Abstract Background The integration of large language models (LLMs) like ChatGPT in diagnostic medicine, with a focus on digital pathology, has garnered significant attention. However, understanding the challenges and barriers associated with the use of LLMs in this context is crucial for their successful implementation. Methods A scoping review was conducted to explore the challenges and barriers of using LLMs, in diagnostic medicine with a focus on digital pathology. A comprehensive search was conducted using electronic databases, including PubMed and Google Scholar, for relevant articles published within the past four years. The selected articles were critically analyzed to identify and summarize the challenges and barriers reported in the literature. Results The scoping review identified several challenges and barriers associated with the use of LLMs in diagnostic medicine. These included limitations in contextual understanding and interpretability, biases in training data, ethical considerations, impact on healthcare professionals, and regulatory concerns. Contextual understanding and interpretability challenges arise due to the lack of true understanding of medical concepts and lack of these models being explicitly trained on medical records selected by trained professionals, and the black-box nature of LLMs. Biases in training data pose a risk of perpetuating disparities and inaccuracies in diagnoses. Ethical considerations include patient privacy, data security, and responsible AI use. The integration of LLMs may impact healthcare professionals’ autonomy and decision-making abilities. Regulatory concerns surround the need for guidelines and frameworks to ensure safe and ethical implementation. Conclusion The scoping review highlights the challenges and barriers of using LLMs in diagnostic medicine with a focus on digital pathology. Understanding these challenges is essential for addressing the limitations and developing strategies to overcome barriers. It is critical for health professionals to be involved in the selection of data and fine tuning of the models. Further research, validation, and collaboration between AI developers, healthcare professionals, and regulatory bodies are necessary to ensure the responsible and effective integration of LLMs in diagnostic medicine. 

Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology – a recent scoping review

Cancer vaccines have attracted widespread interest in tumor therapy because of the potential to induce an effective antitumor immune response. However, many challenges including weak immunogenicity, off-target effects, and immunosuppressive microenvironments have prevented their broad clinical translation. To overcome these difficulties, effective delivery systems have been designed for cancer vaccines. As carriers in cancer vaccine delivery systems, hydrogels have gained substantial attention because they can encapsulate a variety of antigens/immunomodulators and protect them from degradation. This enables hydrogels to simultaneously reverse immunosuppression and stimulate the immune response. Meanwhile, the controlled release properties of hydrogels allow for precise temporal and spatial release of loads in situ to further enhance the immune response of cancer vaccines. Therefore, this review summarizes the classification of cancer vaccines, highlights the strategies of hydrogel-based cancer vaccines, and provides some insights into the future development of hydrogel-based cancer vaccines.

https://www.science.org/doi/pdf/10.1126/sciadv.adc8738?download=true

Hydrogel-guided strategies to stimulate an effective immune response for vaccine-based cancer immunotherapy

Pulmonary hypertension (PH) is an uncommon yet severe condition characterized by sustained elevation of blood pressure in the pulmonary arteries. The delaying treatment can result in disease progression, right ventricular failure, increased risk of complications, and even death. Early recognition and timely treatment are crucial in halting PH progression, improving cardiac function, and reducing complications. Within this study, we present a highly promising hybrid model, known as bERIME_FKNN, which constitutes a feature selection approach integrating the enhanced rime algorithm (ERIME) and fuzzy K-nearest neighbor (FKNN) technique. The ERIME introduces the triangular game search strategy, which augments the algorithm's capacity for global exploration by judiciously electing distinct search agents across the exploratory domain. This approach fosters both competitive rivalry and collaborative synergy among these agents. Moreover, an random follower search strategy is incorporated to bestow a novel trajectory upon the principal search agent, thereby enriching the spectrum of search directions. Initially, ERIME is meticulously compared to 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark functions across diverse dimensionalities such as 10, 30, 50, and 100, ultimately validating its exceptional optimization capability within the model. Subsequently, employing the color moment and grayscale co-occurrence matrix methodologies, a total of 118 features are extracted from 63 PH patients' and 60 healthy individuals' images, alongside an analysis of 14,514 recordings obtained from these patients utilizing the developed bERIME_FKNN model. The outcomes manifest that the bERIME_FKNN model exhibits a conspicuous prowess in the realm of PH classification, attaining an accuracy and specificity exceeding 99%. This implies that the model serves as a valuable computer-aided tool, delivering an advanced warning system for diagnosis and prognosis evaluation of PH. 

Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension.

It has been evidenced by numerous studies that irrational investor sentiment is one of the critical factors leading to dramatic volatility in financial market prices. Therefore, how to effectively predict market prices by information on investor sentiment is a popular but complex topic for researchers, market investors, and financial regulators. In this research, we aim to investigate the effectiveness of stock index prediction using multiple investor sentiment features, and we propose an advanced price trend prediction and trading simulation approach for the Shanghai Stock Exchange index and the Shenzhen Component index by integrating the Boosting, Bagging, and NSGA-II methods. Additionally, the SHAP method is employed as a model interpretation approach to analyze the importance of the sentiment variables and quantify their contributions to the predictions from both local and global perspectives. According to the experimental results, it can be found that the proposed method outperforms the benchmark methods in terms of the hit ratio, accumulated return, and maximum drawdown. It indicates that the proposed method is capable of achieving high accuracy, low risk, and stable profit in price trend prediction and trading simulation of the Chinese stock indexes. Moreover, the SHAP approach incorporated in the proposed method improved the interpretability of the proposed model, which can provide a beneficial reference for market participants to clarify the important sentiment factors and make relative decisions. 

An Integrated Approach of Ensemble Learning methods for Stock Index Prediction using Investor Sentiments

Immunotherapy is a new type of tumor treatment. In this review, the main focus is on an overview of the research progress of immunotherapy in cancer treatment and the combined application of immunotherapy with other therapeutic approaches. 

Immunotherapy: cancer immunotherapy and its combination with nanomaterials and other therapies

Medical machine learning technology has garnered great attention from both the computer and medical fields. In this study, a multi-objective evolutionary algorithm integrating decomposition and harris hawks learning (MOEA/D-HHL) is presented for medical machine learning; harris hawks learning can guarantee a good variety and systematic MOEA/D-HHL solutions. The performance of this MOEA/D-HHL is first evaluated using these benchmarks (DTLZ1-DTLZ7). In addition, MOEA/D-HHL is used to construct machine learning algorithms for medical cancer gene expression data sets with the following three objectives in mind: selected features, classification accuracy, and correlation measures. The MOEA/D-HHL is finally applied efficiently to the clinical data of lupus nephritis and pulmonary hypertension with the best NMI of 0.9652 and ARI of 0.9749 values on lupus nephritis, and the best NMI of 0.9686 and ARI of 0.9742 values on pulmonary hypertension respectively. On clinically relevant data for lupus nephritis and pulmonary hypertension, the experimental results indicate that the proposed MOEA/D-HHL algorithm outperforms current methods. The statistical results demonstrate that all metrics have predictive capabilities and that the suggested MOEA/D-HHL is more stable for an emerging medical machine learning framework. MOEA/D-HHL may be seen as a promising computer-assisted approach for medical machine learning development. 

Medical machine learning based on multiobjective evolutionary algorithm using learning decomposition

Cancer vaccines are a promising strategy for preventing cancer progression through antitumor immunity activation. Some vaccines, such as Sipuleucel-T (an autologous active cellular immunotherapy), have been applied clinically. However, systemic toxicity and low immunogenicity have restricted their broad clinical application. Furthermore, the off-target effects induced by immunostimulatory adjuvants and cytokines also curtail cancer vaccine applications. Polymers, scaffolds, lipids, microneedles, and other biomaterials offer advantageous properties, such as excellent biocompatibility and biodegradability, flexible size, high loading capacity for immune-related components, and a large surface area for immune ligand binding. Thus, various biomaterials are applied as immune adjuvants in cancer vaccines to enhance cancer antigen immunogenicity, protect cargoes from the surrounding microenvironment, and promote antigen presentation. Herein, we divide biomaterial-enhanced cancer vaccines into peptide and protein vaccines, nucleic acid-based vaccines, tumor cell-based compound vaccine, and virus-based vaccines and summarize advances and challenges associated with the use of biomaterials in cancer vaccines. 

Biomaterial-enhanced cancer vaccines

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies. 

Development of NC Program Simulation Software Based on AutoCAD

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Medical machine learning based on multiobjective evolutionary algorithm using learning decomposition

Biomaterial-enhanced cancer vaccines

Development of NC Program Simulation Software Based on AutoCAD