Operational framework and training standard requirements for AI-empowered robotic surgery.

BACKGROUND: For autonomous robot-delivered surgeries to ever become a feasible option, we recommend the combination of human-centered artificial intelligence (AI) and transparent machine learning (ML), with integrated Gross anatomy models. This can be supplemented with medical imaging data of cadavers for performance evaluation. METHODS: We reviewed technological advances and state-of-the-art documented developments. We undertook a literature search on surgical robotics and skills, tracing agent studies, relevant frameworks, and standards for AI. This embraced transparency aspects of AI. CONCLUSION: We recommend "a procedure/skill template" for teaching AI that can be used by a surgeon. Similar existing methodologies show that when such a metric-based approach is used for training surgeons, cardiologists, and anesthetists, it results in a >40% error reduction in objectively assessed intraoperative procedures. The integration of Explainable AI and ML, and novel tissue characterization sensorics to tele-operated robotic-assisted procedures with medical imaged cadavers, provides robotic guidance and refines tissue classifications at a molecular level.

Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery.

BACKGROUND: This paper aims to move the debate forward regarding the potential for artificial intelligence (AI) and autonomous robotic surgery with a particular focus on ethics, regulation and legal aspects (such as civil law, international law, tort law, liability, medical malpractice, privacy and product/device legislation, among other aspects). METHODS: We conducted an intensive literature search on current or emerging AI and autonomous technologies (eg, vehicles), military and medical technologies (eg, surgical robots), relevant frameworks and standards, cyber security/safety- and legal-systems worldwide. We provide a discussion on unique challenges for robotic surgery faced by proposals made for AI more generally (eg, Explainable AI) and machine learning more specifically (eg, black box), as well as recommendations for developing and improving relevant frameworks or standards. CONCLUSION: We classify responsibility into the following: (1) Accountability; (2) Liability; and (3) Culpability. All three aspects were addressed when discussing responsibility for AI and autonomous surgical robots, be these civil or military patients (however, these aspects may require revision in cases where robots become citizens). The component which produces the least clarity is Culpability, since it is unthinkable in the current state of technology. We envision that in the near future a surgical robot can learn and perform routine operative tasks that can then be supervised by a human surgeon. This represents a surgical parallel to autonomously driven vehicles. Here a human remains in the 'driving seat' as a 'doctor-in-the-loop' thereby safeguarding patients undergoing operations that are supported by surgical machines with autonomous capabilities.

Effect of bacterial inoculation, plant genotype and developmental stage on root-associated and endophytic bacterial communities in potato (Solanum tuberosum).

Beneficial bacteria interact with plants by colonizing the rhizosphere and roots followed by further spread through the inner tissues, resulting in endophytic colonization. The major factors contributing to these interactions are not always well understood for most bacterial and plant species. It is believed that specific bacterial functions are required for plant colonization, but also from the plant side specific features are needed, such as plant genotype (cultivar) and developmental stage. Via multivariate analysis we present a quantification of the roles of these components on the composition of root-associated and endophytic bacterial communities in potato plants, by weighing the effects of bacterial inoculation, plant genotype and developmental stage. Spontaneous rifampicin resistant mutants of two bacterial endophytes, Paenibacillus sp. strain E119 and Methylobacterium mesophilicum strain SR1.6/6, were introduced into potato plants of three different cultivars (Eersteling, Robijn and Karnico). Densities of both strains in, or attached to potato plants were measured by selective plating, while the effects of bacterial inoculation, plant genotype and developmental stage on the composition of bacterial, Alphaproteobacterial and Paenibacillus species were determined by PCR-denaturing gradient gel-electrophoresis (DGGE). Multivariate analyses revealed that the composition of bacterial communities was mainly driven by cultivar type and plant developmental stage, while Alphaproteobacterial and Paenibacillus communities were mainly influenced by bacterial inoculation. These results are important for better understanding the effects of bacterial inoculations to plants and their possible effects on the indigenous bacterial communities in relation with other plant factors such as genotype and growth stage.