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Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence.
Sci Rep
January 2025
Research Innovation and Entrepreneurship Unit, University of Buraimi, 512, Buraimi, Oman.
Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing.
View Article and Find Full Text PDFOpening the AI Black Box: Distilling Machine-Learned Algorithms into Code.
Entropy (Basel)
December 2024
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Can we turn AI black boxes into code? Although this mission sounds extremely challenging, we show that it is not entirely impossible by presenting a proof-of-concept method, MIPS, that can synthesize programs based on the automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a benchmark of 62 algorithmic tasks that can be learned by an RNN and find it highly complementary to GPT-4: MIPS solves 32 of them, including 13 that are not solved by GPT-4 (which also solves 30). MIPS uses an integer autoencoder to convert the RNN into a finite state machine, then applies Boolean or integer symbolic regression to capture the learned algorithm.
View Article and Find Full Text PDFFirst Report of Postharvest Fruit Rot on Passion Fruit () caused by in the U.S.A.
Plant Dis
January 2025
University of Florida Tropical Research and Education Center, Plant Pathology, 1615 SE 23rd Way, Homestead, Florida, United States, 33031-3314;
The commercial production of passion fruit is geographically limited (California, Florida, and Hawaii), but the development of cold-tolerant varieties could expand it beyond warm-climate states (Stafne et.al. 2023).
View Article and Find Full Text PDFEngineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency.
J Pathol Inform
December 2024
Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia.
Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. This assessment is still manually performed by trained specialists, who may be a bottleneck within the clinical process. Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation.
View Article and Find Full Text PDFIn response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be given to the guidance issuing from these programs in order to do the work set out for them would mean that clinical staff would not be empowered to provide meaningful safeguards against it in those cases when its recommendations are morally problematic.
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