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Future of Artificial Intelligence (AI) - Machine Learning (ML) Trends in Pathology and Medicine.

Mod Pathol

January 2025

Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, Pittsburgh, PA. Electronic address:

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of medicine. Healthcare organizations are now starting to establish management strategies for integrating such platforms (AI-ML toolsets) which leverage the computational power of advanced algorithms to analyze data and to provide better insights which ultimately translates to enhanced clinical decision-making and improved patient outcomes. Emerging AI-ML platforms and trends in pathology and medicine are reshaping the field by offering innovative solutions to enhance diagnostic accuracy, operational workflows, clinical decision support, and clinical outcomes.

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The ability to add bioactivities, such as cell signaling or ligand recognition, to biomaterials has generated the potential to include multiple bioactivities into a single material. In some cases, it is desirable to localize these activities to different areas of the biomaterial, creating functional patterns. While photolithography and 3D printing have been effective techniques for patterning functions in many materials, patterning remains a challenge in materials composed of protein, in part due to how these materials are artificially assembled.

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Tuberculosis (TB) is the leading cause of death from a single infectious agent. The burden is highest in some low- and middle-income countries. One-quarter of the world's population is estimated to have been infected with TB, which is the seedbed for progressing from TB infection to the deadly and contagious disease itself.

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Background: Collaborative research with end-users is an effective way to generate meaningful research applications and support greater impact on practice and knowledge exchange. To address these needs, a Citizen Advisory Group (CAG) of nine older adults (ages 64-80, 67% women) was formed to advise scientists on the development of Brain Health PRO (BHPro), a web-based platform designed to increase dementia prevention literacy and awareness. The current study evaluated if the CAG met its objectives, how inclusion of the CAG aligned with collaborative research approaches, and the CAG's experience and satisfaction throughout the development process.

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