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Smart Imitator: Learning from Imperfect Clinical Decisions.
J Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
Toward expert-level medical question answering with large language models.
Nat Med
January 2025
Google Research, Mountain View, CA, USA.
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval.
View Article and Find Full Text PDFArtificial intelligence empowered voice generation for amyotrophic lateral sclerosis patients.
Sci Rep
January 2025
NeMO Lab, ASST GOM Niguarda Cà Granda Hospital, Milan, Italy.
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that can result in a progressive loss of speech due to bulbar dysfunction, which can have significant negative impact on the patient's mental well-being. Alternative Augmentative Communication (AAC) strategies based on synthetic voices have been shown to assist patients in maintaining communication and improving their Quality of Life (QoL). However, such synthetic voices are often perceived as impersonal and fail to capture the unique voice and identity of the patient.
View Article and Find Full Text PDFNovel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction.
Sci Rep
January 2025
Department of Computer Science, Faculty of Computers and Informatics, Kafrelsheikh University, Kafrelsheikh, Egypt.
Missing pixel imputation is a critical task in image processing, where the presence of high percentages of missing pixels can significantly degrade the performance of downstream tasks such as image segmentation and object detection. This paper introduces a novel approach for missing pixel imputation based on Generative Adversarial Networks (GANs). We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data.
View Article and Find Full Text PDFReal-time lung extraction from synthesized x-rays improves pulmonary image-guided radiotherapy.
Phys Med Biol
January 2025
Radiation Oncology, University of California San Francisco, 1600 Divisadero St, San Francisco, California, 94143, UNITED STATES.
Lung tumors can be obscured in X-rays, preventing accurate and robust localization. To improve lung conspicuity for image-guided procedures, we isolate the lungs in the anterior-posterior (AP) X-rays using a lung extraction network (LeX-net) that virtually removes overlapping thoracic structures, including ribs, diaphragm, liver, heart, and trachea. Approach: 73,965 thoracic 3DCTs and 106 thoracic 4DCTs were included.
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