Understanding basic principles of Artificial Intelligence: a practical guide for intensivists

Ethical Considerations in the Use of Artificial Intelligence in Pain Medicine

Although the integration of artificial intelligence (AI) into medicine and healthcare holds transformative potential, significant challenges must be necessarily addressed. This technological innovation requires a commitment to ethical principles. Key issues concern autonomy, reliability, and bias. Furthermore, AI development must guarantee rigorous data privacy and security standards. Effective AI implementation demands thorough validation, transparency, and the involvement of multidisciplinary teams to oversee ethical considerations. These issues also concern pain medicine where careful assessment of subjective experiences and individualized care are crucial. Notably, in this rapidly evolving technological landscape, politics plays a pivotal role in establishing rules and regulations. Regulatory frameworks, such as the European Union's Artificial Intelligence Act and recent U.S. executive orders, provide essential guidelines for the responsible use of AI. This step is crucial for balancing innovation with rigorous ethical standards, ultimately leveraging the incredible AI's benefits. As the field evolves rapidly and concepts like algorethics and data ethics become more widespread, the scientific community is increasingly recognizing the need for specialists in this area, such as AI Ethics Specialists.

Artificial intelligence in dentistry and dental biomaterials

Artificial intelligence (AI) technology is being used in various fields and its use is increasingly expanding in dentistry. The key aspects of AI include machine learning (ML), deep learning (DL), and neural networks (NNs). The aim of this review is to present an overview of AI, its various aspects, and its application in biomedicine, dentistry, and dental biomaterials focusing on restorative dentistry and prosthodontics. AI-based systems can be a complementary tool in diagnosis and treatment planning, result prediction, and patient-centered care. AI software can be used to detect restorations, prosthetic crowns, periodontal bone loss, and root canal segmentation from the periapical radiographs. The integration of AI, digital imaging, and 3D printing can provide more precise, durable, and patient-oriented outcomes. AI can be also used for the automatic segmentation of panoramic radiographs showing normal anatomy of the oral and maxillofacial area. Recent advancement in AI in medical and dental sciences includes multimodal deep learning fusion, speech data detection, and neuromorphic computing. Hence, AI has helped dentists in diagnosis, planning, and aid in providing high-quality dental treatments in less time.

Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning

Background

Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidectomy, pose significant concerns regarding cost and patient care. Dissecting the determinants of extended hospitalization is crucial for optimizing postoperative care and resource allocation.

Objective

This study aims to utilize machine learning (ML) techniques to predict post-surgery discharge times in pediatric patients and identify key variables influencing hospital stays.

Methods

The study analyzed data from 423 children who underwent tonsillectomy and/or adenoidectomy at the IRCCS Istituto Giannina Gaslini, Genoa, Italy. Variables included demographic factors, anesthesia-related details, and postoperative events. Preprocessing involved handling missing values, detecting outliers, and converting categorical variables to numerical classes. Univariate statistical analyses identified features correlated with discharge time. Four ML algorithms-Random Forest (RF), Logistic Regression, RUSBoost, and AdaBoost-were trained and evaluated using stratified 10-fold cross-validation.

Results

Significant predictors of delayed discharge included postoperative nausea and vomiting (PONV), continuous infusion of dexmedetomidine, fentanyl use, pain during discharge, and extubation time. The best-performing model, AdaBoost, demonstrated high accuracy and reliable prediction capabilities, with strong performance metrics across all evaluation criteria.

Conclusion

ML models can effectively predict discharge times and highlight critical factors impacting prolonged hospitalization. These insights can enhance postoperative care strategies and resource management in pediatric surgical settings. Future research should explore integrating these predictive models into clinical practice for real-time decision support.

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.

Bridging knowledge gaps: a bibliometric analysis of non-invasive ventilation in palliative care studies

BACKGROUND

Despite being a useful strategy for providing respiratory support to patients with advanced or terminal illnesses, non-invasive ventilation (NIV) requires in-depth investigation in several key aspects.

OBJECTIVES

This bibliometric analysis seeks to comprehensively examine the existing research on the subject. Its goal is to uncover valuable insights that can inform the prediction trajectory of studies, guide the implementation of corrective measures, and contribute to the improvement of research networks.

METHODS

A comprehensive review of literature on NIV in the context of palliative care was conducted using the Web of Science core collection online database. The search utilized the key terms "non-invasive ventilation" and "palliative care" to identify the most relevant articles. All data were gathered on November 7, 2023. Relevant information from documents meeting the specified criteria was extracted, and Journal Citation Reports™ 2022 (Clarivate Analytics) served as the data source. The analysis employed literature analysis and knowledge visualization tools, specifically CiteScope (version 6.2.R4) and VOSviewer (version 1.6.20).

RESULTS

A dataset with bibliometric findings from 192 items was analyzed. We found a consistent upward of the scientific output trend over time. Guidelines on amyotrophic lateral sclerosis management received the highest number of citations. Most documents were published in top-ranked journals. Less than one-third of the documents pertain to clinical studies, especially retrospective analyses (25%). Key topics such as "decision making", and "communication" were less addressed.

CONCLUSIONS

Given the substantial clinical implications, further high-quality studies on this subject are recommended. Encouraging international collaborations is needed. Despite the growing volume of documents in the field, this bibliometric analysis indicates a decline in collaborative networks.

Artificial Intelligence for Personalized Perioperative Medicine

The development of artificial intelligence (AI) is disruptive and unstoppable, also in medicine. Because of the enormous quantity of data recorded during continuous monitoring and the peculiarity of our specialty where stratification and mitigation risk are some of the core aspects, anesthesiology and postoperative intensive care are fertile fields where new technologies find ample room for expansion. Recently, research efforts have focused on the development of a holistic technology that globally embraces the entire perioperative period rather than a fragmented approach where AI is developed to carry out specific tasks. This could potentially revolutionize the perioperative medicine we know today. In fact, AI will be able to expand clinician's ability to interpret, adapt, and ultimately act in a complex reality with facets that are too complex to be managed all at the same time and in a holistic manner. With the support of new tools, as healthcare professionals we have the moral obligation to govern this transition, allowing an ethical and sustainable development of these technologies and avoiding being overwhelmed by them. We should welcome this transhumanist tension which does not aim at the replacement of human capabilities or even at the integration of these but rather at the expansion of a "single intelligence".

Development of a binary classifier model from extended facial codes toward video-based pain recognition in cancer patients

OBJECTIVES

The Automatic Pain Assessment (APA) relies on the exploitation of objective methods to evaluate the severity of pain and other pain-related characteristics. Facial expressions are the most investigated pain behavior features for APA. We constructed a binary classifier model for discriminating between the absence and presence of pain through video analysis.

METHODS

A brief interview lasting approximately two-minute was conducted with cancer patients, and video recordings were taken during the session. The Delaware Pain Database and UNBC-McMaster Shoulder Pain dataset were used for training. A set of 17 Action Units (AUs) was adopted. For each image, the OpenFace toolkit was used to extract the considered AUs. The collected data were grouped and split into train and test sets: 80 % of the data was used as a training set and the remaining 20 % as the validation set. For continuous estimation, the entire patient video with frame prediction values of 0 (no pain) or 1 (pain), was imported into an annotator (ELAN 6.4). The developed Neural Network classifier consists of two dense layers. The first layer contains 17 nodes associated with the facial AUs extracted by OpenFace for each image. The output layer is a classification label of "pain" (1) or "no pain" (0).

RESULTS

The classifier obtained an accuracy of ∼94 % after about 400 training epochs. The Area Under the ROC curve (AUROC) value was approximately 0.98.

CONCLUSIONS

This study demonstrated that the use of a binary classifier model developed from selected AUs can be an effective tool for evaluating cancer pain. The implementation of an APA classifier can be useful for detecting potential pain fluctuations. In the context of APA research, further investigations are necessary to refine the process and particularly to combine this data with multi-parameter analyses such as speech analysis, text analysis, and data obtained from physiological parameters.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

Artificial Intelligence in Medicine and Dentistry

INTRODUCTION

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.

OBJECTIVE

Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.

CONCLUSION

The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

High-rate breakthrough cancer pain and tumour characteristics - literature review and case series

Cancer pain requires careful comprehensive patient evaluation and an appropriate and personalized clinical approach by a trained multidisciplinary team. The proper assessment of breakthrough cancer pain (BTcP) is part of an all-inclusive multidimensional evaluation of the patient. The aim of this narrative review is to explore the relationship between high-rate BTcP, which strongly impacts health- related quality of life and tumour characteristics, in the face of novel approaches that should provide guidance for future clinical practice. The presentation of short, emblematic clinical reports also promotes knowledge of BTcP, which, despite the availability of numerous therapeutic approaches, remains underdiagnosed and undertreated. This article is part of the Management of breakthrough cancer pain Special Issue: https://www.drugsincontext.com/special_issues/management-of-breakthrough-cancer-pain.