Artificial Intelligence and Surgery: Ethical Dilemmas and Open Issues

Background: Artificial Intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threates, we summarize the main ethical issues that may arise from applying AI to surgery, starting from the Ethics Guidelines for Trustworthy Artificial Intelligence framework recently promoted by the European Commission. Study Design: A modified Delphi process has been employed to achieve expert consensus. Results: The main ethical issues that arise from applying AI to surgery, described in detail herein, relate to human agency, accountability for errors, technical robustness, privacy and data governance, transparency, diversity, non-discrimination, and fairness. It may be possible to address many of these ethical issues by expanding the breadth of surgical AI research to focus on implementation science. The potential for AI to disrupt surgical practice suggests that formal digital health education is becoming increasingly important for surgeons and surgical trainees. Conclusions: A multidisciplinary focus on implementation science and digital health education is desirable to balance opportunities offered by emerging AI technologies and respect for the ethical principles of a patient-centric philosophy

Opioid Misuse: A Review of the Main Issues, Challenges, and Strategies

In the United States, from 1999 to 2019, opioid overdose, either regularly prescribed or illegally acquired, was the cause of death for nearly 500,000 people. In addition to this pronounced mortality burden that has increased gradually over time, opioid overdose has significant morbidity with severe risks and side effects. As a result, opioid misuse is a cause for concern and is considered an epidemic. This article examines the trends and consequences of the opioid epidemic presented in recent international literature, reflecting on the causes of this phenomenon and the possible strategies to address it. The detailed analysis of 33 international articles highlights numerous impacts in the social, public health, economic, and political spheres. The prescription opioid epidemic is an almost exclusively North American problem. This phenomenon should be carefully evaluated from a healthcare systems perspective, for consequential risks and harms of aggressive opioid prescription practices for pain management. Appropriate policies are required to manage opioid use and prevent abuse efficiently. Examples of proper policies vary, such as the use of validated questionnaires for the early identification of patients at risk of addiction, the effective use of regional and national prescription monitoring programs, and the proper dissemination and translation of knowledge to highlight the risks of prescription opioid abuse

Stronger security notions for decentralized traceable attribute-based signatures and more efficient constructions

We revisit the notion of Decentralized Traceable Attribute-Based Signatures (DTABS) introduced by El Kaafarani et al. (CT-RSA 2014) and improve the state-of-the-art in three dimensions: Firstly, we provide a new stronger security model which circumvents some shortcomings in existing models. Our model minimizes the trust placed in attribute authorities and hence provides, among other things, a stronger definition for non-frameability. In addition, our model captures the notion of tracing soundness which is important for many applications of the primitive. Secondly, we provide a generic construction that is secure w.r.t. our strong security model and show two example instantiations in the standard model which are more efficient than existing constructions (secure under weaker security definitions). Finally, we dispense with the need for the expensive zero-knowledge proofs required for proving tracing correctness by the tracing authority. As a result, tracing a signature in our constructions is significantly more efficient than existing constructions, both in terms of the size of the tracing proof and the computational cost required to generate and verify it. For instance, verifying tracing correctness in our constructions requires only 4 pairings compared to 34 pairings in the most efficient existing construction

Opioid Misuse: A Review of the Main Issues, Challenges, and Strategies

In the United States, from 1999 to 2019, opioid overdose, either regularly prescribed or illegally acquired, was the cause of death for nearly 500,000 people. In addition to this pronounced mortality burden that has increased gradually over time, opioid overdose has significant morbidity with se-vere risks and side effects. As a result, opioid misuse is a cause for concern and is considered an epidemic. This article examines the trends and consequences of the opioid epidemic presented in recent international literature, reflecting on the causes of this phenomenon and the possible strat-egies to address it. The detailed analysis of 33 international articles highlights numerous impacts in the social, public health, economic, and political spheres. The prescription opioid epidemic is an almost exclusively North American problem. This phenomenon should be carefully evaluated from a healthcare systems perspective, for consequential risks and harms of aggressive opioid prescrip-tion practices for pain management. Appropriate policies are required to manage opioid use and prevent abuse efficiently. Examples of proper policies vary, such as the use of validated question-naires for the early identification of patients at risk of addiction, the effective use of regional and national prescription monitoring programs, and the proper dissemination and translation of knowledge to highlight the risks of prescription opioid abuse

Outcomes in patients with gunshot wounds to the brain.

Introduction:Gunshot wounds to the brain (GSWB) confer high lethality and uncertain recovery. It is unclear which patients benefit from aggressive resuscitation, and furthermore whether patients with GSWB undergoing cardiopulmonary resuscitation (CPR) have potential for survival or organ donation. Therefore, we sought to determine the rates of survival and organ donation, as well as identify factors associated with both outcomes in patients with GSWB undergoing CPR. Methods:We performed a retrospective, multicenter study at 25 US trauma centers including dates between June 1, 2011 and December 31, 2017. Patients were included if they suffered isolated GSWB and required CPR at a referring hospital, in the field, or in the trauma resuscitation room. Patients were excluded for significant torso or extremity injuries, or if pregnant. Binomial regression models were used to determine predictors of survival/organ donation. Results:825 patients met study criteria; the majority were male (87.6%) with a mean age of 36.5 years. Most (67%) underwent CPR in the field and 2.1% (n=17) survived to discharge. Of the non-survivors, 17.5% (n=141) were considered eligible donors, with a donation rate of 58.9% (n=83) in this group. Regression models found several predictors of survival. Hormone replacement was predictive of both survival and organ donation. Conclusion:We found that GSWB requiring CPR during trauma resuscitation was associated with a 2.1% survival rate and overall organ donation rate of 10.3%. Several factors appear to be favorably associated with survival, although predictions are uncertain due to the low number of survivors in this patient population. Hormone replacement was predictive of both survival and organ donation. These results are a starting point for determining appropriate treatment algorithms for this devastating clinical condition. Level of evidence:Level II

Surgeons’ perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

Background: Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons’ knowledge and perception of using AI-based tools in clinical decision-making processes. Methods: An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society’s website and Twitter profile. Results: 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons’ preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion: The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI

Efficient Attribute-Based Signatures for Unbounded Arithmetic Branching Programs

This paper presents the first attribute-based signature (ABS) scheme in which the correspondence between signers and signatures is captured in an arithmetic model of computation. Specifically, we design a fully secure, i.e., adaptively unforgeable and perfectly signer-private ABS scheme for signing policies realizable by arithmetic branching programs (ABP), which are a quite expressive model of arithmetic computations. On a more positive note, the proposed scheme places no bound on the size and input length of the supported signing policy ABP’s, and at the same time, supports the use of an input attribute for an arbitrary number of times inside a signing policy ABP, i.e., the so called unbounded multi-use of attributes. The size of our public parameters is constant with respect to the sizes of the signing attribute vectors and signing policies available in the system. The construction is built in (asymmetric) bilinear groups of prime order, and its unforgeability is derived in the standard model under (asymmetric version of) the well-studied decisional linear (DLIN) assumption coupled with the existence of standard collision resistant hash functions. Due to the use of the arithmetic model as opposed to the boolean one, our ABS scheme not only excels significantly over the existing state-of-the-art constructions in terms of concrete efficiency, but also achieves improved applicability in various practical scenarios. Our principal technical contributions are (a) extending the techniques of Okamoto and Takashima [PKC 2011, PKC 2013], which were originally developed in the context of boolean span programs, to the arithmetic setting; and (b) innovating new ideas to allow unbounded multi-use of attributes inside ABP’s, which themselves are of unbounded size and input length

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

Background Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons’ knowledge and perception of using AI-based tools in clinical decision-making processes. Methods An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society’s website and Twitter profile. Results 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI

Further Lower Bounds for Structure-Preserving Signatures in Asymmetric Bilinear Groups

Structure-Preserving Signatures (SPSs) are a useful tool for the design of modular cryptographic protocols. Recent series of works have shown that by limiting the message space of those schemes to the set of Diffie-Hellman (DH) pairs, it is possible to circumvent the known lower bounds in the Type-3 bilinear group setting thus obtaining the shortest signatures consisting of only 2 elements from the shorter source group. It has been shown that such a variant yields efficiency gains for some cryptographic constructions, including attribute-based signatures and direct anonymous attestation. Only the cases of signing a single DH pair or a DH pair and a vector from $\Z_p$ have been considered. Signing a vector of group elements is required for various applications of SPSs, especially if the aim is to forgo relying on heuristic assumptions. An open question is whether such an improved lower bound also applies to signing a vector of $\ell > 1$ messages. We answer this question negatively for schemes existentially unforgeable under an adaptive chosen-message attack (EUF-CMA) whereas we answer it positively for schemes existentially unforgeable under a random-message attack (EUF-RMA) and those which are existentially unforgeable under a combined chosen-random-message attack (EUF-CMA-RMA). The latter notion is a leeway between the two former notions where it allows the adversary to adaptively choose part of the message to be signed whereas the remaining part of the message is chosen uniformly at random by the signer. Another open question is whether strongly existentially unforgeable under an adaptive chosen-message attack (sEUF-CMA) schemes with 2-element signatures exist. We answer this question negatively, proving it is impossible to construct sEUF-CMA schemes with 2-element signatures even if the signature consists of elements from both source groups. On the other hand, we prove that sEUF-RMA and sEUF-CMA-RMA schemes with 2-element (unilateral) signatures are possible by giving constructions for those notions. Among other things, our findings show a gap between random-message/combined chosen-random-message security and chosen-message security in this setting

EuraHS: the development of an international online platform for registration and outcome measurement of ventral abdominal wall hernia repair

Background Although the repair of ventral abdominal wall hernias is one of the most commonly performed operations, many aspects of their treatment are still under debate or poorly studied. In addition, there is a lack of good definitions and classifications that make the evaluation of studies and meta-analyses in this field of surgery difficult. Materials and methods Under the auspices of the board of the European Hernia Society and following the previously published classifications on inguinal and on ventral hernias, a working group was formed to create an online platform for registration and outcome measurement of operations for ventral abdominal wall hernias. Development of such a registry involved reaching agreement about clear definitions and classifications on patient variables, surgical procedures and mesh materials used, as well as outcome parameters. The EuraHS working group (European registry for abdominal wall hernias) comprised of a multinational European expert panel with specific interest in abdominal wall hernias. Over five working group meetings, consensus was reached on definitions for the data to be recorded in the registry. Results A set of well-described definitions was made. The previously reported EHS classifications of hernias will be used. Risk factors for recurrences and co-morbidities of patients were listed. A new severity of comorbidity score was defined. Post-operative complications were classified according to existing classifications as described for other fields of surgery. A new 3-dimensional numerical quality-of-life score, EuraHS-QoL score, was defined. An online platform is created based on the definitions and classifications, which can be used by individual surgeons, surgical teams or for multicentre studies. A EuraHS website is constructed with easy access to all the definitions, classifications and results from the database. Conclusion An online platform for registration and outcome measurement of abdominal wall hernia repairs with clear definitions and classifications is offered to the surgical community. It is hoped that this registry could lead to better evidence-based guidelines for treatment of abdominal wall hernias based on hernia variables, patient variables, available hernia repair materials and techniques