AI enables rapid and highly accurate analysis of medical images such as MRI, CT scans, and X-rays, providing substantial support to radiologists in the early and precise detection of diseases such as cancer, tumors, and structural abnormalities. This technology can identify subtle patterns that may be imperceptible to the human eye, thereby significantly improving diagnostic accuracy [8].

AI-powered surgical robots enhance the precision and control of surgeons during complex procedures. These systems can filter out hand tremors and enable microscale surgical interventions, leading to reduced patient recovery time and minimized postoperative complications [9].

AI-based systems analyze large volumes of patient data, including medical records, laboratory results, and genetic information, to assist physicians in making more informed and personalized treatment decisions. These systems can recommend potential treatment options and predict which therapeutic approach is likely to be most effective for a specific patient [10].

Wearable devices and smart sensors equipped with AI continuously monitor patients’ vital signs and are capable of detecting abnormal patterns or early indicators of clinical deterioration. This allows healthcare teams to intervene promptly and prevent serious adverse events, particularly in chronic or intensive care patients [11].

AI accelerates the analysis of molecular and genetic data, facilitating the identification of new drug targets and predicting the efficacy and potential side effects of candidate compounds. This significantly shortens the costly and time-consuming process of drug research and development [12].

AI can analyze data related to patient admissions, length of hospital stay, patient flow across departments, and equipment inventory to help hospital administrators optimize resource allocation, predict patient surges, and reduce waiting times. This results in improved operational efficiency and reduced unnecessary costs [13].

AI-powered rehabilitation robots can design and implement personalized therapy programs for patients recovering from stroke, spinal cord injuries, or other motor disorders. By monitoring patient progress and automatically adjusting the intensity and type of exercises, these systems support faster and more effective functional recovery [14].

AI can process and analyze large-scale genomic and clinical datasets to identify patterns associated with rare and genetic diseases, which are often difficult and time-consuming to diagnose. This enables earlier diagnosis and timely initiation of appropriate treatment strategies [15].

Despite significant advancements, developed countries continue to face serious challenges regarding AI ethics and the protection of sensitive patient data. Ensuring that algorithms operate without bias and that patient data are used securely and responsibly requires robust and continuously updated legal and regulatory frameworks. Concerns about data breaches and potential misuse remain significant barriers to the widespread adoption of certain applications [16].

Many advanced AI models, particularly deep neural networks, function as black boxes. This means that understanding how they arrive at specific decisions is often difficult. For physicians and patients, trusting decisions made by systems that cannot fully explain their reasoning represents a major challenge. Improving model interpretability is essential to building trust and facilitating clinical adoption [17].

Although the ultimate goal of AI is to reduce healthcare costs, the initial deployment of advanced systems, acquisition of necessary hardware and software, workforce training, and ongoing maintenance require substantial financial investment. These costs can represent a barrier even for healthcare institutions in developed countries, necessitating innovative financial and economic models [18].

The adoption of new technologies, particularly in a tradition-based profession such as medicine, is often met with resistance. Physicians, nurses, and other clinical staff require extensive and continuous training to understand the capabilities, limitations, and proper use of AI-based medical equipment. Overcoming this cultural resistance and investing in education are key factors for successful implementation [19].

Developed countries, benefiting from robust infrastructure and access to large-scale datasets, have exceptional potential to enhance both the accuracy and speed of disease diagnosis. AI algorithms can elevate the analysis of medical imaging, pathology data, and genetic results to previously unattainable levels of precision, enabling earlier diagnosis and more effective treatment strategies [20].

Access to genomic, lifestyle, and clinical history data enables the design and delivery of highly personalized treatment strategies. AI can analyze these datasets to determine the most appropriate therapeutic approach for each individual, optimize drug efficacy, and minimize side effects, thereby revolutionizing the field of precision medicine [21].

Developed healthcare systems, characterized by their complexity, increasingly rely on AI to optimize medical supply chains, manage surgical scheduling, predict hospital demand, and reduce patient waiting times. These improvements result in enhanced operational efficiency, reduced costs, and improved overall patient experience [21].

A strong innovation ecosystem in developed countries, combined with substantial investment in research and development, creates unique opportunities for the advancement of next-generation AI-based medical devices. Collaboration among universities, technology companies, and healthcare institutions accelerates the translation of innovative ideas into clinically effective solutions [22].

AI enhances telemedicine platforms, facilitating access to specialized healthcare services for individuals in remote or underserved regions, even within developed countries. Intelligent diagnostic tools and remote decision-support systems help ensure consistent quality of care regardless of geographical location [23].

One of the major challenges is the limitation of robust and stable Information and Communication Technology (ICT) infrastructure in certain regions and healthcare centers. In addition, the collection, standardization, and accessibility of high-quality and sufficiently large medical datasets (essential for training AI models) are hindered by fragmented hospital information systems and the absence of unified protocols.

The absence or slow development of clear regulations and guidelines for the evaluation, approval, and deployment of AI-based medical equipment represents a significant barrier. The lack of transparency in licensing pathways and the absence of national standards aligned with emerging technologies may slow down investment and innovation in this field.

A shortage of professionals at the intersection of AI and healthcare, including data scientists with medical knowledge, biomedical engineers proficient in AI, and physicians capable of working with intelligent systems, constitutes a critical challenge. The development of comprehensive and up-to-date educational programs in universities and research centers is essential for training qualified human resources.

The procurement of advanced AI hardware and software can be highly costly due to sanctions and currency fluctuations. Justifying these investments for hospital administrators and policymakers, particularly under budget constraints, requires the development of sustainable business models and clear evidence of return on investment.

Potential resistance to the adoption of new technologies among healthcare staff and patients, as well as the need for organizational cultural change within hospitals to adapt to data-driven and AI-based approaches, represent additional challenges that must be addressed.

Despite the challenges, Iran has considerable scientific and research capacity in the fields of AI and medicine. This potential provides an opportunity to develop and localize AI-based solutions tailored to the specific needs and conditions of the Iranian healthcare system, which may result in more cost-effective and practical applications.

The necessity to enhance the quality of diagnostic and therapeutic services, increase hospital efficiency, and reduce healthcare costs creates a strong incentive for the adoption of advanced technologies such as AI. AI can offer effective solutions to long-standing challenges within Iran’s healthcare system.

The presence of institutions such as the Vice Presidency for Science and Technology and science and technology parks provides opportunities for financial, institutional, and infrastructural support to knowledge-based companies active in the field of medical AI.

Despite existing barriers, a substantial volume of medical data exists within the country. If properly standardized and securely accessed, this data can serve as a valuable resource for the development of indigenous AI algorithms.

In certain domains, AI enables Iran to bypass intermediate stages of technological development and rapidly achieve higher levels of capability, particularly in applications that do not require large-scale investments in complex physical infrastructure.

As AI algorithms mature, medical equipment will evolve from purely diagnostic tools into predictive systems. This will enable healthcare professionals to identify the risk of chronic diseases in high-risk populations before the appearance of overt clinical symptoms [24].

In the coming years, integrated networks of sensors and wearable devices are expected to emerge, transmitting patients’ vital signs in real time to hospital-based analytical systems. This convergence will significantly enhance the quality of remote healthcare and continuous patient monitoring [25].

With the emergence of AI-based clinical decision support systems, the role of physicians will shift from being primary analyzers of raw data to managers of complex and human-centered decision-making processes. In this model, technology handles repetitive tasks, while physicians focus more on empathetic care and strategic treatment decisions [26].

AI will be capable of optimizing the medical equipment supply chain in the country, ranging from forecasting hospital demand for pharmaceuticals and consumables to intelligent maintenance of advanced medical devices through predictive maintenance. This will ultimately lead to reduced operational costs [27].

The ultimate vision is to reduce the gap in healthcare access between metropolitan centers and underserved regions. With portable AI-enabled medical devices, specialized diagnostics can be performed even in the most remote areas of the country with accuracy comparable to major academic hospitals in Tehran [28].