Generative Artificial Intelligence in Anatomic Pathology.

Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities. To explore the applications, benefits, and challenges of generative AI in anatomic pathology, with a focus on its impact on diagnostic processes, workflow efficiency, education, and research. A comprehensive review of current literature and recent advancements in the application of generative AI within anatomic pathology, categorized into unimodal and multimodal applications, and evaluated for clinical utility, ethical considerations, and future potential. Generative AI demonstrates significant promise in various domains of anatomic pathology, including diagnostic accuracy enhanced through AI-driven image analysis, virtual staining, and synthetic data generation; workflow efficiency, with potential for improvement by automating routine tasks, quality control, and reflex testing; education and research, facilitated by AI-generated educational content, synthetic histology images, and advanced data analysis methods; and clinical integration, with preliminary surveys indicating cautious optimism for nondiagnostic AI tasks and growing engagement in academic settings. Ethical and practical challenges require rigorous validation, prompt engineering, federated learning, and synthetic data generation to help ensure trustworthy, reliable, and unbiased AI applications. Generative AI can potentially revolutionize anatomic pathology, enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. Successful integration into clinical practice will require continued interdisciplinary collaboration, careful validation, and adherence to ethical standards to ensure the benefits of AI are realized while maintaining the highest standards of patient care.

Brodsky V ，Ullah E ，Bychkov A ，Song AH ，Walk EE ，Louis P ，Rasool G ，Singh RS ，Mahmood F ，Bui MM ，Parwani AV ... -  《-》

Multimodal Generative AI for Anatomic Pathology-A Review of Current Applications to Envisage the Future Direction.

This review focuses on the purported applications of multimodal Gen-AI models for anatomic pathology image analysis and interpretation to predict future directions. A scoping review was conducted to explore the applications of multimodal Gen-AI models in advancing histopathology image analysis. A comprehensive search was conducted using electronic databases for relevant articles published within the past year (July 1, 2023 to June 30, 2024). The selected articles were critically analyzed to identify and summarize the applications of multimodal Gen-AI in anatomic pathology image analysis. Multimodal Gen AI models reported in the literature claim moderate to high accuracy on tasks including image classification, segmentation, and text-to-image retrieval. This review demonstrates the potential of multimodal Gen AI models for useful applications in pathology, including assisting with diagnoses, generating data for education and research, and detection of molecular features from anatomic pathology images. These models use data from a few academic institutions thus they require validation on diverse real-world data. There is an urgent need to build consensus models for optimal model performance through multicenter collaboration using a federated learning approach and the use of carefully curated synthetic anatomic pathology data. These models also need to achieve reliability, generalizability and meet the standards required for clinical use. Despite the rigorous need for evaluation and the need to address genuine concerns, multimodal GenAI models present a promising perspective for the advancement and scalability of anatomic pathology.

Ullah E ，Baig MM ，Waqas A ，Rasool G ，Singh R ，Shandilya A ，GholamHossieni H ，Parwani AV ... -  《-》

Harnessing the Power of Generative Artificial Intelligence in Pathology Education: Opportunities, Challenges, and Future Directions.

Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches. To explore the application of generative AI, particularly large language models and multimodal tools, for enhancing pathology education. We describe their potential to create personalized learning experiences, streamline content development, expand access to educational resources, and support both learners and educators throughout the training and practice continuum. We draw on insights from existing literature on AI in education and the collective expertise of the coauthors within this rapidly evolving field. Case studies highlight practical applications of large language models, demonstrating both the potential benefits and unique challenges associated with implementing these technologies in pathology education. Generative AI presents a powerful tool kit for enriching pathology education, offering opportunities for greater engagement, accessibility, and personalization. Careful consideration of ethical implications, potential risks, and appropriate mitigation strategies is essential for the responsible and effective integration of these technologies. Future success lies in fostering collaborative development between AI experts and medical educators, prioritizing ongoing human oversight and transparency to ensure that generative AI augments, rather than supplants, the vital role of educators in pathology training and practice.

Cecchini MJ ，Borowitz MJ ，Glassy EF ，Gullapalli RR ，Hart SN ，Hassell LA ，Homer RJ ，Jackups R Jr ，McNeal JL ，Anderson SR ... -  《-》

Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training.

Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student's specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI's knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners.

Gupta N ，Khatri K ，Malik Y ，Lakhani A ，Kanwal A ，Aggarwal S ，Dahuja A ... -  《BMC Medical Education》

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency.

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

Bhuyan SS ，Sateesh V ，Mukul N ，Galvankar A ，Mahmood A ，Nauman M ，Rai A ，Bordoloi K ，Basu U ，Samuel J ... -  《-》