United States

In the rapidly advancing field of AI-assisted medical diagnosis, the generation of medical reports for Chest X-rays (CXR) has significantly improved with the increased availability of radiographs and their corresponding reports. However, these reports often contain complex medical terminology, making them difficult for patients and non-healthcare professionals to understand. In this study, we introduce a strategy called Chained Prompting for Improved Readability of Medical Reports (CPIR-MR), which translates original medical reports into more comprehensible language. Our primary contribution is the creation of a new extension to the IU X-Ray dataset, providing Simplified Medical Reports (SMRs) generated by CPIR-MR. Additionally, we demonstrate that standard methodologies can effectively produce these simplified reports by proposing a multi-modal text decoder (MTD) that combines BLIP with a classification network to generate simplified medical explanations (SMEs) when fine-tuned on SMRs.

AAAI 2025

From Complexity to Clarity: Transforming Chest X-ray Reports with Chained Prompting (Student Abstract)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Large language models (LLMs) are trained on vast amounts of publicly available text. However, the current training frameworks take for granted that these annotations are accurate reflections of the authors’ true intents. This study questions that assumption by examining the gaps between writers’ actual psychological states and the inferences made by third-party annotators. We explore how readers interpret psychological cues in text and demonstrate that third-person annotations often fail to align with first-person realities. By integrating both first- and third-person annotations, we develop computational models that reveal significant biases in how psychological states are perceived and the downstream effects these perceptions have on reader behavior. Our findings challenge the foundational assumptions of LLM training, suggesting that the reliance on potentially flawed third-person annotations could impact model accuracy and real-world applications.

Understanding Annotator Perception: Modeling Psychological Inference from First- and Third-Person Annotations (Student Abstract)

Achieving optimal design is a crucial aspect of any design process for safe and efficient operation. Such tasks typically require numerous simulations over many iterations, which can become computationally expensive. This paper proposes a novel method that combines Physics-informed Neural Networks (PINNs) with a Genetic Algorithm to optimize the parameters of an airfoil that aims to achieve favourable aerodynamic conditions. Traditional solvers are computationally expensive for performing such tasks, but using PINNs can significantly reduce this while keeping accuracy high. The proposed approach shows the advantage of using PINNs in optimizing complex engineering problems.

A Hybrid Framework for Airfoil Optimization: Combining PINNs and Genetic Algorithm (Student Abstract)

Machine unlearning is becoming increasingly important as deep models become more prevalent, particularly when there are frequent requests to remove the influence of specific training data due to privacy concerns or erroneous sensing signals. Spatial-temporal Graph Neural Networks, in particular, have been widely adopted in real-world applications that demand efficient unlearning, yet research in this area remains in its early stages. In this paper, we introduce STEPS, a framework specifically designed to address the challenges of spatio-temporal graph unlearning. Our results demonstrate that STEPS not only ensures data continuity and integrity but also significantly reduces the time required for unlearning, while minimizing the accuracy loss in the new model compared to a model with 0% unlearning.

Efficient Unlearning for Spatio-temporal Graph (Student Abstract)

Probing classifiers are a technique for understanding and modifying the operation of neural networks in which a smaller classifier is trained to use the model's internal representation to learn a related probing task. Similar to a neural electrode array, training probing classifiers can help researchers both discern and edit the internal representation of a neural network. This paper presents an evaluation of the use of probing classifiers to modify the internal hidden state of a chess-playing transformer. 
We demonstrate that intervention vector scaling should follow a negative exponential according to the length of the input to ensure model outputs remain semantically valid after editing the residual stream activations.

Scaling Effects on Latent Representation Edits in GPT Models (Student Abstract)

In this paper, we present a new image similarity search algorithm designed to enhance traditional information retrieval(IR) by adding an image search capability. Our approach uses a quadtree data structure to organize image data, significantly reducing search space and improving retrieval efficiency. We describe an indexing strategy and two query algorithms that can be implemented in any IR system. We tested our method on a 70K material microscopy image dataset, achieving a 25 times improvement in retrieval speed with only a 20% reduction in ranking accuracy.

Efficient Image Similarity Search with Quadtrees (Student Abstract)

Conformal Prediction (CP) is an uncertainty quantification framework that provides prediction sets with a user-specified probability to include the true class in the prediction set. This guarantee on the user-specified probability is known as marginal coverage. Marginal coverage refers to the probability that the true label is included in the prediction set, averaged over all test samples. However, this can lead to inconsistent coverage across different classes, constraining its suitability for high-stakes applications such as pathological workflows. This study implements a Classwise CP method applied to two cancer datasets to achieve class-conditional coverage which ensures that each class has a user-specified probability of being included in the prediction set when it is the true label. Our results demonstrate the effectiveness of this approach through a significant reduction in the average class coverage gap compared to the Baseline CP method.

Ensuring Class-Conditional Coverage for Pathological Workflows (Student Abstract)

Diffusion Models (DMs) offer robust tools for addressing uncertainty and enhancing adaptability in robotics. This work explores their application to trajectory generation, 3D image synthesis, and interpretable scene understanding. For trajectory planning, we propose using colored Gaussian noise to improve robustness and temporal coherence. In 3D image generation, Transfer Entropy enhances information flow between textual and visual modalities for more coherent outputs. Partial Information Decomposition (PID) is leveraged to improve model interpretability and efficiency in scene generation. Rigorous evaluation will assess trajectory quality, robustness, and real-world transferability, aiming to advance autonomous decision-making and scene understanding in robotics.

Diffusion Models for Robotics

Healthcare diagnostics, especially in underserved communities, faces critical gaps in accessibility and accuracy. African Americans experience significant disparities in mental health care, often receiving delayed or inadequate treatment. This research proposes a diagnostic copilot, an AI-powered assistant designed to work alongside healthcare professionals. Using Knowledge-Infused Learning (KIL) and multi-turn conversations, the system integrates clinical knowledge and patient input to deliver actionable, explainable diagnoses in real-time. By engaging with both patients and clinicians, the copilot aims to reduce disparities, enhance trust, and improve diagnostic accuracy in mental health care.

Knowledge-Infused Learning for Developing a Mental Health Diagnostic Copilot in Healthcare Systems

This project leverages Visual Question Answering (VQA) to promote Peruvian gastronomy by utilizing a culturally rich dataset and advanced models such as LLaVA-1.5 and GPT-2 Large. The evaluation will comprise both automated metrics and culinary expert assessments. This system addresses regional variations in dish names, promotes inclusivity by involving Peruvians from diverse regions in dataset construction, and enhances cultural representation.

Visual Question Answering for Peruvian Cuisine in Regional Spanish

The global expansion of Artificial Intelligence (AI) has highlighted significant challenges in inclusivity and representation, particularly for underrepresented communities. Current AI systems often fail to accommodate diverse linguistic and cultural contexts, resulting in biases in name pronunciation, language preservation, and communication. This research proposes a framework for advancing inclusivity in AI through Natural Language Processing (NLP) and Reinforcement Learning (RL). The envisioned system could integrate with home assistants like Siri and Alexa, enabling real-time interactions in local languages while maintaining cultural relevance. Key proposed features include accurate pronunciation of names, conversational capabilities in underrepresented languages, and an interactive platform where users can learn their language, history, and cultural heritage. By leveraging transformer-based models and adaptive RL frameworks, this research aims to explore solutions that bridge the gap in AI inclusivity for low-resource languages and culturally diverse populations.

Downloads

Next from AAAI 2025

Understanding Annotator Perception: Modeling Psychological Inference from First- and Third-Person Annotations (Student Abstract)

Similar lecture

Nothing Abnormal: Disambiguating Medical Reports via Contrastive Knowledge Infusion