Computer systems for medical diagnosis based on machine learning are not mere science fiction. Despite undisputed potential benefits, such systems may also raise problems. Two (interconnected) issues are particularly significant from an ethical point of view: The first issue is that epistemic opacity is at odds with a common desire for understanding and potentially undermines information rights. The second (related) issue concerns the assignment of responsibility in cases of failure. The core of the two issues seems to be that understanding and responsibility are concepts that are intrinsically tied to the discursive practice of giving and asking for reasons. The challenge is to find ways to make the outcomes of machine learning algorithms compatible with our discursive practice. This comes down to the claim that we should try to integrate discursive elements into machine learning algorithms. Under the title of "explainable AI" initiatives heading in this direction are already under way. Extensive research in this field is needed for finding adequate solutions.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268052 | PMC |
| http://dx.doi.org/10.1002/hbm.24886 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Non-Newtonian lacuno-canalicular fluid flow in bone altered by mechanical loading and magnetic field.
Comput Methods Biomech Biomed Engin
January 2025
Department of Mathematics, National Institute of Technology Uttarakhand, Srinagar, India.
As humans age, they experience deformity and a decrease in their bone strength, such brittleness in the bones ultimately lead to bone fracture. Magnetic field exposure combined with physical exercise may be useful in mitigating age-related bone loss by improving the canalicular fluid motion within the bone's lacuno-canalicular system (LCS). Nevertheless, an adequate amount of fluid induced shear stress is necessary for the bone mechano-transduction and solute transport in the case of brittle bone diseases.
View Article and Find Full Text PDFDeconvoluting thermomechanical effects in X-ray diffraction data using machine learning.
Acta Crystallogr A Found Adv
March 2025
Pennsylvania State University, University Park, PA 16802, USA.
X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis.
View Article and Find Full Text PDFQuantitative Prediction of Protein-Polyelectrolyte Binding Thermodynamics: Adsorption of Heparin-Analog Polysulfates to the SARS-CoV-2 Spike Protein RBD.
JACS Au
January 2025
Department of Physics, Freie Universität Berlin, Arnimallee 14, Berlin 14195, Germany.
Interactions of polyelectrolytes (PEs) with proteins play a crucial role in numerous biological processes, such as the internalization of virus particles into host cells. Although docking, machine learning methods, and molecular dynamics (MD) simulations are utilized to estimate binding poses and binding free energies of small-molecule drugs to proteins, quantitative prediction of the binding thermodynamics of PE-based drugs presents a significant obstacle in computer-aided drug design. This is due to the sluggish dynamics of PEs caused by their size and strong charge-charge correlations.
View Article and Find Full Text PDFAI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.
Comput Struct Biotechnol J
January 2025
Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.
Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power.
View Article and Find Full Text PDFData-Driven Maturity Level Evaluation for Cardiomyocytes Derived from Human Pluripotent Stem Cells (Invited Paper).
Electronics (Basel)
December 2024
Department of Mechanical Engineering, City College of New York, New York, NY 10031, USA.
Cardiovascular disease is a leading cause of death worldwide. The differentiation of human pluripotent stem cells (hPSCs) into functional cardiomyocytes offers significant potential for disease modeling and cell-based cardiac therapies. However, hPSC-derived cardiomyocytes (hPSC-CMs) remain largely immature, limiting their experimental and clinical applications.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!