The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews

Transformer-based framework for multi-class segmentation of skin cancer from histopathology images

Introduction

Non-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas.

Method

In this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods.

Results

The evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system.

Discussion

This study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.

ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain

BACKGROUND

ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4's risk stratification of simulated patients with acute nontraumatic chest pain.

METHODS

Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency.

RESULTS

ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45-48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605).

CONCLUSION

While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.

Artificial intelligence in cancer diagnosis: Opportunities and challenges

Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.