can rapidly process vast amounts of genomic data and identify rare genetic variants and their potential impact on patient care.
For example, AI analysis of hundreds of exomes in medulloblastoma cases identified specific molecular subgroups that allow physicians to prescribe precise treatment doses. This approach dramatically increases treatment precision.

2. Designing Personalized Treatment Plans: The Era of Personalized Medicine

2.1. AI-Powered Personalized Treatments

Each patient has unique characteristics requiring a specific treatment plan. Artificial intelligence can provide completely personalized treatment plans by analyzing genetic data, medical history, lifestyle, and other factors.
In the field of pharmacogenomics, artificial intelligence identifies genetic markers and helps develop comprehensive models by analyzing complex relationships between genetic factors and drug responses. This approach ensures that treatments are performed more efficiently and side effects are reduced.
The global personalized medicine market is growing rapidly. The pharmacogenomics segment with a 30.2% share is the largest segment of the personalized medicine market, while the personalized treatments segment is experiencing the fastest growth with a compound annual growth rate of 10.2%.

2.2. Precision Medicine in Oncology

In oncology, artificial intelligence plays a transformative role. AI-based diagnostic tools can identify mutations through genomic analysis and personalize treatments based on each patient's unique cancer profile.
The MoST program in Australia has shown that genomic screening identifies actionable genetic biomarkers in 75% of patients with rare cancers and provides access to targeted therapies, improving outcomes. The global precision oncology market was valued at approximately $115.8 billion in 2024 and is projected to grow at a CAGR of 8.05% until 2030.

3. AI-Powered Robotic Surgery: Extraordinary Precision

3.1. Remarkable Advances in Robotic Surgery

AI-powered robotic surgery is one of the most exciting applications of this technology in medicine. A systematic review of 25 recent studies (2024-2025) showed that AI-assisted robotic surgeries achieve:
  • 25% reduction in surgery time
  • 30% reduction in intraoperative complications
  • 40% improvement in surgical precision
  • 15% reduction in patient recovery time
These advances are remarkable compared to traditional surgical methods and demonstrate that artificial intelligence and robotics can significantly enhance surgical quality.

3.2. Autonomous Surgical Systems

In an unprecedented breakthrough, Johns Hopkins University researchers performed the first real surgery without human assistance. The SRT-H (Hierarchical Surgical Robot Transformer) system, built on a machine learning architecture similar to ChatGPT, can:
  • Adapt to individual anatomical features
  • Make real-time decisions
  • Perform self-correction
  • Respond to voice commands
This robot learned how to perform gallbladder removal by watching surgeon videos and performed the surgery with 100% accuracy.

3.3. Emerging Technologies in Robotic Surgery

Three key technologies are transforming robotic surgery:
1. Digital Twin-Assisted Surgery: This technology creates a virtual version of the patient's anatomy, allowing surgeons to plan and simulate the operation before entering the operating room.
2. Neurovisual Adaptive Control Systems: These systems use AI-based neural and visual feedback to adjust robotic movements in real-time, increasing precision in delicate procedures such as neurosurgery.
3. Augmented Reality (AR) Applications: This technology overlays digital information onto the actual surgical field, giving surgeons better vision.

4. Hospital Management and Efficiency with AI

4.1. Optimizing Hospital Resources

Resource management in hospitals is a major challenge that artificial intelligence can help improve. From schedule optimization to better equipment and staff management, AI has helped achieve a 10% reduction in healthcare costs and a 20% increase in workflow efficiency.
AI systems can:
  • Determine optimal scheduling for operating rooms
  • Optimize the use of medical equipment
  • Reduce waiting times
  • Increase patient satisfaction

4.2. AI-Powered Clinical Documentation

One of the most time-consuming tasks for physicians is preparing clinical notes. AI systems such as Dragon Ambient eXperience (DAX) Copilot, used by over 150 healthcare systems, can:
  • Automatically document patient visits
  • Generate discharge summaries
  • Reduce documentation errors
  • Free up hours of physician time
In Germany, the Elea AI platform has reduced testing and diagnosis time from weeks to hours.

4.3. Clinical Decision Support Systems (CDSS)

CDSS tools provide evidence-based and personalized recommendations by analyzing patient data, including genetic, molecular, and clinical information. These tools:
  • Reduce diagnostic errors
  • Optimize treatment selection
  • Consider individual risk profiles
For example, Mayo Clinic uses AI to analyze low-dose CT scans to detect lung cancer by identifying potential tumors and reducing unnecessary biopsies and surgeries.

5. Improving Preventive Care: From Reactive to Proactive

5.1. Continuous Monitoring and Early Warnings

Smart wearable devices and sensors can continuously monitor patients' health status. Artificial intelligence, by analyzing this information, can:
  • Identify abnormal patterns
  • Issue early warnings about health status
  • Help prevent diseases
  • Facilitate early interventions
In the UK, mental health AI chatbots like Wysa are being tested in NHS services, while ElliQ companion robots in New York's program have shown a 95% reduction in loneliness among the elderly.

5.2. AI-Based Health Recommendations

Artificial intelligence can provide personalized recommendations by analyzing health data and lifestyle:
  • Dietary changes
  • Appropriate exercise programs
  • Stress management
  • Sleep pattern improvement
This preventive approach can increase quality of life and reduce the need for advanced medical care.

6. Drug Discovery and Development: A Revolution in the Pharmaceutical Industry

6.1. Faster Identification of New Drugs

Artificial intelligence has dramatically accelerated the drug discovery process. Companies like Insilico Medicine use generative AI to identify promising drug candidates.
Their Chemistry42 AI engine has discovered drug candidates for diseases such as fibrosis that are now in clinical trials. This AI-based approach:
  • Reduces the number of compounds needed for synthesis
  • Makes personalized treatments more cost-effective
  • Reduces drug development time

6.2. Predicting Efficacy and Safety

Machine learning algorithms can predict the efficacy and safety of new drugs, identify therapeutic targets, and design clinical trials. This capability helps:
  • Reduce drug failure risk: By simulating drug effects in early stages, compounds with high success probability are identified, preventing investment in doomed-to-fail drugs.
  • Lower development costs: Reducing the need for expensive laboratory and animal testing, and accelerating the development process, creates millions of dollars in savings.
  • Accelerate time to market: Reducing drug discovery and development time from 10-15 years to 3-5 years helps critical drugs reach patients in need faster.

7. Challenges and Future Considerations

7.1. Data Quality and Generalizability

One of the biggest challenges is the need for diverse and labeled datasets. Most machine learning models require diverse data, but current resources are limited and often too restricted to ensure generalizability.

7.2. Ethical Issues and Privacy

The use of artificial intelligence in medicine raises concerns about:
  • Patient data privacy: Storing and processing vast amounts of sensitive medical information increases the risk of privacy breaches and data misuse, requiring strong security systems.
  • Liability and accountability: In case of errors in diagnosis or treatment by AI systems, it's unclear who is responsible - the algorithm developer, the hospital, or the physician.
  • Algorithmic bias: If training data doesn't represent the entire population, AI systems may perform poorly for some groups and create discrimination.
  • Equity in technology access: Concerns that only wealthy individuals or residents of developed countries will benefit from AI advantages, deepening the global health gap.

7.3. Initial Costs and Infrastructure

While AI can help reduce costs in the long term:
  • Initial setup costs are high: Purchasing powerful hardware, specialized software, and setting up necessary infrastructure requires significant investment.
  • Advanced technology infrastructure is needed: AI systems require powerful servers, extensive storage space, high-speed internet connection, and advanced security systems.
  • Staff training is required: Hospital staff and physicians must undergo extensive training to effectively use AI tools, which is both time-consuming and costly.
  • Concerns about access equity exist: Large and wealthy hospitals can benefit from this technology sooner, while small centers and underserved areas may lag behind for years.

7.4. Model Interpretability

Many deep learning models operate as "black boxes," making it difficult to understand how they arrive at their decisions. This is challenging in medicine where transparency is critical.

8. The Future of AI in Medicine

8.1. More Advanced Automated Systems

In the future, we may see the emergence of autonomous AI diagnostic systems that can perform specific diagnostic tasks with minimal human supervision. While human oversight will remain essential for complex cases and critical decisions, automated systems can help address healthcare access challenges in underserved areas.

8.2. Multi-Source Integration

The future of personalized medicine lies in integrating data from various sources:
  • Electronic health records
  • Genomic data
  • Immunological information
  • Wearable device data
  • Treatment history
This integration helps create a comprehensive picture of each patient's health.

8.3. Human-AI Collaboration

The future of medicine lies in collaboration between physicians and AI, not replacement. Artificial intelligence can:
  • Act as a physician assistant: Providing evidence-based recommendations, clinical reminders, and decision support without taking the final decision away from the physician.
  • Automate repetitive tasks: Performing time-consuming tasks such as recording information, completing forms, and documentation that take significant time from physicians.
  • Keep physicians focused on complex cases: By delegating simple tasks to AI, physicians can spend more time and energy on complex cases requiring human judgment.
  • Improve clinical decision-making: Providing comprehensive information, accurate analyses, and multiple treatment options that help physicians make the best decisions.

8.4. Predictive and Preventive Medicine

With advances in predictive models and machine learning, medicine is moving toward a more preventive approach. This means:
  • Diagnosing diseases before symptoms appear: Identifying biomarkers and subtle patterns that show disease risk years before clinical symptoms emerge.
  • Early interventions to prevent disease progression: Implementing targeted preventive actions such as lifestyle changes, preventive medications, or regular monitoring to prevent disease progression.
  • Reducing treatment costs: Preventing advanced diseases that require expensive and long-term treatments results in significant savings.
  • Improving quality of life: Maintaining health and preventing debilitating diseases dramatically increases quality of life.

9. Successful Examples of AI Implementation in Medicine

9.1. Mayo Clinic Medical Center

Mayo Clinic uses artificial intelligence in several areas:
  • Early detection of lung cancer by analyzing CT scans
  • Reducing unnecessary biopsies and surgeries
  • Improving diagnostic accuracy

9.2. NHS Healthcare Network in the UK

NHS with extensive AI implementation:
  • All 107 stroke treatment centers use AI tools
  • Significant reduction in decision-making time
  • Improved patient treatment outcomes

9.3. Johns Hopkins University

This university is a pioneer in robotic surgery:
  • Performing the first fully autonomous surgery
  • Developing the SRT-H system
  • 100% accuracy in experimental surgeries

10. The Role of Complementary Technologies

10.1. Deep Learning and Neural Networks

Deep neural networks and Recurrent Neural Networks (RNN) play a key role in processing medical data. These technologies can:
  • Identify complex patterns: Detecting non-linear and complex relationships in medical data that are not observable to humans, such as specific combinations of symptoms indicating rare diseases.
  • Provide accurate predictions: Predicting treatment outcomes, disease progression, and complication probability based on comprehensive analysis of clinical and laboratory data.
  • Learn from historical data: Continuously improving performance by learning from millions of previous cases and automatically updating with new information.

10.2. Natural Language Processing

Natural Language Processing (NLP) plays a vital role in analyzing clinical notes and medical documents:
  • Extracting information from medical texts: Automatic analysis of medical reports, clinical notes, and scientific articles to extract useful and structured information.
  • Generating automatic reports: Creating clinical summaries, discharge reports, and medical documentation automatically from patient data and physician-patient conversations.
  • Facilitating physician-patient communication: Using smart chatbots to answer common patient questions, schedule appointments, and provide initial guidance.

10.3. Machine Vision

Machine vision and image processing are essential in analyzing medical images:
  • Automatic detection of abnormalities: Fast and accurate identification of tumors, lesions, fractures, and other abnormalities in medical images without the need for lengthy manual review.
  • Precise measurement of lesions: Automatic calculation of the size, volume, and quantitative characteristics of lesions, which is vital in assessing disease severity and response to treatment.
  • Tracking disease progression: Comparing images over time for objective evaluation of disease progression or improvement and treatment effects.

10.4. Reinforcement Learning

Reinforcement learning is applied in optimizing treatment plans:
  • Finding the best treatment strategy: Reinforcement learning algorithms identify the best treatment combination for each patient based on previous responses through trial and error.
  • Adjusting drug dosages: Smart systems can dynamically adjust drug doses and determine the optimal amount based on patient response.
  • Optimizing treatment timing: Determining the best time to administer medications and therapeutic interventions for maximum effectiveness and minimum side effects.

11. Skills Required for Working with AI in Medicine

11.1. For Physicians

Future physicians need new skills:
  • Understanding basic AI concepts: Familiarity with how machine learning algorithms work, their limitations and capabilities is essential for effective interaction with AI systems.
  • Interpreting AI system outputs: The ability to understand and evaluate diagnostic results and treatment recommendations provided by artificial intelligence systems.
  • Critically evaluating AI recommendations: The skill to recognize when to follow AI recommendations or make different decisions based on clinical experience.
  • Effectively collaborating with AI systems: Learning how to optimally use AI tools as assistants rather than replacements to improve medical decision-making quality.

11.2. For Technology Professionals

AI professionals in medicine need:
  • Deep understanding of medical challenges: Familiarity with clinical processes, physicians' and patients' needs, and precise understanding of real problems that need solving.
  • Familiarity with medical ethical principles: Respecting patient privacy, the principle of non-harm, and understanding ethical responsibilities in developing medical systems.
  • Proficiency in Python programming: Mastery of the main programming language in data science and machine learning for developing AI models.
  • Mastery of TensorFlow and PyTorch: Proficiency in using advanced deep learning tools for building and training complex medical models.

12. Economic and Social Impact

12.1. Reducing Healthcare Costs

Artificial intelligence can help significantly reduce healthcare costs:
  • 10% reduction in operational costs: By optimizing processes, smart resource management, and reducing time waste, AI can significantly reduce hospitals' daily expenses.
  • Reducing unnecessary tests: Highly accurate smart diagnostic systems reduce the need for repetitive or unnecessary tests and prevent additional costs.
  • Increasing treatment efficiency: By personalizing treatments and more precisely selecting medications, treatment success rates increase and the need for repeated treatments decreases.
  • Preventing advanced diseases: Early detection of diseases before their progression prevents heavy costs of complex and long-term treatments.

12.2. Broader Access to Services

AI technology can:
  • Bring medical services to remote areas: AI-based diagnostic systems can provide high-quality diagnostic services in areas lacking specialist physicians.
  • Facilitate access to specialists: Smart telemedicine platforms enable remote consultation with specialists and eliminate the need for long travels.
  • Reduce healthcare costs: By reducing the need for in-person visits, repetitive tests, and hospitalizations, healthcare costs for patients decrease.
  • Standardize service quality: Standard AI algorithms ensure that all patients, regardless of geographic location, benefit from the same quality level in diagnosis and treatment.

12.3. Creating New Jobs

While some worry about job replacement, AI creates new jobs:
  • AI specialists in medicine: Individuals who work at the intersection of artificial intelligence and medicine and develop diagnostic and therapeutic algorithms.
  • Medical data analysts: Experts who analyze big health data and identify disease patterns, risk factors, and treatment outcomes.
  • Smart healthcare system designers: Specialists who design and implement integrated health management systems with AI capabilities.
  • Health IT managers: Individuals who manage hospital and medical center IT infrastructure and ensure AI systems integration.

Conclusion

Artificial intelligence is creating a fundamental transformation in healthcare. From diagnosing diseases with extraordinary accuracy to designing completely personalized treatment plans, from robotic surgeries with 100% precision to optimal hospital resource management, AI plays a vital role in improving quality and access to healthcare services.
Given recent remarkable advances - from FDA approval of over 950 AI-equipped medical devices to performing the first fully autonomous surgery - the future of medicine will increasingly depend on artificial intelligence. This technology not only helps better diagnose and treat diseases but also, with a preventive and predictive approach, can identify many diseases before symptoms appear and prevent their progression.
Of course, challenges such as data quality, ethical issues, privacy, and initial costs still exist and need attention and resolution. But with collaboration between physicians, technology professionals, policymakers, and society, these challenges can be overcome and the full potential of artificial intelligence can be leveraged to save patients' lives and improve their quality of life.
The future of medicine is a future where humans and artificial intelligence work together and complement each other - physicians with their human experience and empathy, and artificial intelligence with its power to analyze big data and extraordinary precision - to provide the best possible care for each patient.