Residual antigenicity of xenograft tissue after glutaraldehyde tanning may be a factor that determines calcification and durability of bioprostheses. We have pursued the concept of a nonantigenic, noncalcifying, more durable bioprosthesis. We previously described a technique for rapid intraoperative fabrication of an autogenous tissue heart valve (ATHV). That technique has been modified to improve reliability and ease of learning. With the modified technique, a geometrically perfect trileaflet valve can be made in 5 minutes. Although any suitable tissue can be used, the pericardial ATHV is the subject of this report. Autogenous pericardium immersed for 5 minutes in glutaraldehyde has proven satisfactory for valve construction. In vitro testing in the pulse duplicator and accelerated life tester has shown that the stent assembly is capable of function beyond 800,000,000 cycles without failure. In vivo testing has been performed in the juvenile sheep model as described by the National Institutes of Health group. Five sheep were maintained for 5 months postimplant before sacrifice. Explanted valves showed no tissue thickening or shrinkage, problems seen with earlier valves made with untreated autogenous tissue, and the leaflets remained pliable, free of the degenerative changes usually seen in the sheep model.
Applying machine-based learning and synthetic cognition, commonly referred to as artificial intelligence (AI), to medicine intimates prescient knowledge. The ability of these algorithms to potentially unlock secrets held within vast data sets makes them invaluable to healthcare. Complex computer algorithms are routinely used to enhance diagnoses in fields like oncology, cardiology, and neurology. These algorithms have found utility in making healthcare decisions that are often complicated by seemingly endless relationships between exogenous and endogenous variables. They have also found utility in the allocation of limited healthcare resources and the management of end-of-life issues. With the increase in computing power and the ability to test a virtually unlimited number of relationships, scientists and engineers have the unprecedented ability to increase the prognostic confidence that comes from complex data analysis. While these systems present exciting opportunities for the democratization and precision of healthcare, their use raises important moral and ethical considerations around Christian concepts of autonomy and hope. The purpose of this essay is to explore some of the practical limitations associated with AI in medicine and discuss some of the potential theological implications that machine-generated diagnoses may present. Specifically, this article examines how these systems may disrupt the patient and healthcare provider relationship emblematic of Christ's healing mission. Finally, this article seeks to offer insights that might help in the development of a more robust ethical framework for the application of these systems in the future.
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