The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management.

Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.

Con: Mechanical Ventilation During Cardiopulmonary Bypass.

The ventilatory strategy to adopt during cardiopulmonary bypass is still being debated. The rationale for using continuous positive airway pressure or mechanical ventilation would be to counteract alveolar collapse and improve ischemia phenomena and passive alveolar diffusion of oxygen. Although there are several studies supporting the hypothesis of a positive effect on oxygenation and systemic inflammatory response, the real clinical impact of ventilation during cardiopulmonary bypass is controversial. Furthermore, the biases present in the literature make the studies' results nonunique in their interpretation.

Algor-ethics: charting the ethical path for AI in critical care.

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.

The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives.

Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research for applications in diagnostic and clinical decision support, as well as patient triage. Therefore, LLMs can be used for multiple tasks within patient care, research, and education. Throughout 2023, there has been an escalation in the release of LLMs, some of which are applicable in the healthcare domain. This remarkable output is largely the effect of the customization of pre-trained models for applications like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As healthcare professionals, we recognize the imperative to stay at the forefront of knowledge. However, keeping abreast of the rapid evolution of this technology is practically unattainable, and, above all, understanding its potential applications and limitations remains a subject of ongoing debate. Consequently, this article aims to provide a succinct overview of the recently released LLMs, emphasizing their potential use in the field of medicine. Perspectives for a more extensive range of safe and effective applications are also discussed. The upcoming evolutionary leap involves the transition from an AI-powered model primarily designed for answering medical questions to a more versatile and practical tool for healthcare providers such as generalist biomedical AI systems for multimodal-based calibrated decision-making processes. On the other hand, the development of more accurate virtual clinical partners could enhance patient engagement, offering personalized support, and improving chronic disease management.

Artificial Intelligence in Operating Room Management.

This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.

Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review.

Modern medicine, both in clinical practice and research, has become more and more based on data, which is changing equally in type and quality with the advent and development of healthcare digitalization. The first part of the present paper aims to present the steps through which data, and subsequently clinical and research practice, have evolved from paper-based to digital, proposing a possible future of this digitalization in terms of potential applications and integration of digital tools in medical practice. Noting that digitalization is no more a possible future, but a concrete reality, there is a strong need for a new definition of evidence-based medicine, which must take into account the progressive integration of artificial intelligence (AI) in all decision-making processes. So, leaving behind the traditional research concept of human intelligence versus AI, poorly adaptable to real-world clinical practice, a Human and AI hybrid model, seen as a deep integration of AI and human thinking, is proposed as a new healthcare governance system. The second part of our review is focused on some of the major challenges the digitalization process has to face, particularly privacy issues, system complexity and opacity, and ethical concerns related to legal aspects and healthcare disparities. Analyzing these open issues, we aim to present some of the future directions that in our opinion should be pursued to implement AI in clinical practice.

Surgical outcomes and patient-centred perioperative programs.

Perioperative medicine is changing, and its goals are expanding. More and more attention is paid to the surgical experience and the patient's quality of life. Patient-reported data represent a useful tool in this context. Patient-reported outcomes measures (PROMs) and experience measures (PREMs) are among the most used categories. However, creating perioperative programs capable of integrating traditional perioperative data with these scales is not easy. New technologies, particularly artificial intelligence, thanks to their ability to recognise, interpret, process or simulate human feelings, emotions and moods, could provide the necessary tools to combine all perioperative aspects, placing the patients and their needs at the centre of the process.

From Big Data's 5Vs to clinical practice's 5Ws: enhancing data-driven decision making in healthcare.

The use of AI-based algorithms is rapidly growing in healthcare, but there is still an ongoing debate about how to manage and ensure accountability for their clinical use. While most of the studies focus on demonstrating a good algorithm performance it is important to acknowledge that several additional steps are needed for reaching an effective implementation of AI-based models in daily clinical practice, with implementation being one of the main key factors. We propose a model characterized by five questions that can guide in this process. Additionally, we believe that a hybrid intelligence, human and artificial respectively, is the new clinical paradigm that offer the most benefits for developing clinical decision support systems for bedside use.

Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios.

This paper aims to highlight the potential applications and limits of a large language model (LLM) in healthcare. ChatGPT is a recently developed LLM that was trained on a massive dataset of text for dialogue with users. Although AI-based language models like ChatGPT have demonstrated impressive capabilities, it is uncertain how well they will perform in real-world scenarios, particularly in fields such as medicine where high-level and complex thinking is necessary. Furthermore, while the use of ChatGPT in writing scientific articles and other scientific outputs may have potential benefits, important ethical concerns must also be addressed. Consequently, we investigated the feasibility of ChatGPT in clinical and research scenarios: (1) support of the clinical practice, (2) scientific production, (3) misuse in medicine and research, and (4) reasoning about public health topics. Results indicated that it is important to recognize and promote education on the appropriate use and potential pitfalls of AI-based LLMs in medicine.

Is lung ultrasound score a useful tool to monitoring and handling moderate and severe COVID-19 patients in the general ward? An observational pilot study.

Lung ultrasound is a well-established diagnostic tool in acute respiratory failure, and it has been shown to be particularly suited for the management of COVID-19-associated respiratory failure. We present exploratory analyses on the diagnostic and prognostic performance of lung ultrasound score (LUS) in general ward patients with moderate-to-severe COVID-19 pneumonia receiving O2 supplementation and/or noninvasive ventilation. From March 10 through May 1, 2020, 103 lung ultrasound exams were performed by our Forward Intensive Care Team (FICT) on 26 patients (18 males and 8 females), aged 62 (54 - 76) and with a Body Mass Index (BMI) of 30.9 (28.7 - 31.5), a median 6 (5 - 9) days after admission to the COVID-19 medical unit of the University Hospital of Parma, Italy. All patients underwent chest computed tomography (CT) the day of admission. The initial LUS was 16 (11 - 21), which did not significantly correlate with initial CT scans, probably due to rapid progression of the disease and time between CT scan on admission and first FICT evaluation; conversely, LUS was significantly correlated with PaO2/FiO2 ratio throughout patient follow-up [R = - 4.82 (- 6.84 to - 2.80; p < 0.001)]. The area under the receiving operating characteristics curve of LUS for the diagnosis of moderate-severe disease (PaO2/FiO2 ratio ≤ 200 mmHg) was 0.73, with an optimal cutoff value of 11 (positive predictive value: 0.98; negative predictive value: 0.29). Patients who eventually needed invasive ventilation and/or died during admission had significantly higher LUS throughout their stay.

Multidisciplinary management of a large pheochromocytoma presenting with cardiogenic shock: a case report.

BACKGROUND: Pheochromocytoma is well-known for sudden initial presentations, particularly in younger patients. Hemodynamic instability may cause serious complications and delay a patient's ability to undergo surgical resection. Larger tumors present a further challenge because of the risk of catecholamine release during manipulations. In the case we present, increases in systemic vascular resistance caused cardiogenic shock, and the size of the lesion prompted surgeons to veer off from their usual approach. CASE PRESENTATION: A 38-year-old female patient was admitted to our intensive care unit with hypertension and later cardiogenic shock. Profound systolic dysfunction (left ventricular ejection fraction of 0.12) was noted together with severely increased systemic vascular resistance, and gradually responded to vasodilator infusion. A left-sided 11-cm adrenal mass was found with computed tomography and confirmed a pheochromocytoma with a meta-iodo-benzyl-guanidine scintigraphy. Surgical treatment was carefully planned by the endocrinologist, anesthesiologist and surgeon, and was ultimately successful. After prolonged hemodynamic stabilization, open adrenalectomy and nephrectomy were deemed safer because of lesion size and the apparent invasion of the kidney. Surgery was successful and the patient was discharged home 5 days after surgery. She is free from disease at almost 2 years from the initial event. CONCLUSIONS: Large, invasive pheochromocytoma can be safely and effectively managed with open resection in experienced hands, provided all efforts are made to achieve hemodynamic stabilization and to minimize. Catecholamine release before and during surgery.

Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization.

We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus on the administrative and managerial Operating Room (OR) perspective. Studies conducted on adult (≥ 18 years) patients between 2015 and February 2019 were deemed eligible. A total of 19 papers were included. Our review suggests that the use of Machine Learning (ML) in the field of OR organization has many potentials. Predictions of the surgical case duration were obtain with a good performance; their use could therefore allow a more precise scheduling, limiting waste of resources. ML is able to support even more complex models, which can coordinate multiple spaces simultaneously, as in the case of the post-anesthesia care unit and operating rooms. Types of Artificial Intelligence could also be used to limit another organizational problem, which has important economic repercussions: cancellation. Random Forest has proven effective in identifing surgeries with high risks of cancellation, allowing to plan preventive measures to reduce the cancellation rate accordingly. In conclusion, although data in literature are still limited, we believe that ML has great potential in the field of OR organization; however, further studies are needed to assess the effective role of these new technologies in the perioperative medicine.