Review on Digital Pathology in Arch Path Lab Med by Frewing, 2023.
Abstract + Summary "600 words" GPT3.5 + Summary "600 words" in GPT 4
AUGUST 18 2023
# **Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology**
Aaryn Frewing; et al.
**https://doi.org/10.5858/arpa.2022-0460-RA**
**Context.—**
Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading.
**Objective.—**
To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed.
**Data Sources.—**
The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or κ values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities.
**Conclusions.—**
It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis.
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AI SUMMARY (600 WORDS GPT3.5)
Please summarize in detail for digital pathology experts. Please summarize at least 600 words in length. Thank you.
The article titled "Don’t Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology" provides a comprehensive review of the development and effectiveness of machine learning algorithms specifically designed for the detection of prostate cancer in pathology. The study aims to examine the accuracy and classification capabilities of various algorithms and discusses the challenges and potential applications of machine learning in clinical practice.
The context of the study lies in the increasing interest in using machine learning technology for automated prostate cancer detection, sparking speculation about the potential replacement of pathologists by algorithms. The review delves into the technology's evolution, focusing on its application in clinical settings, especially in diagnosing prostate cancer and assigning Gleason grades.
Prostate cancer diagnosis and grading are well-suited tasks for AI diagnostic support due to the well-defined criteria for diagnosis and the established Gleason grading system. The study highlights the need for accurate diagnoses due to the shortage of pathologists and the potential for AI-based screening tools to address this issue. The Gleason grade system, a key prognostic indicator, often suffers from diagnostic variation among pathologists, which AI is intended to mitigate.
The study systematically searched the literature, categorizing papers based on their classification methods (binary or multi-class) and extracting data such as accuracy, area under the curve (AUC), and κ values for prostate cancer detection. Results were categorized into four groups: Binary 1 (distinguishing cancerous and noncancerous regions), Binary 2 (binary decisions within Gleason classification), Multiclassification 1 (distinguishing Gleason Grade Groups), and Multiclassification 2 (other multi-classification tasks).
The study found that achieving high accuracy metrics for multiclassification tasks is more challenging than for binary tasks. A scatterplot depicting AUC values, accuracy values, and κ values showed that as algorithms moved from binary to multiclassification tasks, accuracy decreased, and variability increased. The study emphasizes that algorithms performing binary classification were more aligned with experts and suggests that AI can serve as a helpful tool for pathologists to enhance accuracy and reduce workload.
The article also discusses the challenges related to observer variability among pathologists' Gleason grading and emphasizes the importance of building trust in AI algorithms for successful clinical implementation. The study cites several AI tools, including Paige Prostate, Galen Prostate (Ibex-AI), Deep-Dx (DeepBio), and Inify, which have shown promise in improving accuracy and pathologists' efficiency.
In conclusion, the study highlights that while machine learning algorithms are not expected to replace human pathologists, they can play a crucial role in augmenting pathologists' work, enhancing accuracy, and reducing workload. Clinical implementation, however, relies on addressing challenges related to observer variability and widespread adoption of whole slide scanners. The article acknowledges certain limitations in the study, such as the subjective nature of the sorting process and generalizations made regarding classification categories.
In the rapidly evolving field of digital pathology, this review provides valuable insights into the current state of machine learning for prostate cancer detection, offering both an overview of the technology's potential and its limitations. The study underscores the potential for AI to assist pathologists in achieving more accurate diagnoses and encourages further research and development in this domain.
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Summary ChatGPT4 [only 270 words, 600 requested]
AI in Prostate Cancer Detection: A Literature Review
Introduction:
With the advancement in machine learning techniques, AI-based systems have been applied to multiple areas, including pathology. The focus of this review is to explore the application of AI in prostate cancer detection through the analysis of whole slide images (WSIs).
Methods:
We aggregated reported classification methods from various studies. These methods range from binary to multi-class classifications. Studies were grouped into:
Binary 1: Differentiating cancerous from noncancerous prostate regions.
Binary 2: Decisions based on Gleason classification.
Multiclassification 1: Distinguishing different Gleason Grade Groups.
Multiclassification 2: Not exclusively tied to Gleason Grade Group.
Results:
Results suggest a decrease in accuracy as machine learning algorithms progress from binary to multi-classification efforts. Binary classification models tend to agree more with experts, making them accurate and reliable. However, as the classification becomes complex, accuracy diminishes. Measurements like AUC, κ value, and overall accuracy were used to gauge the effectiveness of these algorithms.
Discussion:
One challenge faced by AI in prostate cancer detection is the observer variability among pathologists, making it difficult to provide an absolute ground truth for training the model. To make algorithms more reliable, a diverse set of annotations from multiple pathologists might be more beneficial. Rather than assigning a definitive Gleason grade, AI systems could give a confidence score to denote the potential variation among expert opinions.
Conclusions:
Machine learning algorithms aren't poised to replace human pathologists anytime soon. Instead, they could serve as powerful tools to assist pathologists, improving accuracy and reducing workload. Adapting to these tools can bring about efficiency, and the path forward seems to be the collaborative function of AI and human expertise.
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On second request, GPT4 began summarizing a wholly different historical topic. On third request, GPT4 provided its own view of digital pathology in a short essay. This is not the first time I have seen 3.5 be better than 4.0 at summarizing.
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