Legal Aspects of the Use Artificial Intelligence in Telemedicine

Objective: the rapid expansion of the use of telemedicine in clinical practice and the increasing use of Artificial Intelligence has raised many privacy issues and concerns among legal scholars. Due to the sensitive nature of the data involved particular attention should be paid to the legal aspects of those systems. This article aimed to explore the legal implication of the use of Artificial Intelligence in the field of telemedicine, especially when continuous learning and automated decision-making systems are involved; in fact, providing personalized medicine through continuous learning systems may represent an additional risk. Particular attention is paid to vulnerable groups, such as children, the elderly, and severely ill patients, due to both the digital divide and the difficulty of expressing free consent.Methods: comparative and formal legal methods allowed to analyze current regulation of the Artificial Intelligence and set up its correlations with the regulation on telemedicine, GDPR and others.Results: legal implications of the use of Artificial Intelligence in telemedicine, especially when continuous learning and automated decision-making systems are involved were explored; author concluded that providing personalized medicine through continuous learning systems may represent an additional risk and offered the ways to minimize it. Author also focused on the issues of informed consent of vulnerable groups (children, elderly, severely ill patients).Scientific novelty: existing risks and issues that are arising from the use of Artificial Intelligence in telemedicine with particular attention to continuous learning systems are explored.Practical significance: results achieved in this paper can be used for lawmaking process in the sphere of use of Artificial Intelligence in telemedicine and as base for future research in this area as well as contribute to limited literature on the topic

Regulating Smart Robots and Artificial Intelligence in the European Union

Objective: In recent years, the need for regulation of robots and Artificial Intelligence has become apparent in Europe. European Union needs a standardized regulation that will ensure a high level of security in robotics systems to prevent potential breaches. Therefore a new regulation should make clear that it is the responsibility of producers to identify the blind spots in these systems, exposing their flaws, or, when a vulnerability is discovered in a later stage, to update the system even if that model is not on the market anymore. This article aims at suggesting some possible revisions of the existing legal provisions in the EU.Methods: The author employed the Kestemont legal methodology, analyzing legal text, comparing them, and connecting them with technical elements regarding smart robots, resulting in the highlighting of the critical provisions to be updated.Results: This article suggests some revisions to the existing regulatory proposals: according to the author, although the AI Act and the Cyberresilience Act represent a first step towards this direction, their general principles are not sufficiently detailed to guide programmers on how to implement them in practice, and policymakers should carefully assess in what cases lifelong learning models should be allowed to the market. The author suggests that the current proposal regarding mandatory updates should be expanded, as five years are a short time frame that would not cover the risks associated with long-lasting products, such as vehicles.Scientific novelty: The author has examined the existing regulatory framework regarding AI systems and devices with digital elements, highlighted the risks of the current legal framework, and suggested possible amendments to the existing regulatory proposals.Practical significance: The article can be employed to update the existing proposals for the AI Act and the Cyber-resilience Act

MSR32 COVID-19 Beds’ Occupancy and Hospital Complaints: A Predictive Model

Objectives COVID-19 pandemic limited the number of patients that could be promptly and adequately taken in charge. The proposed research aims at predicting the number of patients requiring any type of hospitalizations, considering not only patients affected by COVID-19, but also other severe viral diseases, including untreated chronic and frail patients, and also oncological ones, to estimate potential hospital lawsuits and complaints. Methods An unsupervised learning approach of artificial neural network’s called Self-Organizing Maps (SOM), grounding on the prediction of the existence of specific clusters and useful to predict hospital behavioral changes, has been designed to forecast the hospital beds’ occupancy, using pre and post COVID-19 time-series, and supporting the prompt prediction of litigations and potential lawsuits, so that hospital managers and public institutions could perform an impacts’ analysis to decide whether to invest resources to increase or allocate differentially hospital beds and humans capacity. Data came from the UK National Health Service (NHS) statistic and digital portals, concerning a 4-year time horizon, related to 2 pre and 2 post COVID-19 years. Results Clusters revealed two principal behaviors in selecting the resources allocation. In case of increase of non-COVID hospitalized patients, a reduction in the number of complaints (-55%) emerged. A higher number of complaints was registered (+17%) against a considerable reduction in the number of beds occupied (-26%). Based on the above, the management of hospital beds is a crucial factor which can influence the complaints trend. Conclusions The model could significantly support in the management of hospital capacity, helping decision-makers in taking rational decisions under conditions of uncertainty. In addition, this model is highly replicable also in the estimation of current hospital beds, healthcare professionals, equipment, and other resources, extremely scarce during emergency or pandemic crises, being able to be adapted for different local and national settings

Preventing litigation with a predictive model of COVID-19 ICUs occupancy

The COVID-19 pandemic has generated an overall slowdown in hospital activities that might lead to delays in healthcare interventions, and the scarcity of resources can raise concerns about ventilators allocation criteria. These circumstances could lead to lawsuits against hospitals and healthcare professionals: together with Regions and States, they may be vulnerable to legal actions, due to the breach of right to health, to physical integrity and right to life, to the manifestation of the informed consent in the medical field or on the basis of contractual or Aquilian obligations. In this context, predicting the litigation rate could be useful to assess the economic impact of a dispute at a local and national level, so that hospital managers and public institutions can perform multi-dimensional and cost/benefit evaluations to decide whether to invest resources to increase critical care surge capacity. In this work we present CLIP (COVID-19 LItigation Prediction), a modeling approach supported by swarm intelligence designed to forecast the occupancy of intensive care units using COVID-19 time-series. CLIP fits a logistic model of COVID-19 patients admission in order to estimate the future number of patients, and then exploits a probabilistic model to predict the number of occupied intensive care beds, whose parameters are calibrated by means of Fuzzy Self-Tuning Particle Swarm Optimization. We assume that each individual rejected from an intensive care unit due to the lack of resources should be considered a potential plaintiff. The development and the availability of such a predictive model, that could further be used within other clinical conditions and important diseases, could help policy-makers in taking decisions under conditions of uncertainty

Predicting and Characterizing Legal Claims of Hospitals with Computational Intelligence: the Legal and Ethical Implications

In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre- and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial Intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C- Means and swarm intelligence. The dataset that we analyse allows us to compare pre- and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field