Over the past few years, artificial intelligence (AI) has significantly improved healthcare. Once the stuff of science fiction, AI is now widely used, even in our daily lives — often without us thinking about it. All healthcare professionals — especially executives and medical doctors — need to understand the capabilities of advanced AI tools and other breakthrough innovations. This understanding will allow them to recognize opportunities and threats emerging technologies can bring to their organizations. We hope to contribute to a meaningful public discussion about the role of this new type of AI and how our approach to healthcare and medicine can best evolve with the rapid development of this technology. Since medicine learns by example, only a few possible uses of AI in medicine are provided, which merely outline the system’s capabilities. Among the examples, it is worth highlighting the roles of AI in medical notes, education, preventive programs, consultation, triage and intervention. It is believed by the authors that large language models such as chat generative pre-trained transformer (ChatGPT) are reaching a level of maturity that will soon impact clinical medicine as a whole and improve the delivery of individualized, compassionate, and scalable healthcare. It is unlikely that AI will replace physicians in the near future. The human aspects of care, including empathy, compassion, critical thinking, and complex decision-making, are invaluable in providing holistic patient care beyond diagnosis and treatment decisions. The GPT-4 has many limitations and cannot replace direct contact between an experienced physician and a patient for even the most seemingly simple consultations, not to mention the ethical and legal aspects of responsibility for diagnosis. Over the past few years, artificial intelligence (AI) has significantly improved healthcare. Once the stuff of science fiction, AI is now widely used, even in our daily lives — often without us thinking about it. All healthcare professionals — especially executives and medical doctors — need to understand the capabilities of advanced AI tools and other breakthrough innovations. This understanding will allow them to recognize opportunities and threats emerging technologies can bring to their organizations. We hope to contribute to a meaningful public discussion about the role of this new type of AI and how our approach to healthcare and medicine can best evolve with the rapid development of this technology. Since medicine learns by example, only a few possible uses of AI in medicine are provided, which merely outline the system’s capabilities. Among the examples, it is worth highlighting the roles of AI in medical notes, education, preventive programs, consultation, triage and intervention. It is believed by the authors that large language models such as chat generative pre-trained transformer (ChatGPT) are reaching a level of maturity that will soon impact clinical medicine as a whole and improve the delivery of individualized, compassionate, and scalable healthcare. It is unlikely that AI will replace physicians in the near future. The human aspects of care, including empathy, compassion, critical thinking, and complex decision-making, are invaluable in providing holistic patient care beyond diagnosis and treatment decisions. The GPT-4 has many limitations and cannot replace direct contact between an experienced physician and a patient for even the most seemingly simple consultations, not to mention the ethical and legal aspects of responsibility for diagnosis.

Background: We are currently experiencing a third digital revolution driven by artificial intelligence (AI), and the emergence of new chat generative pre-trained transformer (ChatGPT) represents a significant technological advancement with profound implications for global society, especially in the field of education. Methods: The aim of this study was to see how well ChatGPT performed on medical school exams and to highlight how it might change medical education and practice. Recently, OpenAI’s ChatGPT (OpenAI, San Francisco; GPT-4 May 24 Version) was put to the test against a significant Polish medical specialization licensing exam (PES), and the results are in. The version of ChatGPT-4 used in this study was the most up-to-date model at the time of publication (GPT-4). ChatGPT answered questions from June 28, 2023, to June 30, 2023. Results: ChatGPT demonstrates notable advancements in natural language processing models on the tasks of medical question answering. In June 2023, the performance of ChatGPT was assessed based on its ability to answer a set of 120 questions, where it achieved a correct response rate of 67.1%, accurately responding to 80 questions. Conclusions: ChatGPT may be used as an assistance tool in medical education. While ChatGPT can serve as a valuable tool in medical education, it cannot fully replace human expertise and knowledge due to its inherent limitations. Background: We are currently experiencing a third digital revolution driven by artificial intelligence (AI), and the emergence of new chat generative pre-trained transformer (ChatGPT) represents a significant technological advancement with profound implications for global society, especially in the field of education. Methods: The aim of this study was to see how well ChatGPT performed on medical school exams and to highlight how it might change medical education and practice. Recently, OpenAI’s ChatGPT (OpenAI, San Francisco; GPT-4 May 24 Version) was put to the test against a significant Polish medical specialization licensing exam (PES), and the results are in. The version of ChatGPT-4 used in this study was the most up-to-date model at the time of publication (GPT-4). ChatGPT answered questions from June 28, 2023, to June 30, 2023. Results: ChatGPT demonstrates notable advancements in natural language processing models on the tasks of medical question answering. In June 2023, the performance of ChatGPT was assessed based on its ability to answer a set of 120 questions, where it achieved a correct response rate of 67.1%, accurately responding to 80 questions. Conclusions: ChatGPT may be used as an assistance tool in medical education. While ChatGPT can serve as a valuable tool in medical education, it cannot fully replace human expertise and knowledge due to its inherent limitations.

The interplay between adiponectin and resistin, the two adipokines of opposite effects, may determine the metabolic profile of obese individuals and development of obesity-related complications. The current study was conducted to assess how adiponectin/resistin interplay in sera and adipose tissues may influence the metabolic profile of obese and normal-weight subjects. Concentrations of adiponectin and resistin were measured on protein level by immunoassay in visceral and subcutaneous adipose tissues from 50 obese (body mass index > 40 kg/m2) and 28 normal-weight (body mass index 20–24.9 kg/m2) individuals. Simultaneously expression of ADIPOQ and RETN (encoding adiponectin and resistin, respectively) was assessed on mRNA level by real-time PCR. ADIPOQ mRNA (P = 0.0001) and adiponectin protein (P = 0.0013) levels were lower, while RETN mRNA (P = 0.0338) and resistin (P < 0.0001)—higher in subcutaneous adipose tissues of obese subjects. ADIPOQ and RETN mRNA levels did not correlate with protein concentrations in the investigated adipose tissues. In obesity adiponectin serum concentrations correlated positively with ADIPOQ mRNA in subcutaneous adipose tissue (P = 0.005) and negatively with protein levels in visceral adipose tissue (P = 0.001). Obesity was associated with higher adiponectin–resistin index value in sera (P < 0.0001) and decreased in subcutaneous adipose tissue (P < 0.001), but only adiponectin–resistin index measured in sera was significantly higher in obese with the metabolic syndrome (P = 0.04). Obesity affects synthesis of adiponectin and resistin mainly in subcutaneous adipose tissue. The adiponectin–resistin index assessed in the adipose tissues has a different prognostic value compared to the adiponectin–resistin index in serum and does not reflect a metabolic risk in obese individuals.