This article examines the complex relationship between disease perception, negative emotions, and their impact on postoperative recovery in patients with perianal diseases. These conditions not only cause physical discomfort, but also carry a significant emotional burden, often exacerbated by social stigma. Psychological factors, including stress, anxiety, and depression, activate neuroendocrine pathways, such as the hypothalamic-pituitary-adrenal axis, disrupting the gut microbiota and leading to dysbiosis. This disruption can delay wound healing, prolong hospital stay, and intensify pain. Drawing on the findings of Hou et al, our article highlights the critical role of illness perception and negative emotions in shaping recovery outcomes. It advocates for a holistic approach that integrates psychological support and gut microbiota modulation, to enhance healing and improve overall patient outcomes.

The integration of Artificial Intelligence (AI) in psychiatry presents opportunities for enhancing patient care but raises significant ethical concerns and challenges in clinical application. Addressing these challenges necessitates an informed and ethically aware psychiatric workforce capable of integrating AI into practice responsibly.

A mixed-methods study was conducted to assess the outcomes of the "CONNECT with AI" - (Collaborative Opportunity to Navigate and Negotiate Ethical Challenges and Trials with Artificial Intelligence) workshop, aimed at exploring AI's ethical implications and applications in psychiatry. This workshop featured presentations, discussions, and scenario analyses focusing on AI's role in mental health care. Pre- and post-workshop questionnaires and focus group discussions evaluated participants' perspectives, and ethical understanding regarding AI in psychiatry.

Participants exhibited a cautious optimism towards AI, recognizing its potential to augment mental health care while expressing concerns over ethical usage, patient-doctor relationships, and AI's practical application in patient care. The workshop significantly improved participants' ethical understanding, highlighting a substantial knowledge gap and the need for further education in AI among psychiatrists.

The study underscores the necessity of continuous education and ethical guideline development for psychiatrists in the era of AI, emphasizing collaborative efforts in AI system design to ensure they meet clinical needs ethically and effectively. Future initiatives should aim to broaden psychiatrists' exposure to AI, fostering a deeper understanding and integration of AI technologies in psychiatric practice.

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.

For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.