The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations.

Purpose: This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance.

Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive diagnostic procedures.

Literature Review: Cystoscopy is the gold standard for initial macroscopic assessments of the human urinary bladder to rule out (or diagnose) bladder cancer (BCa). Despite having guidelines, cystoscopic findings are diverse and often challenging to classify. The extent of the false negatives and false positives in cystoscopic diagnosis is currently unknown.

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Who of human or robot has its place in space? The robot, because it can replace human beings for exploration missions that are always particularly dangerous both for the health and the safety of astronauts. But human also tends to gain a place in space, when he can be assisted by the robot as a tool that facilitates his work, or when the machine can serve as a medium to extend humanity to the confines of the universe. All these hypotheses raise ethical and legal questions to which the article gives some solutions.

[Chapter 8. Humans or robots in space. Ethical and legal approaches.].

Who of human or robot has its place in space? The robot, because it can replace human beings for exploration missions that are always particularly dangerous both for the health and the safety of astronauts. But human also tends to gain a place in space, when he can be assisted by the robot as a tool that facilitates his work, or when the machine can serve as a medium to extend humanity to the confines of the universe. All these hypotheses raise ethical and legal questions to which the article gives some solutions.

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.

[Robotization of the human under the law].

The robotization of the human implies a more or less intimate hybridization with the machine. When it participates in the repair of the human being, it generates a modification of the legal status of the robot that passes from the category of things to that of personn, which has remarkable effects in terms of civil liability. However, as the therapeutic aspect of hybridization disapears, not only does the robot move away from the category of personn, but the hybrid body raises many questions about fundamental rights.

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.