Saturday, July 22, 2023

AI Corner: AI Reads and Reports on 50 New Articles on Digital Pathology, AI

Medicare policy for digital pathology and artificial intelligence runs the gamut.  

On one hand, all the new AMA Category III goes get "no pay" bundled status from CMS hopsital outpatient policy, and hospital outpatient centers are where a lot of biopsies originate (here).

On the other hand, there are a number of PLA codes using artificial intelligence or the newest AMA synonym (augmented algorithmic analysis) - here.  At the other extreme, slide-based PLA codes have been priced as high as $5435 (0295U, ADLT pricing, here).  [This is not a bundled service, because ADLT tests are freed from hospital outpatient bundling.]

AI Reads and Reports on 50 Digital Pathology Papers

To get a helicopter view of recent publications in digital pathology and AI, I did the following:

1.  Search PubMed for the two phrases together, "digital pathology" and "artificial intelligence."

2.  Filter for "open access."

3.  Display the first 50 hits, with abstracts.

OK, I then gave those 50 abstracts (using a text splitter) to ChatGPT3.5, and asked it several questions about the consolidated information.

Results are shown below.



A biomedical scientist who is expert in digital pathology has identified these 50 important articles.   Please discuss the state of the field of digital pathology and pathology artificial intelligence, based on your reading of these 50 abstracts

Based on the abstracts of the 50 identified articles, it is evident that the field of digital pathology and pathology artificial intelligence (AI) has made significant advancements and holds great potential for improving diagnostic accuracy, efficiency, and patient outcomes in various areas of pathology. Here are some key observations and insights from the abstracts:

Adoption of Digital Pathology: Digital pathology is becoming increasingly prevalent, with more studies exploring its application in different cancer types and diseases. The use of whole-slide imaging (WSI) and AI algorithms is revolutionizing the way pathologists interpret and analyze histopathological samples.

Improving Diagnostic Accuracy: AI-based tools, such as deep learning models and convolutional neural networks (CNNs), are being developed and applied to assist pathologists in diagnosing various cancers, including prostate, colon, and pancreatic cancers. These tools show promising results in improving diagnostic accuracy and reducing inter-observer variability.

Predicting Biomarkers: AI is proving valuable in predicting important biomarkers, such as microsatellite instability (MSI), BRAF mutation status, and other genetic alterations, in colorectal cancer and other malignancies. These predictions can guide personalized treatment strategies and improve patient outcomes.

Tumor Microenvironment Analysis: AI algorithms are being utilized to assess the immune response within the tumor microenvironment. CD8+ cell density gradients and tumor-infiltrating lymphocytes are being investigated as prognostic indicators in various cancers, including bladder cancer and melanoma.

Cascading Diagnostic Workflow: AI-based tools, like Swin Transformer, are enabling efficient prediction of biomarkers with smaller training datasets, which could reduce the number of samples requiring standard testing. This cascading diagnostic workflow can potentially decrease turnaround times and costs.

Impact on Pathologist Workflows: AI tools are being integrated into pathology workflows to aid in tumor detection, grading, and quantification. The synergistic usage of AI and pathologists' expertise is demonstrated to improve efficiency and reduce the need for additional studies and second opinions.

Tissue Clearing and 3D Reconstruction: Techniques like tissue clearing and 3D reconstruction of digitized serially sectioned slides are providing novel insights into the characteristics of pancreatic cancer, enabling visualization at the cellular level and bridging the gap between bench and bedside.

Code-Free Machine Learning: Code-free ML platforms, such as Microsoft Custom Vision and Google AutoML, are being explored for classifying histopathological images. These platforms show promise in predicting clinically relevant biomarkers, including MSI, with high accuracy.

Challenges and Limitations: Despite the progress made, there are still challenges to overcome, such as the need for large and diverse datasets for training AI models, ensuring generalizability and reproducibility, and addressing ethical and regulatory considerations.

Overall, the field of digital pathology and pathology AI is rapidly evolving and has the potential to transform pathology practice, enabling more precise and personalized patient care. The integration of AI with pathologists' expertise holds promise for enhancing diagnostic accuracy, improving efficiency, and ultimately impacting patient outcomes in the field of pathology. However, further research, validation, and standardization are essential to ensure the safe and effective implementation of AI technologies in routine clinical practice.


Can you discuss the 50 abstracts again, in about twice as much detail.

NOTE TO READER: I was looking for a longer essay, but instead, this triggered AI to give a one or two sentence summary of all 50 abstracts.


Certainly! Let's discuss the 50 abstracts in more detail:

1 Digital Pathology for Cancer Diagnosis:

Multiple abstracts highlight the growing use of digital pathology in cancer diagnosis. Whole-slide imaging (WSI) allows pathologists to view and analyze tissue samples digitally, improving accessibility and collaboration.

2 AI-Assisted Prostate Cancer Diagnosis:

AI tools like Paige Prostate are aiding pathologists in detecting, grading, and quantifying prostate cancer. In a study, pathologists using Paige Prostate showed improved diagnostic accuracy and reduced turnaround time.

3 AI for Brain Tumor Classification:

Researchers are using AI, specifically convolutional neural networks (CNNs), to classify central nervous system (CNS) histopathology images. AI models achieved high accuracy in distinguishing gliomas from brain metastases.

4 AI-Based Colorectal Cancer Biomarker Prediction:

AI models, such as Swin Transformer (Swin-T), are being employed to predict biomarkers in colorectal cancer (CRC) using H&E-stained images. Swin-T showed remarkable efficiency and accuracy even with small training datasets.

5 AI-Driven Pancreatic Cancer Insights:

Tissue clearing and 3D reconstruction of digitized slides are revealing novel insights into pancreatic cancer. This approach allows visualization at the cellular level and has potential clinical implications.

6 AI-Assisted Bladder Cancer Prognosis:

In non-muscle-invasive papillary urothelial carcinoma, CD8+ cell density gradient indicators across the tumor epithelium-stroma interface were explored for prognosis. The findings showed potential for improved risk prediction.

7 AI-Enhanced Prognostic Models:

AI-assisted predictive models are being developed for various cancers. Integrating gradient indicators of CD8+ cell density with clinical data improved the prediction of recurrence-free survival in bladder cancer.

8 AI for Microsatellite Instability Prediction:

AI algorithms, like Swin-T, are being utilized for MSI prediction in colorectal cancer. Swin-T demonstrated robust performance with 5-10 times greater efficiency compared to existing algorithms.

9 AI for Immune Response Analysis:

AI is being used to analyze the immune response in cancer. Evaluating tumor-infiltrating lymphocytes and CD8+ cell density gradients can provide prognostic value in melanoma and other malignancies......

[Truncated, continued to #50]

In summary, the field of digital pathology and pathology AI has made significant strides in improving diagnostic accuracy, efficiency, and personalized medicine approaches. AI tools, such as Swin-T and Paige Prostate, are demonstrating remarkable performance in predicting biomarkers and prognosis in various cancers. Tissue clearing, 3D reconstruction, and the use of AI algorithms have provided new insights into cancer biology. The integration of AI into pathology workflows has the potential to transform the field, improving patient care and outcomes. 

However, challenges remain, including the need for diverse and large datasets, standardization, and ethical considerations. With continued research and validation, AI-driven pathology is poised to play a critical role in the future of cancer diagnosis and treatment.


What appear to be the most impactful applications of AI in digital pathology?


Based on the 50 abstracts discussed, several impactful applications of AI in digital pathology stand out:

Cancer Diagnosis and Classification: AI algorithms, such as convolutional neural networks (CNNs), are proving highly effective in accurately diagnosing and classifying various cancers, including prostate, colorectal, brain, and bladder cancers. AI-driven pathology analysis offers precise tumor subtyping, reducing diagnostic uncertainty and inter-observer variability.

Biomarker Prediction: AI models, like Swin Transformer (Swin-T), show great promise in predicting key biomarkers in cancer, such as microsatellite instability (MSI) in colorectal cancer. These predictions can guide personalized treatment strategies and improve patient outcomes.

Prognostic Models: AI-assisted predictive models, incorporating immune response data and other clinical factors, are improving risk prediction and prognosis in cancer patients. These models have the potential to aid clinicians in developing tailored treatment plans.

Digital Pathology Workflow Improvement: AI integration into pathology workflows is enhancing diagnostic efficiency, reducing turnaround time, and improving accuracy. Code-free AI platforms are making AI-driven pathology analysis more accessible to pathologists.

Immune Response Analysis: AI is facilitating the analysis of the tumor microenvironment and immune response, allowing researchers to better understand the role of immune cells in cancer progression and patient outcomes.

Tissue Clearing and 3D Reconstruction: AI-driven 3D reconstruction of digitized slides is providing novel insights into cancer biology, particularly in pancreatic cancer. This technique allows for visualization at the cellular level and aids in bridging the gap between bench and bedside.

Education and Training: AI technology is being used to educate and train pathologists, improving their diagnostic skills and enhancing tumor classification accuracy.

Cascading Diagnostic Workflows: AI models, such as Paige Prostate, are reducing the need for additional studies and second opinions by assisting pathologists in making accurate diagnoses, streamlining the diagnostic process.

Personalized Medicine: AI's ability to predict biomarkers and prognosis in cancer is paving the way for personalized medicine approaches, where treatments can be tailored to individual patients based on their specific cancer characteristics.

Overall, AI in digital pathology is revolutionizing cancer diagnosis and research, leading to more accurate and efficient pathology workflows, personalized treatment strategies, and improved patient outcomes. As the field continues to advance, AI's impact on digital pathology is expected to grow, further transforming cancer care and management.


What topics within digital pathology and AI appear to be attracting the biggest share of the research?


Based on the 50 abstracts discussed, several topics within digital pathology and AI appear to be attracting the biggest share of research:

Cancer Diagnosis and Classification: The majority of research focuses on developing AI models for accurate cancer diagnosis and subtyping, spanning various cancer types, including prostate, colorectal, brain, bladder, and pancreatic cancers. AI's potential in improving diagnostic accuracy and reducing inter-observer variability is a significant area of interest.

Predictive Biomarkers: Many studies concentrate on using AI to predict key biomarkers in cancer, such as microsatellite instability (MSI) and other molecular alterations. Predicting biomarkers aids in treatment decision-making and personalized medicine approaches.

Immune Response Analysis: AI-driven analysis of the tumor microenvironment and immune response is a prominent area of research. Understanding the role of immune cells and their impact on cancer progression is crucial for developing immunotherapies and predicting patient outcomes.

Pathology Workflow Improvement: Research is focused on integrating AI into digital pathology workflows to enhance diagnostic efficiency and reduce turnaround time. Code-free AI platforms and automated image analysis tools are gaining attention for their potential to aid pathologists in routine practice.

Prognostic Models: Development and validation of AI-assisted prognostic models, incorporating clinical data and immune response information, are gaining traction in cancer research. These models can help in risk stratification and treatment planning.

Tissue Clearing and 3D Reconstruction: AI-driven 3D reconstruction of digitized slides, particularly in pancreatic cancer research, is an emerging area of interest. This technique offers new insights into cancer biology and provides a more comprehensive view of tumor characteristics.

Education and Training: Several studies explore the use of AI in education and training for pathologists. AI-based tools are being developed to enhance diagnostic skills and provide valuable learning resources.

Cascading Diagnostic Workflows: Research is being conducted on AI models that can assist pathologists in making accurate diagnoses, potentially reducing the need for additional studies and second opinions.

Comparative Analysis of AI Models: Studies are evaluating the performance of different AI algorithms, such as Swin Transformer, ResNet, and CNNs, to identify the most efficient and accurate models for specific pathology tasks.

Digital Pathology Datasets: Efforts are being made to create and curate large-scale digital pathology datasets for training and validating AI models, aiming to improve the generalizability and reproducibility of AI-driven pathology analysis.

Overall, cancer diagnosis, predictive biomarkers, and immune response analysis are the most prominent areas of research in digital pathology and AI. These areas hold significant potential to advance cancer care and research and are driving the majority of investigations in the field.


What factors are holding back the progress of digital pathology and AI?


Several factors are holding back the progress of digital pathology and AI:

Data Privacy and Security Concerns: Digital pathology generates vast amounts of patient data, including sensitive medical information. Ensuring patient privacy and data security is a paramount concern, which can lead to strict regulations and hinder data sharing for research purposes.

Limited Access to Quality Datasets: AI models require large and diverse datasets for robust training and validation. However, obtaining high-quality annotated pathology images with diverse patient populations can be challenging, limiting the performance and generalizability of AI algorithms.

Standardization and Interoperability: The lack of standardized protocols and formats for data acquisition, storage, and sharing hampers seamless integration and collaboration between different digital pathology systems and AI tools.

Technical Challenges in Image Analysis: While AI has shown promising results, accurate and reliable image analysis is still a challenge. Variability in staining, tissue preparation, and image quality can impact the performance of AI algorithms.

Regulatory and Legal Barriers: The adoption of AI in clinical practice requires regulatory approval, which can be a lengthy and complex process. Legal and liability issues related to AI-generated diagnoses and treatment recommendations also need to be addressed.

Integration with Existing Workflows: Implementing AI into routine pathology workflows may require significant changes in infrastructure, training, and acceptance from pathologists. Integration challenges can slow down the adoption of AI technologies.

Cost and Resource Constraints: The initial investment in acquiring AI systems and training data can be costly for healthcare institutions, particularly smaller or resource-constrained settings.

Ethical and Societal Implications: AI raises ethical considerations, including the potential impact on jobs and the responsibility for AI-generated diagnoses. Ensuring transparency and accountability in AI algorithms is crucial for gaining trust from both healthcare professionals and patients.

Validation and Reproducibility: Validating AI models across different datasets and pathology laboratories is essential to ensure reproducibility and reliability. Standardized benchmarking is needed to compare the performance of various AI algorithms.

Interpretability and Explainability: AI algorithms often operate as black boxes, making it challenging to understand the reasoning behind their decisions. In critical medical applications, explainable AI is crucial for gaining the trust of pathologists and patients.

Addressing these challenges will require collaboration between researchers, healthcare providers, regulatory bodies, and technology developers. 

Overcoming these barriers will pave the way for the widespread adoption and realization of the full potential of digital pathology and AI in transforming cancer diagnosis and personalized medicine.


Were any abstracts in this group notably pessimistic about the potential of AI in digital pathology?


From the collection of abstracts provided, there were no abstracts that were notably pessimistic about the potential of AI in digital pathology. Instead, the abstracts focused on the various advancements, applications, and benefits of AI in digital pathology, highlighting its potential in improving accuracy, efficiency, and diagnostic outcomes. The abstracts generally showcased the positive impact of AI in assisting pathologists, enhancing disease diagnosis, predicting biomarkers, and optimizing treatment strategies. However, it's worth noting that individual research studies or papers may present different perspectives, and the absence of pessimistic abstracts in this specific collection does not necessarily imply a lack of concerns or challenges in the field of digital pathology and AI.



Were any abstracts particularly optimistic or enthusiastic about the progress of digital pathology and AI?


Yes, several abstracts in the provided collection were particularly optimistic and enthusiastic about the progress of digital pathology and AI. These abstracts highlighted the potential transformative impact of AI in various aspects of pathology, ranging from improving diagnostic accuracy and efficiency to predicting biomarkers and advancing cancer research. Some of the abstracts expressed excitement about the use of AI as a tool to assist pathologists in detecting, grading, and quantifying diseases like prostate cancer and pancreatic cancer.

Furthermore, several abstracts emphasized the advancements in AI algorithms, such as Swin Transformer (Swin-T), and their ability to achieve state-of-the-art predictive performance with smaller training datasets, making AI more accessible and efficient for pathology applications. The potential of AI to revolutionize cancer diagnosis, reduce turnaround times, and cut costs in the diagnostic workflow was highlighted in several abstracts as well.

Overall, the tone of many abstracts was positive, optimistic, and enthusiastic about the potential of AI in digital pathology, acknowledging it as a promising tool to enhance clinical decision-making and revolutionize the field of pathology.


If I were a young medical school professor in pathology, and I want to work on digital pathology and AI, what advice would you give me, based on these abstracts?


As a young medical school professor interested in digital pathology and AI, the abstracts provide valuable insights and guidance for your research journey in this exciting field. Here is some advice based on the information gleaned from the abstracts:

Stay Updated with Advancements: Digital pathology and AI are rapidly evolving fields. It's crucial to stay updated with the latest advancements in both technology and research literature. Follow conferences, journals, and research publications to keep abreast of cutting-edge developments.

Focus on Diagnostic Accuracy: Many abstracts emphasize the potential of AI to improve diagnostic accuracy. Consider exploring projects that focus on developing AI algorithms or tools to assist pathologists in accurate disease diagnosis, especially in challenging cases.

Investigate Biomarker Predictions: Some abstracts highlight AI's role in predicting biomarkers in various diseases. Consider investigating the use of AI to predict important biomarkers, as this can have significant implications for treatment decisions and patient outcomes.

Work with Smaller Datasets: Pay attention to abstracts discussing AI algorithms like Swin Transformer (Swin-T) that demonstrate excellent performance with smaller training datasets. This could be advantageous if you have limited access to large datasets initially.

Collaborate with Computational Experts: Building AI models and leveraging digital pathology technologies may require computational expertise. Collaborate with experts in computer science, data science, or machine learning to maximize the potential of your research.

Focus on Clinical Impact: Many abstracts highlight the potential clinical impact of AI in reducing turnaround times, improving efficiency, and cutting costs. Prioritize research that can directly translate into real-world clinical applications to address practical challenges in pathology.

Validate AI Models: If you develop AI models, ensure thorough validation on external datasets to assess generalizability and reliability. Validation is essential to establish the credibility and clinical utility of your AI-based tools.

Address Ethical and Regulatory Concerns: As AI technologies become more integrated into medical practice, consider the ethical and regulatory implications of your work. Ensure compliance with data privacy and patient protection guidelines.

Collaborate and Network: Foster collaborations with colleagues and researchers in related fields, such as oncology, radiology, and computational biology. Building a network will broaden your research opportunities and enhance interdisciplinary knowledge.

Publish and Present: Disseminate your research findings through peer-reviewed publications and conference presentations. Sharing your work will contribute to the collective knowledge in the field and help establish your expertise.

Remember, the field of digital pathology and AI is still expanding, and there are numerous avenues for impactful research. Focus on problems that matter to clinical practice, and your contributions have the potential to transform patient care and advance the field of pathology. Good luck with your research endeavors!



Many large institutions, like UCLA, Mayo, Sloan Kettering, would have large amounts of slides, molecular information, clinical information, etc, in areas like patients with breast cancer.  

How could these archives be developed into novel AI-based patient services?


Developing the assets in breast cancer pathology into a single clinical service that benefits doctors and patients requires a strategic and interdisciplinary approach. Here are steps the company can take to achieve this goal:

Data Integration and Cohort Selection: Consolidate and integrate the data on 20,000 patients, including RNA expression, age, stage of disease, grade, and other clinical information. Identify specific subgroups or cohorts within the dataset that could benefit from targeted clinical services.

AI-Based Diagnostic Tool: Leverage the available data to develop an AI-based diagnostic tool for breast cancer. Train machine learning models using the RNA expression data and clinical variables to create a robust and accurate diagnostic tool that aids pathologists in diagnosing and staging breast cancer.

Biomarker Discovery: Utilize the RNA expression data to identify potential biomarkers associated with disease progression, treatment response, or prognosis. These biomarkers could further enhance diagnostic accuracy and inform personalized treatment decisions.

Predictive Modeling for Treatment Response: Develop predictive models using the clinical and RNA expression data to determine how patients are likely to respond to specific treatments. This information can help doctors tailor treatments to individual patients, optimizing outcomes and reducing side effects.

Digital Pathology Platform: Establish a digital pathology platform that allows pathologists to access and analyze patient data efficiently. The platform should incorporate AI algorithms for image analysis, enabling automated and standardized assessments of breast tissue samples.

Clinical Decision Support System: Integrate the diagnostic tool, biomarker predictions, and treatment response models into a user-friendly clinical decision support system. This system will assist pathologists and oncologists in making informed treatment recommendations based on comprehensive patient data.

Validation and Regulatory Compliance: Conduct thorough validation studies to assess the accuracy and reliability of the AI-based diagnostic tool and predictive models. Ensure compliance with regulatory requirements to obtain necessary approvals for clinical use.

Collaboration with Healthcare Providers: Collaborate with healthcare providers, including pathology labs and oncology centers, to pilot and implement the clinical service. Seek feedback from clinicians to refine and improve the service based on their practical needs.

Patient Education and Engagement: Develop educational materials and tools to help patients understand the benefits of the clinical service and the importance of breast cancer screening and early detection. Engage patients in the decision-making process to enhance their participation in their own healthcare.

Outcome Monitoring and Continuous Improvement: Continuously monitor the outcomes and performance of the clinical service. Collect feedback from doctors and patients to identify areas for improvement and further refine the service based on real-world experience.

By following these steps and building a comprehensive and user-friendly clinical service, the universities or spinout companies can provide valuable support to doctors in diagnosing and managing breast cancer or other cancers, while improving patient outcomes and overall healthcare efficiency