There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.
The functional units of natural photosynthetic systems control the process of converting sunlight into chemical energy. In this article, we explore a series of chemically and structurally modified bacteriochlorophyll and chlorophyll pigments through computational chemistry to evaluate their electronic spectroscopy properties. More specifically, we use multiconfigurational and time-dependent density functional theory methods, along with molecular dynamics simulations, to compute the models' energetics both in an implicit and explicit solvent environment.
Constructed wetlands, serving as artificially simulated natural wetland water treatment systems, have emerged as effective technologies for ecologically treating wastewater. Biochar, a carbon material derived from biomass waste pyrolysis, possesses significant specific surface area, abundant functional groups, and high stability. The integration of biochar into artificial wetland systems enhances the removal efficiency of pollutants.
ConspectusOrganic mixed ionic electronic conductors (OMIECs) represent an exciting and emerging class of materials that have recently revitalized the field of organic semiconductors. OMIECs are particularly attractive because they allow both ionic and electronic transport while retaining the inherent benefits of organic semiconducting materials such as mechanical conformability and biocompatibility. These combined properties make the OMIECs ideal for applications in bioelectronics, energy storage, neuromorphic computing, and electrochemical transistors for sensing.
Objective: This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions.
Methods: The study employs a comprehensive literature review, examining existing research on AI in healthcare, and synthesizes findings from various surveys and studies that explore the attitudes of patients and doctors towards AI applications in medical settings.
Results: The analysis reveals that patient anxiety encompasses algorithm aversion, robophobia, lack of humanistic care, challenges in human-machine interaction, and concerns about AI's universal applicability.
Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing.
