Management of Medico-Legal Risks in Digital Health Era: A Scoping Review

Immersive interfaces for clinical applications: current status and future perspective

Digital immersive technologies have become increasingly prominent in clinical research and practice, including medical communication and technical education, serious games for health, psychotherapy, and interfaces for neurorehabilitation. The worldwide enthusiasm for digital health and digital therapeutics has prompted the development and testing of numerous applications and interaction methods. Nevertheless, the lack of consistency in the approaches and the peculiarity of the constructed environments contribute to an increasing disparity between the eagerness for new immersive designs and the long-term clinical adoption of these technologies. Several challenges emerge in aligning the different priorities of virtual environment designers and clinicians. This article seeks to examine the utilization and mechanics of medical immersive interfaces based on extended reality and highlight specific design challenges. The transfer of skills from virtual to clinical environments is often confounded by perceptual and attractiveness factors. We argue that a multidisciplinary approach to development and testing, along with a comprehensive acknowledgement of the shared mechanisms that underlie immersive training, are essential for the sustainable integration of extended reality into clinical settings. The present review discusses the application of a multilevel sensory framework to extended reality design, with the aim of developing brain-centered immersive interfaces tailored for therapeutic and educational purposes. Such a framework must include broader design questions, such as the integration of digital technologies into psychosocial care models, clinical validation, and related ethical concerns. We propose that efforts to bridge the virtual gap should include mixed methodologies and neurodesign approaches, integrating user behavioral and physiological feedback into iterative design phases.

Artificial intelligence and big data from digital health applications: publication trends and analysis

PURPOSE

The integration of big data with artificial intelligence in the field of digital health has brought a new dimension to healthcare service delivery. AI technologies that provide value by using big data obtained in the provision of health services are being added to each passing day. There are also some problems related to the use of AI technologies in health service delivery. In this respect, it is aimed to understand the use of digital health, AI and big data technologies in healthcare services and to analyze the developments and trends in the sector.

DESIGN/METHODOLOGY/APPROACH

In this research, 191 studies published between 2016 and 2023 on digital health, AI and its sub-branches and big data were analyzed using VOSviewer and Rstudio Bibliometrix programs for bibliometric analysis. We summarized the type, year, countries, journals and categories of publications; matched the most cited publications and authors; explored scientific collaborative relationships between authors and determined the evolution of research over the years through keyword analysis and factor analysis of publications. The content of the publications is briefly summarized.

FINDINGS

The data obtained showed that significant progress has been made in studies on the use of AI technologies and big data in the field of health, but research in the field is still ongoing and has not yet reached saturation.

RESEARCH LIMITATIONS/IMPLICATIONS

Although the bibliometric analysis study conducted has comprehensively covered the literature, a single database has been utilized and limited to some keywords in order to reach the most appropriate publications on the subject.

PRACTICAL IMPLICATIONS

The analysis has addressed important issues regarding the use of developing digital technologies in health services and is thought to form a basis for future researchers.

ORIGINALITY/VALUE

In today's world, where significant developments are taking place in the field of health, it is necessary to closely follow the development of digital technologies in the health sector and analyze the current situation in order to guide both stakeholders and those who will work in this field.

The limits of learning engagement and academic leadership within the higher education digitalization process - analysis by using PLS SEM

There is convincing evidence that the learning environments digitalization of tools and equipment ultimately results in the speed and depth learning involvement of academia members, by raising attainment of each of the digital learning experiences. The majority of the research that was conducted on the topic of enhancing the digital skills of learners, which would ultimately lead to an increase in their active engagement, was conducted on students in primary and secondary education, leaving members of higher education outside of the scope of the study. Given the uninterrupted search for academic performance and innovation, the current research considers the technological changes that lead to the transformation of the traditional academic learning environments as previously known. The current paper considers the changes in the learners' engagement in the context of the dually digital transformation of the higher academic multi-institutional digitally-learning enhancements. An important factor to be considered regards the leadership evolution (in terms of teaching) that over time, led to a different speed contextual shift, according to its effectiveness, leading to higher or lower students learning (dis)engagement. The current manuscript aims to examine how the higher education digitalization levels could affect the student's learning engagement, under the close monitoring of the academia leadership styles practice. Data collection and analysis implied at first a qualitative approach by issuing an online-distributed survey that resulted in a number of 2272 valid responses. After performing structural equation modelling and proving a valid assessment tool, the analysis resulted into statistically proving the validity of two main hypotheses according to which students learning engagement has a positive effect on the practice of academic leadership. Additionally, results emphasized the fact that higher education digitalization has altogether a negative effect of students learning engagement. Consequently, the current study stresses on the importance of different peers' categories in the context of higher education institutions performance, with an emphasis on the different levels of students' engagement and the leadership styles evolution and practice, aspects uniformly developing within a continuously digitally transformation of the higher education environment.

Robotics and AI into healthcare from the perspective of European regulation: who is responsible for medical malpractice?

The integration of robotics and artificial intelligence into medical practice is radically revolutionising patient care. This fusion of advanced technologies with healthcare offers a number of significant benefits, including more precise diagnoses, personalised treatments and improved health data management. However, it is critical to address very carefully the medico-legal challenges associated with this progress. The responsibilities between the different players concerned in medical liability cases are not yet clearly defined, especially when artificial intelligence is involved in the decision-making process. Complexity increases when technology intervenes between a person's action and the result, making it difficult for the patient to prove harm or negligence. In addition, there is the risk of an unfair distribution of blame between physicians and healthcare institutions. The analysis of European legislation highlights the critical issues related to the attribution of legal personality to autonomous robots and the recognition of strict liability for medical doctors and healthcare institutions. Although European legislation has helped to standardise the rules on this issue, some questions remain unresolved. We argue that specific laws are needed to address the issue of medical liability in cases where robotics and artificial intelligence are used in healthcare.

Artificial intelligence in colorectal multidisciplinary team meetings. What are the medicolegal implications?

AIM

To give an insight into areas for future development and suggestions in the complexities of incorporation of AI into human colorectal cancer (CRC) care while bringing into focus the importance of clinicians' roles in patient care.

METHODS

Existing literature around AI use in CRC care is reviewed and potential regulatory issues and medicolegal implications around its implementation in CRC multidisciplinary team meetings (MDTs) are identified.

RESULTS

Challenges with patient privacy and confidentiality, patient consent, inequity and bias, patient autonomy, as well as AI system transparency and the liability and accountability issues arising from complications that arise from AI-aided clinical decisions are important focusses associated with the use of AI in CRC MDTs.

CONCLUSION

Consideration of various medicolegal aspects of the use of AI in CRC MDTs is warranted to ensure its safe and smooth incorporation into CRC MDTs. AI function as a clinical decision support system and does not replace professional expertise.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.

Telemedicine with special focus on allergic diseases and asthma-Status 2022: An EAACI position paper

Efficacious, effective and efficient communication between healthcare professionals (HCP) and patients is essential to achieve a successful therapeutic alliance. Telemedicine (TM) has been used for decades but during the COVID-19 pandemic its use has become widespread. This position paper aims to describe the terminology and most important forms of TM among HCP and patients and review the existing studies on the uses of TM for asthma and allergy. Besides, the advantages and risks of TM are discussed, concluding that TM application reduces costs and time for both, HCP and patients, but cannot completely replace face-to-face visits for physical examinations and certain tests that are critical in asthma and allergy. From an ethical point of view, it is important to identify those involved in the TM process, ensure confidentiality and use communication channels that fully guarantee the security of the information. Unmet needs and directions for the future regarding implementation, data protection, privacy regulations, methodology and efficacy are described.

Perception of primary care doctors towards telemedicine in Kuching, Sarawak: A cross-sectional study

Introduction

Telemedicine is the provision of healthcare remotely via information and communications technology (ICT). This study aimed to assess the familiarity and factors related to the perception towards telemedicine and the willingness to practise telemedicine among primary care doctors.

Methods

A multi-centre cross-sectional study was conducted prospectively at all public healthcare clinics across Kuching, Sarawak. A questionnaire was adapted and modified from an overseas validated questionnaire, consisting of four parts: demographic data, familiarity towards telemedicine, factors related to the perception of telemedicine and willingness to implement telemedicine.

Results

A total of 131 doctors were recruited. Of them, 43.5% had never interacted with patients via email, WhatsApp or Telegram, while 68.7% had never attended any conferences, speeches or meetings regarding telemedicine. The doctors had low familiarity towards guidelines, technology and medical applications of telemedicine. The majority agreed on the ability of telemedicine to save patients' time and money, the potential of ICT in healthcare and the necessity during a pandemic but perceived the possibility of technical difficulties. The doctors who had experience in interacting with patients via email, WhatsApp or Telegram (P=0.001) and those who had ≤8 years of working experience (P=0.04) had a significantly better perception towards telemedicine.

Conclusion

Although the familiarity towards telemedicine among public primary care doctors is low, their perception is good in a majority of areas. Adequate technological support and continuous education on telemedicine and its guidelines, especially medicolegal issues, are imperative to adopt and propagate telemedicine in primary care.

AI and professional liability assessment in healthcare. A revolution in legal medicine?

The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the healthcare-delivery landscape. Artificial intelligence may enhance patient safety and improve healthcare outcomes, but it presents notable ethical and legal dilemmas. Moreover, as AI streamlines the analysis of the multitude of factors relevant to malpractice claims, including informed consent, adherence to standards of care, and causation, the evaluation of professional liability might also benefit from its use. Beginning with an analysis of the basic steps in assessing professional liability, this article examines the potential new medical-legal issues that an expert witness may encounter when analyzing malpractice cases and the potential integration of AI in this context. These changes related to the use of integrated AI, will necessitate efforts on the part of judges, experts, and clinicians, and may require new legislative regulations. A new expert witness will be likely necessary in the evaluation of professional liability cases. On the one hand, artificial intelligence will support the expert witness; however, on the other hand, it will introduce specific elements into the activities of healthcare workers. These elements will necessitate an expert witness with a specialized cultural background. Examining the steps of professional liability assessment indicates that the likely path for AI in legal medicine involves its role as a collaborative and integrated tool. The combination of AI with human judgment in these assessments can enhance comprehensiveness and fairness. However, it is imperative to adopt a cautious and balanced approach to prevent complete automation in this field.

Comparative survey among paediatricians, nurses and health information technicians on ethics implementation knowledge of and attitude towards social experiments based on medical artificial intelligence at children's hospitals in Shanghai: a cross-sectional study

OBJECTIVES

Implementing ethics is crucial to prevent harm and promote widespread benefits in social experiments based on medical artificial intelligence (MAI). However, insufficient information is available concerning this within the paediatric healthcare sector. We aimed to conduct a comparative survey among paediatricians, nurses and health information technicians regarding ethics implementation knowledge of and attitude towards MAI social experiments at children's hospitals in Shanghai.

DESIGN AND SETTING

A cross-sectional electronic questionnaire was administered from 1 July 2022 to 31 July 2022, at tertiary children's hospitals in Shanghai.

PARTICIPANTS

All the eligible individuals were recruited. The inclusion criteria were as follows: (1) should be a paediatrician, nurse and health information technician, (2) should have been engaged in or currently participating in social experiments based on MAI, and (3) voluntary participation in the survey.

PRIMARY OUTCOME

Ethics implementation knowledge of and attitude to MAI social experiments among paediatricians, nurses and health information technicians.

RESULTS

There were 137 paediatricians, 135 nurses and 60 health information technicians who responded to the questionnaire at tertiary children's hospitals. 2.4-9.6% of participants were familiar with ethics implementation knowledge of MAI social experiments. 31.9-86.1% of participants held an 'agree' ethics implementation attitude. Health information technicians accounted for the highest proportion of the participants who were familiar with the knowledge of implementing ethics, and paediatricians or nurses accounted for the highest proportion among those who held 'agree' attitudes.

CONCLUSIONS

There is a significant knowledge gap and variations in attitudes among paediatricians, nurses and health information technicians, which underscore the urgent need for individualised education and training programmes to enhance MAI ethics implementation in paediatric healthcare.

Clinicians' Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

INTRODUCTION

The healthcare industry has made significant progress in information technology, which has improved healthcare procedures and brought about advancements in clinical care services. This includes gathering crucial clinical data and implementing intelligent health information management. Artificial Intelligence (AI) has the potential to bolster further existing health information systems, notably electronic health records (EHRs). With AI, EHRs can offer more customized and adaptable roles for patients. This study aims to delve into the current and potential uses of AI and examine the obstacles that come with it.

METHOD

In this study, we employed a qualitative methodology and purposive sampling to select participants. We sought out clinicians who were eager to share their professional insights. Our research involved conducting three focus group interviews, each lasting an hour. The moderator began each session by introducing the study's goals and assuring participants of confidentiality to foster a collaborative environment. The facilitator asked open-ended questions about EHR, including its applications, challenges, and AI-assisted features.

RESULTS

The research conducted by 26 participants has identified five crucial areas of using AI in healthcare delivery. These areas include predictive analysis, clinical decision support systems, data visualization, natural language processing (NLP), patient monitoring, mobile technology, and future and emerging trends. However, the hype surrounding AI and the fact that the technology is still in its early stages pose significant challenges. Technical limitations related to language processing and context-specific reasoning must be addressed. Furthermore, medico-legal challenges arise when AI supports or autonomously delivers healthcare services. Governments must develop strategies to ensure AI's responsible and transparent application in healthcare delivery.

CONCLUSION

AI technology has the potential to revolutionize healthcare through its integration with EHRs and other existing technologies. However, several challenges must be addressed before this potential can be fully realized. The development and testing of complex EHR systems that utilize AI must be approached with care to ensure their accuracy and trustworthiness in decision-making about patient treatment. Additionally, there is a need to navigate medico-legal obligations and ensure that benefits are equitably distributed.

Artificial intelligence in forensic medicine and forensic dentistry

This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

Telehealth for a better service delivery in orthopaedic surgery

Telehealth has the potential to change the way we approach patient care. From virtual consenting to reducing carbon emissions, costs, and waiting times, it is a powerful tool in our clinical armamentarium. There is mounting evidence that remote diagnostic evaluation and decision-making have reached an acceptable level of accuracy and can safely be adopted in orthopaedic surgery. Furthermore, patients' and surgeons' satisfaction with virtual appointments are comparable to in-person consultations. Challenges to the widespread use of telehealth should, however, be acknowledged and include the cost of installation, training, maintenance, and accessibility. It is also vital that clinicians are conscious of the medicolegal and ethical considerations surrounding the medium and adhere strictly to the relevant data protection legislation and storage framework. It remains to be seen how organizations harness the full spectrum of the technology to facilitate effective patient care.

Unleashing the potential of AI for pathology: challenges and recommendations

Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Telemedicine Support for Primary Care Providers versus Usual Care in Patients with Heart Failure: Protocol of a Pragmatic Cluster Randomised Trial within the Brazilian Heart Insufficiency with Telemedicine (BRAHIT) Study

Heart failure is a prevalent condition and a frequent cause of hospital readmissions and poor quality of life. Teleconsultation support from cardiologists to primary care physicians managing patients with heart failure may improve care, but the effect on patient-relevant outcomes is unclear. We aim to evaluate whether collaboration through a novel teleconsultation platform in the Brazilian Heart Insufficiency with Telemedicine (BRAHIT) project, tested on a previous feasibility study, can improve patient-relevant outcomes. We will conduct a parallel-group, two-arm, cluster-randomised superiority trial with a 1:1 allocation ratio, with primary care practices from Rio de Janeiro as clusters. Physicians from the intervention group practices will receive teleconsultation support from a cardiologist to assist patients discharged from hospitals after admission for heart failure. In contrast, physicians from the control group practices will perform usual care. We will include 10 patients per each of the 80 enrolled practices (n = 800). The primary outcome will be a composite of mortality and hospital admissions after six months. Secondary outcomes will be adverse events, symptoms frequency, quality of life, and primary care physicians' compliance with treatment guidelines. We hypothesise that teleconsulting support will improve patient outcomes.

Effects of and Prospects for the Hierarchical Medical Policy in Beijing, China

Hierarchical medical policies are widely used worldwide to reduce healthcare costs, rationalize the use of healthcare resources, and improve accessibility and fairness of healthcare services. However, few case studies have evaluated the effects and prospects of such policies. Medical reform efforts in China have distinct goals and characteristics. Therefore, we investigated the effects of a hierarchical medical policy in Beijing and assessed the future potential of the policy to yield insights for other countries, especially developing countries. Different methods were used to analyze multidimensional data from official statistics, a questionnaire survey of 595 healthcare workers from 8 representative public hospitals in Beijing, a questionnaire survey of 536 patients, and 8 semi-structured interview records. The hierarchical medical policy had strong positive effects on improving access to healthcare services, balancing the workload of healthcare workers in various levels of public hospitals, and improving the management of public hospitals. The remaining obstacles include severe job stress among healthcare workers, the high cost of some healthcare services, and the need for improvement in the development level and service capacity of primary hospitals. This study provides useful policy recommendations regarding the implementation and extension of the hierarchical medical policy, including the need for governments to improve the hospital assessment system and for hospitals to actively participate in developing medical partnerships.

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review

OBJECTIVES

The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type.

METHODS

A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials.

RESULTS

Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety.

CONCLUSION

This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.

A Survey on forensic odontologists' activity in Italy during the COVID-19 pandemic

Few information on virus contagion at the beginning of the covid-19 pandemic led to severe restrictions in the dental and forensic activity in Italy, the introduction of procedural guidelines and implementation of preventive measures. A specific survey on Italian forensic odontologists (FOds) activity was conducted to investigate the COVD-19 pandemic impact on daily practices, the preventive measures adopted to manage the risks of contagion procedures performed on living and dead people and the possible peculiar cases that required the intervention of a medical examiner. A total of 122 FOds answered, mostly males over 46 years coming from northern Italy. The results highlight the lack of specific guidelines for the procedures on living people compared to those on the dead but the regulations for the daily clinical practice resulted more than sufficient: in fact, more than 80% of FOds adopted the preventive and safety measures provided for dental practices. The forensic activity significantly decreased during the initial period (more than 75%) and gradually normalized to pre-pandemic numbers in approximately 50% of cases after the implementation of the vaccination campaign. 13 cases of occupational contagion have been reported, most of them (more than 85%) in northern and central Italy. In two cases members of the dental staff sued the employer for responsibility in the contagion. The decrease of the overall ID activity during the pandemic time can be more likely attributed to the of the dental data than the real impact of the pandemic regulations. The use of telematic tools, such as teleconferences, for many procedures proved to be an important resource useful for application even in post-pandemic times.

Molecular autopsy: Twenty years of post-mortem diagnosis in sudden cardiac death

In the forensic medicine field, molecular autopsy is the post-mortem genetic analysis performed to attempt to unravel the cause of decease in cases remaining unexplained after a comprehensive forensic autopsy. This negative autopsy, classified as negative or non-conclusive, usually occurs in young population. In these cases, in which the cause of death is unascertained after a thorough autopsy, an underlying inherited arrhythmogenic syndrome is the main suspected cause of death. Next-generation sequencing allows a rapid and cost-effectives genetic analysis, identifying a rare variant classified as potentially pathogenic in up to 25% of sudden death cases in young population. The first symptom of an inherited arrhythmogenic disease may be a malignant arrhythmia, and even sudden death. Early identification of a pathogenic genetic alteration associated with an inherited arrhythmogenic syndrome may help to adopt preventive personalized measures to reduce risk of malignant arrhythmias and sudden death in the victim's relatives, at risk despite being asymptomatic. The current main challenge is a proper genetic interpretation of variants identified and useful clinical translation. The implications of this personalized translational medicine are multifaceted, requiring the dedication of a specialized team, including forensic scientists, pathologists, cardiologists, pediatric cardiologists, and geneticists.

Clinician and computer: a study on doctors' perceptions of artificial intelligence in skeletal radiography

BACKGROUND

Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians' confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography.

METHODS

A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review.

RESULTS

The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20).

CONCLUSIONS

This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI.

Multiomics, artificial intelligence, and precision medicine in perinatology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Contrast Agents during Pregnancy: Pros and Cons When Really Needed

Many clinical conditions require radiological diagnostic exams based on the emission of different kinds of energy and the use of contrast agents, such as computerized tomography (CT), positron emission tomography (PET), magnetic resonance (MR), ultrasound (US), and X-ray imaging. Pregnant patients who should be submitted for diagnostic examinations with contrast agents represent a group of patients with whom it is necessary to consider both maternal and fetal effects. Radiological examinations use different types of contrast media, the most used and studied are represented by iodinate contrast agents, gadolinium, fluorodeoxyglucose, gastrographin, bariumsulfate, and nanobubbles used in contrast-enhanced ultrasound (CEUS). The present paper reports the available data about each contrast agent and its effect related to the mother and fetus. This review aims to clarify the clinical practices to follow in cases where a radiodiagnostic examination with a contrast medium is indicated to be performed on a pregnant patient.

Art and digital technologies to support resilience during the oncological journey: The Art4ART project

•

Digital technologies can be useful in welcoming patients using the beauty of art.

•

Cancer patients typically need to be supported in their treatment pathway.

•

Digital entertainment can become a resilience-enhancing strategy.

•

Art4ART project offers an art-based digital supporting patients’ resilience.

•

Art4ART offers a research platform about the role of humanities as cure.

Introduction

New digital technologies can become a tool for welcoming the patient through the artistic dimension. Cancer patients, in particular, need support that accompanies and supports them throughout their treatment.

Materials and methods

The Art4ART project consist in the structural proposal to cancer patients of a web-based digital platform containing several forms of art as video-entertainments; a multimedia immersive room; an art-based welcoming of the patients with several original paintings; an environment with a peacefulness vertical garden; a reconceptualization of the chemotherapy-infusion seats. Data regarding patients’ preference and choices will be stored and analysed also using artificial intelligence (AI) algorithm to measure and predict impact indicators regarding clinical outcomes (survival and toxicity), psychological indicators. Moreover, the same digital platform will contribute to a better organization of the activities.

Discussion

Through the systematic acquisition of patient preferences and through integration with other clinical parameters, it will be possible to measure the clinical, psychological, organisational, and social impact of the newly implemented Art4ART project. The use of digital technology leads us to apply the reversal of viewpoint from therapeutic acts to patient-centred care.

Ethical and Legal Aspects of Technology-Assisted Care in Neurodegenerative Disease

Technological solutions are increasingly seen as a way to respond to the demands of managing complex chronic conditions, especially neurodegenerative diseases such as Parkinson’s Disease. All of these new possibilities provide a variety of chances to improve the lives of affected persons and their families, friends, and caregivers. However, there are also a number of challenges that should be considered in order to safeguard the interests of affected persons. In this article, we discuss the ethical and legal considerations associated with the use of technology-assisted care in the context of neurodegenerative conditions.

Applications and Current Medico-Legal Challenges of Telemedicine in Ophthalmology

BACKGROUND

The digital revolution is redesigning the healthcare model, and telemedicine offers a good example of the best cost-effectiveness ratio. The COVID-19 pandemic has catalysed the use of the telemedicine. The aim of this review is to describe and discuss the role and the main applications of telemedicine in the ophthalmic clinical practice as well as the related medico-legal aspects.

METHODS

45 original articles and 5 reviews focused on this topic and published in English language from 1997 and 2021 were searched on the online databases of Pubmed, Scopus, Web of Sciences and Embase, by using the following key words: "telemedicine", "privacy", "ophthalmology", "COVID-19" and "informed consent".

RESULTS

Telemedicine is able to guarantee patient care using information and communication technologies. Technology creates an opportunity to link doctors with the aim of assessing clinical cases and maintaining high standards of care while performing and saving time as well. Ophthalmology is one of the fields in which telemedicine is most commonly used for patient management.

CONCLUSIONS

Telemedicine offers benefits to patients in terms of saving time and costs and avoiding physical contact; however, it is necessary to point out significant limitations such as the absence of physical examinations, the possibility of transmission failure and potential violations of privacy and confidentiality.