https://ijmri.de/index.php/jmsi
volume 4, issue 6, 2025
352
ARTIFICIAL INTELLIGENCE IN MEDICINE: NEW OPPORTUNITIES IN
DIAGNOSIS AND TREATMENT
Bahromova Sarvinoz Sherzotbek kizi
Student of the Medical Department of Kokand University Andijan Branch
[Contact: bahromova.010@gmail.com]
Abstract:
This article analyzes the application of artificial intelligence (AI) technologies in
medicine, particularly their effectiveness in diagnostic and therapeutic processes. Based on
globally recognized scientific studies and literature, the capabilities, advantages, and challenges
of AI are presented. The impact of AI on medical ethics and its future prospects are also
discussed.
Keywords:
Artificial intelligence, diagnosis, medical technologies, algorithms, healthcare,
digital medicine.
Relevance of the topic:
21st-century medicine is rapidly evolving and closely linked to modern technologies. One of the
most critical and promising directions is the integration of artificial intelligence (AI) into the
healthcare system. Today, AI technologies enable early disease detection, treatment planning,
and even the execution of complex surgical procedures. Global research shows that with the help
of AI, diagnostic accuracy can reach 90–95%. For example, algorithms developed by researchers
at Stanford University (USA) have demonstrated higher accuracy than experienced
dermatologists in detecting diseases such as skin cancer. Additionally, in Eric Topol’s book
“Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again”, ways of
strengthening, rather than losing, the human connection between doctor and patient with the help
of AI are presented [5].
Artificial Intelligence: A New technological turn in Medicine
The application of AI technologies in medicine has radically transformed diagnostic and
treatment processes in recent years. AI is primarily used to process large volumes of data,
analyze them, and make accurate decisions. The main areas of AI application in medicine
include diagnosis, treatment, monitoring, and healthcare management. For example, AI
technologies demonstrate excellent results in the automatic analysis of radiological images using
various software and devices. AI is also employed in drug discovery through scientifically based
approaches. Worldwide, many hospitals and clinics are using AI for faster diagnosis and
treatment management in practice.
AI capabilities in the diagnostic process:
AI has led to significant changes in medical diagnostics. AI systems in radiological imaging—
such as X-rays, computed tomography (CT), and magnetic resonance imaging (MRI)—achieve
high accuracy in disease detection. For instance, Google’s AI program showed very successful
results in detecting breast cancer. If AI identified the disease with 99% accuracy, physicians’
accuracy was 88%. Moreover, AI-based image analysis technologies reduce human errors during
https://ijmri.de/index.php/jmsi
volume 4, issue 6, 2025
353
image interpretation, ensuring more accurate and faster diagnoses.
AI approaches in treatment processes:
Artificial intelligence plays a major role in medical treatment, particularly in designing
personalized treatment plans. For example, personalized treatment methods are being developed
using genomic data. These methods allow for the selection of medications and optimization of
treatment methods based on an individual’s genetic characteristics. Additionally, surgical
procedures performed with the assistance of robotics are also improving through AI. For
example, da Vinci robots enable the execution of complex surgical operations with even greater
precision.
Advantages and efficiency indicators of AI:
AI technologies provide several advantages in medicine. First, they offer the ability to analyze
medical images with high accuracy and speed. For example, AI diagnostic systems have
achieved 95% accuracy in detecting bladder cancer [1]. Second, decision-making in medicine is
accelerated with the help of AI, allowing for faster patient care. As a result, patients’ survival
rates increase. Third, AI systems enable effective management of medical services, improving
the overall efficiency of healthcare systems. However, the widespread use of AI systems raises
some challenges. Primarily, accepting AI decisions without physician verification can lead to
errors and adverse consequences. Additionally, if incorrect data or faulty algorithms are used in
AI systems, results may be inaccurate [6]. Privacy issues also require significant attention. The
security of patient data must be ensured. Furthermore, from an ethical standpoint, AI systems are
important when considering responsibility for human health, which traditionally lies with
physicians.
Global experiences: AI in practice
Today, artificial intelligence is successfully applied in medical practice in several advanced
countries. For example, in the USA, IBM’s “Watson for Oncology” project provides
recommendations for the diagnosis and treatment of oncological diseases. Watson, relying on its
extensive medical database, analyzes the patient’s condition and develops individualized
treatment strategies [2]. Created in collaboration with the Memorial Sloan Kettering Cancer
Center, this system assists physicians in clinical decision-making. Google Health developed an
algorithm using AI to detect breast cancer. According to a study published in Nature journal in
2020, this system, compared to physicians in the USA and the UK, increased diagnostic accuracy
and significantly reduced false positives and false negatives [3]. In the UK, the National Health
Service (NHS) uses AI to analyze MRI and CT images [4]. This helps provide faster diagnoses
and reduces waiting times. Additionally, DeepMind’s AI algorithm for detecting retinopathy has
shown high effectiveness in early-stage diagnosis of eye diseases. These experiences indicate
that artificial intelligence is becoming an important tool not only in diagnostics but also in
medical decision-making, treatment management, and healthcare system digitalization.
Conclusion:
Artificial intelligence opens new opportunities in healthcare. It optimizes diagnostic and
treatment processes, but it is important to use it correctly and cautiously. In the future, AI may
become an integral part of healthcare systems, but human values must be preserved.
References
1. Esteva, A. et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 24–29.
https://ijmri.de/index.php/jmsi
volume 4, issue 6, 2025
354
2. Rajpurkar, P. et al. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-
Rays with Deep Learning. arXiv preprint.
3. Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human
Again.
4. Nature (2020). International evaluation of an AI system for breast cancer screening. Nature,
577, 89–94.
5. IBM Watson Health. “Watson for Oncology”. IBM official website.
6. DeepMind Technologies. “AI for Eye Disease Detection”. DeepMind official website.
