Abdul Halim, A. A., Andrew, A. M., Yasin, M. N., Rahman, M. A., Jusoh, M., Veeraperumal, V., Rahim, H. A., Illahi, U., Karim, M. K. A., & Scavino, E. (2021). Existing and emerging breast cancer detection technologies and their challenges: A review. Applied Sciences, 11(22), 10753.
The purpose of this article is to review the screening methods for breast cancer used worldwide and the technological advancements incorporated to enhance care delivery. Additionally, it examines the progression and challenges that AI presents in diagnosing this ailment. It presents and compares the state of the art and performance between signal and image processing. It utilizes numerous recent scholarly articles from various databases to offer evidence on this topic.
The study’s findings suggest that the use of AI in the early diagnosis of breast cancer can play a significant role in ensuring proper treatment and a significant reduction in women’s deaths. The current clinical techniques available for this condition, such as mammography, ultrasound, and magnetic resonance imaging, have numerous reliability issues and, hence, are ineffective. It also reveals that the use of AI can help address the challenges of disease discerning in various ways, including early detection.
One significant challenge this review has is incorporating some outdated journal articles, which might affect its credibility. Moreover, further research is needed to construct predictive models of care. Nevertheless, it is relevant to advance nursing practice as it offers insight into new tools that can be employed in care delivery and the solutions they might bring.
Barrios, C. H. (2022). Global Challenges in Breast Cancer Detection and Treatment. The Breast, 62(Suppl 1), S3-S6.
The purpose of this article is to investigate the challenges that healthcare organizations report in the treatment and detection of breast cancer. According to this review, while this condition is a universal issue and its burden is increasing significantly, reports suggest that it will account for more incidence and mortality in the future decades, especially among underserved populations. It calls upon low- and middle-income countries (LMIC) to find solutions to their healthcare systems’ limited resources. It presents a well-researched and informed perspective on this condition. It draws evidence from available data, statistics, and global health perspectives.
While the article utilizes credible evidence to support its argument on global challenges in detecting and treating breast cancer, it could benefit from the inclusion of specific examples and case studies to further substantiate its claims. This could help illustrate how other nations have addressed these issues and how LMICs can incorporate them.
Moreover, there is limited discussion of cultural factors, especially now that the world is experiencing globalization. A more in-depth discussion of these elements could make the article more comprehensive. However, based on the topic, this study sheds light on dealing with this urgent priority and why early intervention is necessary. It underscores the value of training, education, and technology-based approaches.
Chopra, S., Khosla, M., & Vidya, R. (2023). Innovations and challenges in breast cancer care: A review. Medicina, 59(5), 957.
Chopra et al. (2023) aim to explore recent reviews on innovations and challenges in breast cancer care, including diagnosis and symptom management. According to this article, the development of technology has significantly revolutionized the detection, treatment, and survival associated with these conditions. These advancements include minimally invasive surgical techniques, imaging tools, targeted therapies, personalized medicine, multidisciplinary care, and radiation therapy. This study utilizes peer-reviewed, published scientific literature and ongoing research from the MEDLINE database to achieve its objectives. Based on English-written sources alone, the authors considered all evidence.
After reviewing the required literature, this study concluded that various innovations, including AI, radiotherapy, and others, play a significant role in detecting breast cancer, personalizing treatment, and predicting the response to treatment. However, these advancements have unique challenges and limitations that must be addressed. A significant critique of this research is the reliance on English-written sources, which may have limited its comprehensiveness.
Moreover, while it discusses issues related to cost and accessibility, it does not address the disparities that underserved people face. This article is relevant to advanced nursing practice, especially in oncology. For instance, raising awareness on how technology can benefit breast cancer care equips nurses to offer evidence-based care.
Coccia, M. (2019). Artificial intelligence technology in cancer imaging: Clinical challenges for detection of lung and breast cancer. Journal of Social and Administrative Sciences, 6(2), 82-98.
This article aims to demonstrate that AI-related techniques in imaging can aid pathologists in detecting cancer subtypes, metastases, and gene mutations. It employs two case studies to demonstrate the application and effectiveness of this innovation in distinguishing and characterizing breast and lung cancers. It also draws on data from ScienceDirect (2019) to illustrate the evolutionary pathways of AI and deep learning in the field of cancer (Coccia, 2019). It incorporates numerous keywords to find relevant titles and abstracts for the research.
The results of this study suggest that high mortality rates associated with cancer have influenced the use of AI technology trajectories in imaging. When they are utilized, they facilitate early detection and characterization, enabling the efficient application of anticancer therapies. The new advancement has the potential to bring about an innovative paradigm shift in the diagnostic assessment of this condition.
AI has been utilized in medical image analysis and histopathological identification in medicine, proving efficient for human experts. While this review is comprehensive and detailed, it is limited to a single database and only two case studies, which may limit the generalizability of the findings. Nevertheless, this article is pertinent to advanced nursing practice as it lays a foundation and highlights the potential of AI to improve care for cancer patients.
Dileep, G., & Gyani, S. G. (2022). Artificial intelligence in breast cancer screening and diagnosis. Cureus, 14(10), e30318.
This article aims to review the application of AI in detecting breast cancer at an early stage. It will employ a narrative approach to retrieve information from previously conducted studies, sourced from various databases, including PubMed, Google Scholar, Elsevier, Sci-Hub, and other relevant databases. Most of the sources incorporated in this research are written in English and have been published within the last 15 years. The research compares the results in the literature to obtain more solid evidence on the topic.
The findings suggest that incorporating AI into breast cancer screening has facilitated early detection of the disease. Technological development works on this condition in various ways, including radiomics, deep learning, and machine learning. They are innovative and can help pathologists deliver quality patient care and expedite the process of obtaining results. However, it is linked to privacy violations, ethical risks, and more.
One weakness of this article is that it does not thoroughly discuss the solutions to the adverse impacts of AI. However, this article is relevant to advanced nursing practice because it highlights the other side of innovations. The high incidence of this disease is associated with various factors, including lifestyle, genetics, and environmental influences. Therefore, medical professionals must have the tools to conduct proper screening for early diagnosis and treatment.
Fozza, A., De Rose, F., De Santis, M. C., Meattini, I., Meduri, B., D’angelo, E., Dei, D., Figlia, V., La Rocca, E., Fregatti, P., Satragno, C., Belgioia, L., & Giaj-Levra, N. (2023). Technological advancements and future perspectives in breast cancer radiation therapy. Expert review of anticancer therapy, 23(4), 407–419.
This article notes that breast cancer is one of the most common tumors affecting women from all over the globe, especially those from underserved communities. Over the years, medical professionals have been utilizing radiation therapy to manage it. Moreover, recent advancements have led to the introduction of technology options to enhance target definition, therapeutic index, and patient selection. This study aims to summarize these innovations and their effectiveness in managing breast cancer. It relies on numerous recent studies on managing this condition and the future of technology.
The findings of this article suggest that the introduction of technological advancements can support the prescription of postoperative partial breast cancer treatment. Additionally, image-guided radiotherapy plays a crucial role in enhancing the effectiveness of these interventions. With the global healthcare system experiencing the development of hybrid magnetic resonance linear accelerators in recent periods, adaptive planning, target volume outline procedures, and radiomics will be facilitated.
The article concludes that AI holds promise for a brighter future in medicine, particularly in the treatment of cancer. One major weakness of this research is that it reviewed outdated sources, which may affect its overall credibility. However, it provides background on the effects of innovations for breast cancer, which is relevant to advanced nursing practice.
Lalani, N., Alqarni, S., & Jimenez, R. B. (2023). The potential of proton therapy for locally advanced breast cancer: Clinical and technical considerations. Current Oncology, 30(3), 2869-2878.
This article aims to provide an overview of proton therapy as a therapeutic modality for locally advanced breast cancer. According to this study, the intervention has unique features that enable the abrupt dose fall-off distal to the target of interest (Lalani et al., 2023). It highlights that the healthcare system has experienced improvements in oncology care due to the introduction of many approaches to treat cancer. It utilizes numerous recent articles from various databases to offer evidence on clinical and technical considerations of proton radiotherapy.
The findings of this study suggest that, unlike traditional interventions, which have numerous challenges, such as unintentional dose deposition, proton radiotherapy is a promising solution due to its unique properties. It ensures effective and precise allocation of medication while sparing normal tissues. One limitation of this article is that it does not thoroughly explore the potential negative implications of this intervention, thereby limiting its ability to offer an objective perspective. However, it is still relevant to advance nursing practice in oncology, especially breast cancer. Since nurses play a significant role in caring for and educating patients about their treatment options, this source helps them achieve their goals. It continues to update these healthcare professionals on the advancements in the medical field.
Mäurer, M., Schott, D., Pizon, M., Drozdz, S., Wendt, T., Wittig, A., & Pachmann, K. (2022). Increased circulating epithelial tumor cells (CETC/CTC) over the course of Adjuvant radiotherapy is a predictor of less favorable outcomes in patients with early-stage breast cancer. Current Oncology, 30(1), 261-273.
This article notes that adjuvant radiotherapy (RT) is a common multidisciplinary treatment strategy component for breast cancer in its early stages. It plays a significant role in reducing the incidence of loco-regional recurrence and distant events. Its purpose is to explore changes in the number of circulating epithelial tumor cells (CETC/CTC), which can reach far-reaching tissues and regrow into metastases during RT.
It also evaluates if CETC/CTCs are available in breast cancer patients who have correlated distant metastases and local recurrence. The study involved analyzing blood samples twice (0-6 weeks before and after using RT) from 165 patients irradiated between 2002 and 2012. The participants were followed for a median of 8.97 years.
The results suggest that participants with higher CETC/CTC numbers have worse disease-free survival compared to others. CETC/CTC behavior was the best element in determining subsequent relapse-free survival. The maniac method showed that many breast cancer patients who underwent surgery had high levels of this factor. This is a potential predictor that can help determine if a patient is at risk of having this medical condition.
One weakness of this article is its reliance on primary data and the inclusion of a limited number of participants. It would be challenging to generalize such data to a broader population. However, it shows that the introduction of technology in healthcare would help detect breast cancer at an early stage, which is relevant to advanced nursing practice.
Mehrotra, R., & Yadav, K. (2022). Breast cancer in India: Present scenario and the challenges ahead. World Journal of Clinical Oncology, 13(3), 209-218.
This study is based in India, where breast cancer has become the top cause of death since the 1990s, leading to high mortalities. It reviews numerous studies to help understand the elements that have contributed to making this condition a significant burden in the country. Most of the sources utilized in this research are dated within the last century, making their information more credible. Moreover, it highlights the importance of early diagnosis and treatment modalities, as well as the limitations of the Indian healthcare system over the years (Mehrotra & Yadav, 2022). It underscores new interventions and what the future of managing breast cancer holds, especially with the impact of COVID-19.
The findings of this study suggest that the burden of breast cancer is growing at a higher rate, and countries need to prepare effectively. Some of the ways that this can be attained are by implementing a national cancer screening program and a robust awareness campaign. They must also address the shortage of a skilled workforce and the infrastructure needs. This will involve upgrading healthcare workers’ skills and the adoption of new technologies. One weakness of this article is its limited focus on COVID-19, which narrows its perspective. The research nevertheless builds on the progress made in managing breast cancer, making it relevant to advance nursing practice.
Potnis, K. C., Ross, J. S., Aneja, S., Gross, C. P., & Richman, I. (2022). Artificial intelligence in breast cancer screening: Evaluation of FDA device regulation and future recommendations. JAMA Internal Medicine, 182(12), 1306-1312.
This article aims to describe the current US Food and Drug Administration (FDA) regulatory process for AI tools used in various fields, including oncology. It also summarizes evidence on all the steps in the guidelines and considers the advantages and disadvantages of these rules. It uses this agency’s premarket notifications and other publicly available documents. The data will span from January 1, 2017, to December 31, 2021. Nine products that this body has approved for identifying this ailment were included in the project. The main variables measured were specificity, sensitivity, and area under the curve.
The study’s findings suggested an essential gap in data sources, dataset type, clinical utility assessment, and validation approach reporting. More regulations are needed to govern these AI tools effectively, as they also raise significant ethical concerns. One weakness of this article is that it relies on a single source of information and only includes publicly available data. It is relevant to advanced nursing practice, as it advocates for strengthening FDA evidentiary standards, which are crucial for ensuring patient safety and efficacy. It makes it clear that even after AI applications are approved for screening this condition, the point of doubt lies in their appropriateness and accuracy, and their clinical utility remains a concern.
Rabiei, R., Ayyoubzadeh, S. M., Sohrabei, S., Esmaeili, M., & Atashi, A. (2022). Prediction of breast cancer using machine learning approaches. Journal of Biomedical Physics & Engineering, 12(3), 297–308.
This study aims to forecast this disease using various machine learning approaches, applying laboratory, demographic, and mammographic data. To achieve this, it will utilize analysis of 5178 independent records from the Motamed Cancer Institute (ACECR) database, 25% of which pertain to patients with this condition. The research utilized genetic algorithms (GA), gradient boosting trees (GBT), neural networks (MLP), and random forests (RF). These models were first trained with laboratory and demographic features, followed by all of them, including mammographic properties.
The findings suggest that machine learning has significant potential in predicting breast cancer from features extracted from data. It also reports that RF has a higher efficiency compared to other approaches used. Moreover, the gradient boosting performed stronger than the neural network. These results implied that the best way to predict breast cancer and detect it earlier is to combine multiple risk factors in modeling the necessary care plan.
These elements ensure the collection, storage, and management of intelligent systems and data, making care delivery effective. This research has two limitations: the use of only one database and the inability to access and utilize genetic data. However, the article’s comprehensive description of the effectiveness of new technologies in healthcare makes it relevant for advanced nursing practice.
Taylor, C. R., Monga, N., Johnson, C., Hawley, J. R., & Patel, M. (2023). Artificial intelligence applications in breast imaging: Current status and future directions. Diagnostics, 13(12), 2041.
This paper aims to investigate the application of AI in breast imaging and its potential to enhance diagnosis and treatment. It also aims to paint a picture of what the future holds for technological development in this field. It utilizes articles dated within the last century from numerous credible databases to find evidence that justifies the topic. It is based on the fact that for more than 20 years, professionals in the medical field have been attempting to use computers to detect breast malignancies.
The results of this study suggest that incorporating AI into breast imaging would enhance the screening process. Some of the advantages of AI integration include risk assessment, decision-making support, workflow and triage efficiency, breast density quantitation, image enhancement, chemotherapy assessment, and quality evaluation (Taylor et al., 2023). One weakness in this research is the inclusion of some outdated sources in its review.
However, this article’s comprehensive and detailed perspective is commendable. It is relevant to advanced nursing practice because it provides a glimpse into the future of breast cancer management. It gives hope to everyone, as it enables the early detection of malignancies and provides appropriate treatment. This also forms a foundation for future studies on this topic and for finding solutions related to AI’s adverse impacts.