Artificial Intelligence and Medical Practice: Between Technological Promise and Cognitive Responsibility

Artificial intelligence can reduce the burden on doctors and improve their analytical skills. Collaboration between physicians and artificial intelligence has the greatest potential to improve clinical decision-making and patient health outcomes. Due to the complexity of the main symptoms of the diseases, it is difficult to develop early diagnostic tools. Therefore, effective and timely diagnosis of the disease remains a challenge in the medical field. Although the integration of artificial intelligence into medical practice is still in its early stages, its full use for medical diagnostics is possible, especially in the direction of cancer prevention. Artificial intelligence can have a significant impact on medical practice, in particular improving the accuracy of diagnosis, saving costs and time compared to traditional diagnostic methods. Also, artificial intelligence has the potential to make clinical laboratory testing more accurate, faster, and more efficient. It is worth noting that in the health sector, errors in the diagnostic process are the biggest problem. Artificial intelligence can minimize diagnostic errors and detect life-threatening diseases in patients at an early stage. In this regard, the decisive role of artificial intelligence in determining the dosage of medicines, as well as in predicting drug contraindications, which is important for improving the treatment results, is crucial. Artificial intelligence has the potential to analyze individual cases and compare them with patient databases, allowing for individualized treatment planning. This indicates the revolutionary potential of artificial intelligence in medicine. However, experts admit that artificial intelligence presents a challenge because the implementation of algorithms is not an independent process and takes place in a dynamic environment where human reaction and adaptation play a crucial role. Medical education about artificial intelligence is essential because future doctors will have to provide medical services to patients in completely different healthcare settings. To fully realize the potential of artificial intelligence, it is necessary to have properly qualified human resources, to collect quality medical data, to take into account the confidentiality and availability of patient data. Continuous evaluation of AI systems is essential to ensure their sustainable performance over time. Arguably, collaboration between computer scientists and healthcare providers is essential for the practical and successful implementation of artificial intelligence.

We appreciate Dr. Qi and Dr. Niu for their insightful comments on our study, "Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation." Their observations underscore significant considerations in the application of artificial intelligence (AI) in plastic surgery. We agree with their concern about potential biases in ChatGPT's responses. The AI's frequent attribution of the title "parent of plastic surgery" to Sir Harold Delf Gillies, despite gender-neutral terminology, highlights underlying biases from training data. These biases often reflect historical texts and contemporary writings. Addressing them requires refining training datasets for balanced representation and developing algorithms that adjust dynamically to diverse inputs. The authors also question the criteria ChatGPT uses to identify key contributions to plastic surgery. The AI's focus on microsurgery, minimally invasive techniques, and tissue engineering, while significant, may prioritize keyword prevalence over a holistic evaluation. Enhancing ChatGPT's capabilities through targeted training and input from subject matter experts could improve the AI's ability to generate more balanced outputs. The identified bias favoring reconstructive over cosmetic procedures is another critical point. While reconstructive advancements are transformative, cosmetic surgery also has significant innovations. Ensuring ChatGPT presents a balanced view of both reconstructive and cosmetic advancements is essential. This can be achieved by diversifying training data and calibrating the AI to give equitable weight to different subspecialties within plastic surgery. AI models like ChatGPT are proficient in processing and generating information but lack the human elements of creativity, intuition, and emotional depth critical for groundbreaking innovations. AI should complement, not replace, the expert judgment and innovative thinking of skilled plastic surgeons. Ensuring the accuracy of AI-generated responses is crucial. Clinicians must verify AIgenerated information against established medical literature and clinical guidelines to maintain accuracy in medical practice. Continuous feedback and improvement mechanisms are vital to enhance AI's clinical utility. The improvement of AI in plastic surgery will be driven by active involvement from surgeons, providing comprehensive and balanced data for training to ensure AI systems evolve to support and enhance clinical practice effectively.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

BackgroundGenerative artificial intelligence (AI) is rapidly transforming healthcare, but its adoption introduces significant ethical and practical challenges. Algorithmic bias, ambiguous liability, lack of transparency, and data privacy risks can undermine patient trust and create health disparities, making their resolution critical for responsible AI integration.ObjectivesThis systematic review analyzes the generative AI landscape in healthcare. Our objectives were to: (1) identify AI applications and their associated ethical and practical challenges; (2) evaluate current data-centric, model-centric, and regulatory solutions; and (3) propose a framework for responsible AI deployment.MethodsFollowing the PRISMA 2020 statement, we conducted a systematic review of PubMed and Google Scholar for articles published between January 2020 and May 2025. A multi-stage screening process yielded 54 articles, which were analyzed using a thematic narrative synthesis.ResultsOur review confirmed AI’s growing integration into medical training, research, and clinical practice. Key challenges identified include systemic bias from non-representative data, unresolved legal liability, the “black box” nature of complex models, and significant data privacy risks. Proposed solutions are multifaceted, spanning technical (e.g., explainable AI), procedural (e.g., stakeholder oversight), and regulatory strategies.DiscussionCurrent solutions are fragmented and face significant implementation barriers. Technical fixes are insufficient without robust governance, clear legal guidelines, and comprehensive professional education. Gaps in global regulatory harmonization and frameworks ill-suited for adaptive AI persist. A multi-layered, socio-technical approach is essential to build trust and ensure the safe, equitable, and ethical deployment of generative AI in healthcare.ConclusionsThe review confirmed that generative AI has a growing integration into medical training, research, and clinical practice. Key challenges identified include systemic bias stemming from non-representative data, unresolved legal liability, the “black box” nature of complex models, and significant data privacy risks. These challenges can undermine patient trust and create health disparities. Proposed solutions are multifaceted, spanning technical (such as explainable AI), procedural (like stakeholder oversight), and regulatory strategies.

Background: Artificial Intelligence (AI), recognized as the fourth industrial revolution, is revolutionizing medical practice through enhanced diagnostics, treatment planning, and healthcare delivery. Its expanded role post-COVID-19 underscores the urgency of AI literacy among future physicians. This study aimed to explore medical students’ perceptions of AI in medical education. Method: A cross-sectional study was conducted from June to July 2024 at CMH and AIMC medical colleges in Pakistan. Using a non-probability convenience sampling technique, an online questionnaire was distributed among MBBS students from both public and private institutions. A minimum of 10% of students from each academic year were invited, yielding a final sample of 139 respondents (response rate: 92%). Associations between categorical variables were analyzed using the chi-square test. Results: Among participants, 57.2% were female and 42.8% male. Most students (96.4%) agreed that computers aid learning, and 93.5% believed AI will be essential in future medical practice. Additionally, 77% felt AI would improve medical education, while 87.7% expressed willingness to receive AI-based instruction. A majority (89.1%) supported the inclusion of AI training in the curriculum, and 83.5% believed AI knowledge would benefit their careers. Female students were significantly more likely to support curriculum integration (p = 0.03) and AI instruction (p = 0.04), though no significant gender differences were observed in perceptions of AI’s future role (p = 0.12). Conclusion: Medical students exhibited highly positive attitudes toward AI. While they do not view AI as replacing physicians, they recognize its transformative role. Integrating AI education into MBBS curricula is therefore essential.

Artificial Intelligence in Medical Practice: The Question to the Answer?

BackgroundIn the years to come, artificial intelligence will become an indispensable tool in medical practice. The digital transformation will undoubtedly affect today’s medical students. This study focuses on trust from the perspective of three groups of medical students - students from Croatia, students from Slovakia, and international students studying in Slovakia.MethodsA paper-pen survey was conducted using a non-probabilistic convenience sample. In the second half of 2022, 1715 students were surveyed at five faculties in Croatia and three in Slovakia.ResultsSpecifically, 38.2% of students indicated familiarity with the concept of AI, while 44.8% believed they would use AI in the future. Patient readiness for the implementation of technologies was mostly assessed as being low. More than half of the students, 59.1%, believe that the implementation of digital technology (AI) will negatively impact the patient-physician relationship and 51,3% of students believe that patients will trust physicians less. The least agreement with the statement was observed among international students, while a higher agreement was expressed by Slovak and Croatian students 40.9% of Croatian students believe that users do not trust the healthcare system, 56.9% of Slovak students agree with this view, while only 17.3% of international students share this opinion. The ability to explain to patients how AI works if they were asked was statistically significantly different for the different student groups, international students expressed the lowest agreement, while the Slovak and Croatian students showed a higher agreement.ConclusionThis study provides insight into medical students’ attitudes from Croatia, Slovakia, and international students regarding the role of artificial intelligence (AI) in the future healthcare system, with a particular emphasis on the concept of trust. A notable difference was observed between the three groups of students, with international students differing from their Croatian and Slovak colleagues. This study also highlights the importance of integrating AI topics into the medical curriculum, taking into account national social & cultural specificities that could negatively impact AI implementation if not carefully addressed.

Current challenges of implementing artificial intelligence in medical imaging

Artificial intelligence is the study of algorithms that give machines the ability to "reason" and acquire cognitive functions to achieve human–level performance in cognition-related tasks such as, for example, problem solving, object and word recognition, and decision-making. Currently, there are a lot of studies proving that artificial intelligence can not only diagnose diseases on a par with doctors, but also spend much less time on it. Artificial intelligence has entered many areas of medicine, and recently its role has become more significant in the diagnosis and treatment of pancreatic pathology. Over the past decade, the number and variation of methods for analyzing medical images has increased significantly due to the development of artificial intelligence, new programs for analyzing and systematizing objects. The aim of this review is to analyze, summarize and evaluate data published in the scientific literature on the use of artificial intelligence techniques to diagnose pancreatic pathology based on the results of computed tomography. It is demonstrated further perspectives and the need to develop this area in medical practice. A systematic literature search was conducted on the databases of the journals PubMed and eLibrary. The search for literature was carried out by Keywords"artificial intelligence", "pancreas", "computed tomography", "radiomics". The search interval was 2015-2023. The authors investigated all research studies of foreign and Russian scientists, which contain information on the use of diverse options of artificial intelligence techniques for differential diagnosis of pancreatic pathology, mainly based on computed tomography, and their assessment to demonstrate their further beneficial development in the field of medicine. To date, artificial intelligence programs based on spiral computed tomography data allow differentiating the pathology of the pancreas with high accuracy, which greatly facilitates human efforts and allows applying them as an indispensable assistant in work. That is why it is necessary to introduce these technologies into the circulation of medical institutions as actively as possible in order to expand the database of artificial intelligence, which will achieve more accurate results in the diagnosis of pancreatic diseases and more.

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Artificial Intelligence (AI) is transforming healthcare delivery through its applications in early disease detection and personalized treatment planning. This comprehensive technical article examines the current landscape of AI integration in medical practice, highlighting how advanced algorithms analyze complex healthcare data to identify disease indicators earlier than conventional methods and develop individualized therapeutic approaches. It covers supervised, unsupervised, and reinforcement learning techniques being applied across various medical domains, particularly in oncology and cardiovascular disease. By leveraging diverse data sources—including electronic health records, medical imaging, genomic information, and wearable device data—AI systems demonstrate promising capabilities in revolutionizing diagnostic accuracy, treatment selection, and chronic disease management. The article also addresses significant challenges in implementing healthcare AI, including data quality concerns, integration difficulties, regulatory uncertainties, and ethical considerations. As healthcare organizations navigate these implementation barriers, emerging approaches such as federated learning, explainable AI, and continuous learning systems offer potential solutions to expand AI adoption while ensuring equitable, transparent, and clinically valuable applications.

Integrating Artificial Intelligence Techniques in Medical Bacteriology is a scientific research paper that explores the transformative role of artificial intelligence (AI) in enhancing diagnostic and therapeutic practices within the field of bacteriology. As AI technologies increasingly permeate healthcare, this study provides a comprehensive analysis of how machine learning (ML) and deep learning algorithms can significantly improve the accuracy and timeliness of bacterial pathogen detection and antibiotic resistance management, marking a notable advancement in laboratory medicine.[1-][2] The research emphasizes the potential of AI to streamline workflows and enhance operational efficiency in medical bacteriology. By automating processes such as error detection, result interpretation, and image analysis, AI systems can significantly reduce the turnaround time for diagnostic results, ultimately leading to improved patient outcomes.[3][4] The paper also highlights the importance of data quality management in the development of AI models, advocating for adherence to established standards throughout the dataset lifecycle to ensure the reliability of AI applications in clinical settings.[5]Despite the promising advancements, the integration of AI in healthcare is not without its challenges. The study discusses the current limitations in the clinical efficacy and cost-effectiveness of AI applications, revealing a gap between research outcomes and real-world implementation.[4] Additionally, ethical considerations surrounding data privacy and algorithmic transparency are addressed, emphasizing the need for regulatory frameworks that promote safe and equitable AI use in medical practice.[6]Overall, this research provides a critical examination of the trends and innovations in AI applications in medical bacteriology, employing statistical analysis and bibliometric techniques to identify research hotspots and emerging patterns in the field from 2010 to 2024.[7][6] By integrating AI methodologies, the study aims to lay the groundwork for future research directions and improve quality assurance standards

Implementing a reform in medical education requires students’ awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students’ perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.

Medicine as an area of endeavor is very important to humanity because of its significance to the healthy preservation of human life. Overtime, various devices with varying levels of sophistication, (dependent largely on the era), have been developed with the aim of providing support to medical professionals in the delivery of medical services. As of today, Artificial Intelligence (AI) has also lent its support to the development of medical devices by not only making such devices smarter, but in some cases being the device itself. As will be discussed in later parts of this article, so sophisticated is AI that it could even assist in the performance of medical surgeries. Two important components of AI’s role in medical practice are natural language processing and computer vision which are play key roles in patient assessment and diagnosis.

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.

Artificial intelligence is widely used in healthcare. World leading scientific schools are experimenting with various machine learning models and trying to implement them into medical practice. In various fields of medicine, there are good solutions already exists that allow artificial intelligence products use as medical devices for making medical decisions. For example, artificial intelligence products received a special impetus to this during COVID-19 in radiology. For high-quality artificial intelligence training, high-quality datasets are needed. That’s why as in general artificial intelligence, the transition from a model-centric approach to a data-centric approach is gradually moving. The data quality for neural networks training is becoming more and more acute. There are many problems associated with the statistically reliable medical datasets collection. The article considers an innovative solution of recent years - synthetic datasets, which can solve many dataset quality problems for artificial intelligence training. Key factors affecting the complexity of medical data mining are analyzed and examples of synthetic datasets can reduce artificial intelligence development time, balance data and improve the quality of artificial intelligence products are presented.KeywordsArtificial intelligenceSynthetic datasetsMedical artificial intelligence products