Artificial intelligence is a new field that uses computers to act like human intelligence. AI is going to make big changes in healthcare in the next few years and will become much bigger in the next decades. Artificial intelligence is expected to help medical centers work better and efficiently, diagnose diseases accurately, and create treatment plans.

Artificial Intelligence has been used in health care fields with some success (1). Even though we have made a lot of progress using artificial intelligence for medical research, there are still differences between what the computers can do and what we need for real-life health care situations (2).

The fast growth of AI technology can be used in hospitals to improve how they treat patients. This could make a big difference in how healthcare is delivered. It is important to write down and share information about how AI is used in hospitals, so that healthcare workers have the right tools and know-how to use it properly (3).

To make better decisions, we need to evaluate how AI can change our healthcare system. Considering how important AI could be in many areas, we should look at more than just how well it works and how much it costs. We need to think about its overall value in the world and how it can help people in their daily lives (4).

Due to the increasing importance of the use and application of artificial intelligence in the field of health technology assessment and evidence-based policymaking, especially after Covid-19 pandemic, “health technology assessment (HTA) in action journal” decided to invite researchers and experts to help to develop this knowledge by publishing specialized papers in this field. It is hoped that the published articles can provide up-to-date results in this field.

The editor wants to thank all the authors who submitted their work to the special issue of the "HTA in Action journal" Also, thanks to all the reviewers for working hard to review the articles and help the editors make the final decision.

Artificial intelligence (AI) has become an integral part of modern healthcare, with its algorithms and other AI-enabled applications supporting medical professionals in clinical and research settings. The digital revolution is transforming the way we approach medical care. Currently, numerous AI products have been developed to cover various aspects of healthcare, such as predicting the risk of acute and chronic diseases (e.g., cardiovascular risk, gastrointestinal bleeding, and eye conditions) and forecasting cancer risk, among other cases. Artificial intelligence has the capacity to revolutionize the utilization of health information collected in datasets. However, the specific characteristics of AI, including vagueness, complexity, data dependency, and automated behavior, can pose potential risks to users’ fundamental rights and safety. Therefore, it is crucial to recognize and mitigate these risks and provide legal solutions for any harm resulting from these risks. In the realm of healthcare, AI plays a pivotal role in advancing reliable prediction capabilities. Consequently, the
storage and processing of data are imperative for emerging diagnostic and decision-making technologies. Nevertheless, these advances also introduce privacy risks, raising significant legal challenges for medical institutions. Understanding the various levels of these risks assists healthcare professionals and institutions in managing these challenges and complying with regulations. This descriptive research article comprehensively examines and implements the regulatory frameworks governing the United States and the European Union.
Additionally, it draws upon documented research in this field to discuss the utilization of AI in healthcare, along with the associated legal issues, including informed consent and malpractice.

Background: The smartification of supply chains, which enables organizations to stay informed about crises in a timely manner and make appropriate decisions in response to the resulting pressures, has consistently been a crucial factor in the realm of organizational transformation. The healthcare industry in Iran faces more challenges and crises than other industries, especially due to its vital role in public health prevention and care.
Objectives: According to the reasons described, this research aims to simulate and improve the efficiency of the healthcare supply chain when confronted with crises.
Methods: In this study, the efficiency of the healthcare supply chain was simulated using the system dynamics approach and Vensim DSS.
Results: The results indicated that healthcare supply chain efficiency in Iran is unsatisfactory and may confront challenges during crises. Therefore, this study places particular emphasis on examining scenarios for improving the current situation, which stems from the consensus of experts and stakeholders in this field. In the presented scenarios, a 2% improvement in the utilization of advanced intelligent technologies and a 5% improvement in intelligent inventory management were observed. Notably, the combined effect of these two
scenarios led to an overall enhancement in the average efficiency of the healthcare smart supply chain. These improvements can increase the average efficiency levels within the pharmaceutical manufacturing segment up to 1.3%, 5.8%, and 7.7% in each of the aforementioned scenarios.
Conclusions: It can be asserted that although the advancement of smart technologies and intelligent inventory management individually contribute to enhancing the efficiency of the healthcare supply chain in Iran, combining these changes can provide the groundwork for further increasing efficiency up to 7.7%.

Background: The potentials of artificial intelligence (AI) have permeated all industries and fields, and the advantages of AI are extensively employed. This technology offers a wide range of benefits in the pharmaceutical industry, including reducing human interventions and increasing the speed and accuracy of tasks. This can expedite time-consuming activities, such as drug discovery, production, clinical trials,
research and development, and ultimately, determining a drug’s position in target markets.
Methods: A comprehensive scope review was performed in this descriptive, applied study on the applications of AI in the pharmaceutical industry in Iran. Relevant data were meticulously gathered and extracted from diverse sources, including various search engines, key databases, such as Medline, PubMed, Elsevier, and the Iranian Center for Scientific Information and Documentation, and information databases, reference books, and reports from the World Health Organization. These results represent our research on key themes, including AI, the pharmaceutical industry, drug production, innovation, and evolution. Our main focus lies on the application of AI in the manufacturing part of the pharmaceutical industry, with a deliberate decision not to delve into the technical aspects. This approach allowed us to prioritize a comprehensive understanding of the practical implications and advancements in the manufacturing processes facilitated by artificial intelligence.
Results: The retrieved studies showed that AI has the potential to enhance crucial processes in pharmaceutical companies across various dimensions. These potential capabilities are observed in areas such as quality control, human resource management, research and development, finance, supply chain management, logistics, data management, operations management, customer relationship management, and commerce, which are further discussed.
Conclusions: Pharmaceutical companies can utilize the tools provided by AI in various value-creating processes to enhance their efficiency and effectiveness. This requires the adoption and integration of this innovative technology at various levels of organizational planning so that these companies can harness the greater potential it offers with appropriate investments.

Background: In line with the advancement of Artificial Intelligence (AI), innovative solutions have been designed to improve healthrelated Sustainable Development Goals (SDGs). Accordingly, there is an increasing trend in the realm of AI and SDG research areas.
Objectives: This study aimed to analyze the trends and patterns of AI research in health-related SDGs using bibliometric analysis.
Methods: The bibliometric approach facilitated the identification of key terms and countries from previous research. We used VOSviewer to map and analyze data obtained from three databases: Scopus, Web of Science, and PubMed.
Results: Our findings illustrated that research on health has been a popular area of study in recent years. In particular, we observed a significant increase in research on AI in health-related SDGs during 2015 - 2022.
Conclusions: This study provides insights into the trends and patterns of AI research in health-related SDGs using bibliometric analysis. The findings can guide future research by identifying key terms that require further investigation.

It appears that the use of artificial intelligence in the early diagnosis of Alzheimer's disease holds the potential for positive outcomes in the treatment process and will have a beneficial impact on therapeutic planning. However, further in-depth studies in this area are necessary to gain a more comprehensive understanding of its full potential and effectiveness