Artificial vision for the blind may be feasible by interfacing a television camera with electronics stimulating the visual cortex. The status of a major collaborative effort involving the College of Physicians and Surgeons of Columbia University, the University of Utah, and the University of Western Ontario is reviewed. Results have been very encouraging, although much work remains to be done.
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http://dx.doi.org/10.1227/00006123-197910000-00022 | DOI Listing |
Front Comput Neurosci
January 2025
Data Science and Analytics Innovation Center, University of Missouri-Kansas City, Kansas City, MO, United States.
Ophthalmic Genet
January 2025
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, N.Y, US.
Background: Retinoblastoma is diagnosed and treated without biopsy based solely on appearance (with the indirect ophthalmoscope and imaging). More than 20 benign ophthalmic disorders resemble retinoblastoma and errors in diagnosis continue to be made worldwide. A better noninvasive method for distinguishing retinoblastoma from pseudo retinoblastoma is needed.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
Glaucoma is the leading cause of irreversible blindness globally. Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across diverse identity groups.
View Article and Find Full Text PDFNat Med
January 2025
Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process.
View Article and Find Full Text PDFRev Neurosci
January 2025
557765 Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN ), Tehran, Iran.
The recognition and classification of facial expressions using artificial intelligence (AI) presents a promising avenue for early detection and monitoring of neurodegenerative disorders. This narrative review critically examines the current state of AI-driven facial expression analysis in the context of neurodegenerative diseases, such as Alzheimer's and Parkinson's. We discuss the potential of AI techniques, including deep learning and computer vision, to accurately interpret and categorize subtle changes in facial expressions associated with these pathological conditions.
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