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Mysteries, Epistemological Modesty, and Artificial Intelligence in Surgery
Tyler J Loftus1, Gilbert R Upchurch1, Daniel Delitto1
1Department of Surgery, University of Florida Health, Gainesville, FL, United States.
Frontiers in Artificial Intelligence
|October 29, 2020
Summary
Many complex biological problems are treated as puzzles, not mysteries. This approach, common in predictive analytics and surgical phenotyping, often leads to failure by overlooking data complexity.
Area of Science:
- Immunology
- Computational Biology
- Medical Informatics
Background:
- Life presents puzzles solvable with linear data, and mysteries requiring complex interpretation.
- Distinguishing between puzzles and mysteries is crucial for effective problem-solving.
- Current predictive analytics and phenotyping in surgery often misclassify mysteries as puzzles.
Purpose of the Study:
- To highlight the critical difference between puzzles and mysteries in scientific inquiry.
- To critique the application of puzzle-solving methodologies to complex biological and medical problems.
- To advocate for nuanced analytical approaches in fields like immunology and surgical phenotyping.
Main Methods:
- Conceptual analysis differentiating puzzle-solving from mystery-solving.
- Illustrative examples from infectious disease and immune response modulation.
- Critique of traditional predictive analytics and phenotyping strategies in surgery.
Main Results:
- Puzzles are solved through linear data assimilation.
- Mysteries require deep, complex data interpretation.
- Treating mysteries as puzzles, by gathering more data linearly, is an unsuccessful strategy.
Conclusions:
- A paradigm shift is needed in how complex biological and medical challenges are approached.
- Recognizing and addressing the inherent complexity of mysteries is essential for scientific advancement.
- Novel analytical frameworks are required for effective predictive analytics and phenotyping in surgery.
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