artificial general intelligence

BACKGROUND: Hartmann's reversal, a complex elective surgery, reverses and closes the colostomy in individuals who previously underwent a Hartmann's procedure due to colonic pathology like cancer or diverticulitis. It demands careful planning and patient optimisation to help reduce postoperative complications. Preoperative evaluation of body composition has been useful in identifying patients at high risk of short-term postoperative outcomes following colorectal cancer surgery. We sought to explore the use of our in-house derived Artificial Intelligence (AI) algorithm to measure body composition within patients undergoing Hartmann's reversal procedure in the prediction of short-term postoperative complications...

Robust reinforcement learning (RRL) aims to seek a robust policy by optimizing the worst case performance over an uncertainty set. This set contains some perturbed Markov decision processes (MDPs) from a nominal MDP (N-MDP) that generate samples for training, which reflects some potential mismatches between the training simulator (i.e., N-MDP) and real-world settings (i.e., the testing environments). Unfortunately, existing RRL algorithms are only applied to the tabular setting and it is still an open problem to extend them into more general continuous state space...

This article proposes a quantum spatial graph convolutional neural network (QSGCN) model that is implementable on quantum circuits, providing a novel avenue to processing non-Euclidean type data based on the state-of-the-art parameterized quantum circuit (PQC) computing platforms. Four basic blocks are constructed to formulate the whole QSGCN model, including the quantum encoding, the quantum graph convolutional layer, the quantum graph pooling layer, and the network optimization. In particular, the trainability of the QSGCN model is analyzed through discussions on the barren plateau phenomenon...

In radial basis function neural network (RBFNN)-based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting phenomenon. To address this problem, this article proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the classical forgetting mechanisms are recast into a memory mechanism...

OBJECTIVE: To compare the diagnostic efficacy of AI-guided mpMRI-TRUS fusion assisted transperineal systematic biopsy, targeted biopsy and combined biopsy in the diagnosis of PCa, and to evaluate the clinical application value of combined biopsy. METHODS: From April 2022, the general personal information and clinical data of patients with suspicious prostate lesions (PI-RADS≥3) detected by 3.0T mpMRI were collected, then underwent AI-guided mpMRI-TRUS fusion-assisted transperineal prostate biopsy...

OBJECTIVES: To evaluate the accuracy of ChatGPT and Bard in answering pathology examination questions requiring image interpretation. METHODS: The study evaluated ChatGPT-4 and Bard's performance using 86 multiple-choice questions, with 17 (19.8%) focusing on general pathology and 69 (80.2%) on systemic pathology. Of these, 62 (72.1%) included microscopic images, and 57 (66.3%) were first-order questions focusing on diagnosing the disease. The authors presented these artificial intelligence (AI) tools with questions, both with and without clinical contexts, and assessed their answers against a reference standard set by pathologists...

Technological advances raise new puzzles and challenges for cognitive science and the study of how humans think about and interact with artificial intelligence (AI). For example, the advent of large language models and their human-like linguistic abilities has raised substantial debate regarding whether or not AI could be conscious. Here, we consider the question of whether AI could have subjective experiences such as feelings and sensations ('phenomenal consciousness'). While experts from many fields have weighed in on this issue in academic and public discourse, it remains unknown whether and how the general population attributes phenomenal consciousness to AI...

No abstract text is available yet for this article.

Informed consent is a fundamental tenet of patient-centred clinical practice as it upholds the ethical principle of patient autonomy and promotes shared decision-making. In the medicolegal realm, failure to meet the accepted standards of consent can be considered as medical negligence which has both legal and professional implications. In general, valid consent requires three core components: (1) the presence of mental capacity - characterised by the patient's ability to comprehend, retain information, weigh options and communicate the decision, (2) adequate information disclosure - based on the 'reasonable physician' or 'reasonable patient' standards and (3) voluntariness in decision-making...

PURPOSE: The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains. METHODS: An observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain...

In our 2023 paper, entitled "Modeling interactions between the embodied and the narrative self: Dynamics of the self-pattern within LIDA," Kugele, Newen, Franklin, and I propose a functional description and implementation of a central element of Gallagher & Newen's pattern theory of self, which identifies an agent's self with a dynamic pattern of so-called cognitive aspects which govern their thought and behavior (Gallagher, 2013; Newen, 2018; Gallagher & Daly, 2018). The pattern theory explicitly rejects the traditional conceptualization of the self as a unitary entity with certain properties that resides within agents, with the idea of a pattern of aspects being central to its ability to account for the dynamic, yet relatively stable development of most natural agents' selves...

PURPOSE: This American Academy of Ophthalmology Ophthalmic Technology Assessment aims to assess the effectiveness of conventional teleretinal screening (TS) in detecting diabetic retinopathy (DR) and diabetic macular edema (DME). METHODS: A literature search of the PubMed database was conducted most recently in July 2023 to identify data published between 2006 and 2023 on any of the following elements related to TS effectiveness: (1) the accuracy of TS in detecting DR or DME compared with traditional ophthalmic screening with dilated fundus examination or 7-standard field Early Treatment Diabetic Retinopathy Study photography, (2) the impact of TS on DR screening compliance rates or other patient behaviors, and (3) cost-effectiveness and patient satisfaction of TS compared with traditional DR screening...

ChatGPT/GPT-4 is a conversational large language model (LLM) based on artificial intelligence (AI). The potential application of LLM as a virtual assistant for bariatric healthcare professionals in education and practice may be promising if relevant and valid issues are actively examined and addressed. In general medical terms, it is possible that AI models like ChatGPT/GPT-4 will be deeply integrated into medical scenarios, improving medical efficiency and quality, and allowing doctors more time to communicate with patients and implement personalized health management...

BACKGROUND: Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. METHODS: A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases...

With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. With the development of deep learning and GPU technology, the demand for high-precision and high-efficiency 3D reconstruction information is increasing, especially in the fields of unmanned systems, human-computer interaction, virtual reality, and medicine...

The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications...

In recent years, the landscape of diagnostic imaging has undergone a significant transformation with the emergence of home radiology, challenging the traditional paradigm. This shift, bringing diagnostic imaging directly to patients, has gained momentum and has been further accelerated by the global COVID-19 pandemic, highlighting the increasing importance and convenience of decentralized healthcare services. This study aims to offer a nuanced understanding of the attitudes and experiences influencing the integration of in-home radiography into contemporary healthcare practices...

BACKGROUND: Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance. METHODS: From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model...

No abstract text is available yet for this article.

Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g. pixels) that are relevant to the model's decision. These explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by extracting at some intermediate layer of a neural network, subspaces that capture the multiple and distinct activation patterns (e...