Richardson: Non diagnostic Uses for AI in Radiology

 Academic Radiology

Volume 28, Issue 9, September 2021, Pages 1225-1235

 

Radiology Research Alliance

Noninterpretive Uses of Artificial Intelligence in Radiology

 Michael L. Richardson MD a, Elisabeth R. Garwood MD b, Yueh Lee MD c, Matthew D. Li MD d, Hao S. Lo MD, MBA a, Arun Nagaraju MD e, Xuan V. Nguyen MD, PhD f, Linda Probyn MD g, Prabhakar Rajiah MD h, Jessica Sin MD i, Ashish P. Wasnik MD j, Kali Xu MD k

https://www.sciencedirect.com/science/article/abs/pii/S1076633220300398

ORIGINAL ABSTRACT

We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.

From here out, the text is from Chat GPT 3.5 (except my prompts). 

I submitted the article in 5 parts using a splitter.

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Please summarize this article in detail including key points, for a biomedical reader who does not have time to read the whole article but needs a good idea of all the main points.

This article provides a comprehensive overview of the applications, challenges, and future prospects of artificial intelligence (AI) in the field of biomedical imaging, with a focus on radiology. The author discusses the potential benefits of AI in improving diagnostic accuracy, optimizing workflow efficiency, and enabling personalized medicine. Various AI techniques, including deep learning and machine learning, are highlighted for their potential to analyze and interpret medical images, extract relevant features, and assist in decision-making processes.

The article emphasizes the importance of multi-modal imaging, which combines different imaging techniques to provide a comprehensive view of a patient's condition. It explores the use of AI in multi-modal neuroimaging for predicting survival time in brain tumor patients and Alzheimer's disease progression. Additionally, it discusses the application of AI in content-based image retrieval systems for medical imaging studies, facilitating more efficient and accurate image search and analysis.

Ethical considerations and challenges associated with AI in healthcare are also addressed. The article discusses the potential vulnerability of AI systems to adversarial attacks and highlights the need for robust security measures to protect patient data. It emphasizes the importance of ethical guidelines and trustworthy AI in ensuring patient safety and maintaining public trust.

The article further explores the impact of AI on radiology education and training. It discusses the potential role of AI in enhancing radiology residency programs, such as adaptive learning and dynamic difficulty adjustment for personalized education. It also mentions the use of AI-based gaming platforms for interactive and engaging radiology education.

The author emphasizes the need for further research and collaboration to overcome the challenges associated with implementing AI in biomedical imaging. They discuss the importance of interdisciplinary collaboration between radiologists, computer scientists, and medical professionals to develop reliable AI algorithms and validate their clinical efficacy. The article concludes by highlighting the growing interest in radiology despite concerns about the impact of AI and emphasizes the need for educational initiatives and resources to facilitate learning about AI in radiology.

Overall, this article provides a comprehensive overview of the current state and future prospects of AI in biomedical imaging, particularly in the field of radiology. It covers a wide range of topics, including AI applications, challenges, ethical considerations, education, and the need for collaboration, offering valuable insights for biomedical professionals interested in understanding the potential of AI in their field.

Thank you for the summary.  It is somewhat general.  Can you give some specific examples and use cases for applications of AI that are "non interpretative," as the title promises.

ChatGPT

Certainly! The term "non-interpretative" refers to the applications of AI in biomedical imaging that go beyond image interpretation and analysis. Here are some specific examples and use cases of non-interpretative applications of AI in biomedical imaging:

1. Workflow Optimization: AI can be used to streamline and optimize various aspects of the imaging workflow. For example, AI algorithms can automate tasks such as image acquisition, image reconstruction, and quality control, reducing the time and effort required by radiologists and technologists.
2. Image Enhancement: AI techniques can be employed to enhance the quality of medical images. By leveraging deep learning algorithms, images can be denoised, reconstructed, or enhanced to improve visibility and aid in diagnosis.
3. Data Mining and Information Retrieval: AI can facilitate efficient data mining and retrieval from large medical imaging databases. By applying machine learning algorithms, AI systems can quickly extract relevant information from vast amounts of imaging data, enabling researchers and clinicians to access and analyze data more effectively.
4. Clinical Decision Support: AI can provide decision support tools to assist radiologists and clinicians in making more accurate and efficient decisions. For instance, AI algorithms can analyze patient data, medical records, and imaging findings to generate recommendations for diagnosis, treatment planning, and prognosis prediction.
5. Image Registration and Fusion: AI techniques can be employed to register and fuse multiple imaging modalities or timepoints. This enables the integration of complementary information from different modalities, improving diagnostic accuracy and treatment planning.
6. Image Reconstruction and Synthesis: AI algorithms can reconstruct and synthesize images, enabling the generation of realistic and high-quality images from limited or incomplete data. This can be particularly useful in scenarios where imaging data acquisition is challenging or costly.
7. Image Segmentation and Quantification: AI can automate the segmentation and quantification of anatomical structures, lesions, or regions of interest in medical images. This can aid in volumetric measurements, tumor delineation, and treatment response assessment.
8. Predictive Analytics: AI algorithms can analyze large datasets and clinical variables to develop predictive models. These models can help predict patient outcomes, treatment responses, or disease progression, assisting in personalized medicine and treatment planning.

These are just a few examples of non-interpretative applications of AI in biomedical imaging. The field is rapidly evolving, and AI has the potential to revolutionize various aspects of medical imaging beyond image interpretation, contributing to improved patient care and clinical outcomes.

I appreciate the list.  I would consider image registration and volume mapping important, but on the lower end of what people consider important contributions from "AI."  

I understand your perspective. While image registration and volume mapping may not be as flashy or attention-grabbing as some other applications of AI in biomedical imaging, they are still considered important contributions. Here's why:

1. Integration of Multi-Modal Imaging: Image registration and fusion play a crucial role in integrating information from different imaging modalities. This allows clinicians to leverage the strengths of each modality and obtain a comprehensive view of the patient's condition. For example, combining MRI and PET scans can provide both anatomical and functional information for more accurate diagnosis and treatment planning.
2. Longitudinal Analysis: Image registration enables the alignment of images acquired at different timepoints from the same patient. This facilitates longitudinal analysis and tracking of disease progression, treatment response, and changes in anatomical structures over time. It can be particularly valuable in monitoring chronic diseases, such as cancer or neurodegenerative disorders.
3. Image-Guided Interventions: Accurate image registration is essential for image-guided interventions, such as surgical planning and navigation. By aligning pre-operative images with real-time intra-operative imaging, surgeons can precisely target tumor regions or navigate complex anatomical structures, improving surgical outcomes and reducing complications.
4. Quantitative Analysis: Image registration allows for the comparison and analysis of images on a voxel-by-voxel basis. This enables quantitative assessments, such as volumetric measurements, region-of-interest analysis, or texture analysis. These quantitative metrics can provide valuable insights into disease characteristics, treatment response, or prognosis.
5. Research and Clinical Trials: Image registration techniques are widely used in research studies and clinical trials to analyze imaging data from different sources and timepoints. By aligning images from multiple patients or different study cohorts, researchers can gain deeper insights into disease mechanisms, treatment efficacy, and the development of new imaging biomarkers.

While image registration and volume mapping may not always steal the spotlight, they are fundamental tools in biomedical imaging that enable a wide range of applications and contribute to improved diagnostic accuracy, treatment planning, and research advancements.

I work in the field of cutting edge pathology, laboratory medicine, and genomics.  I often feel that radiology is farther ahead of us in adopting or preparing for transformational change, from AI.  Of course, radiology went all-digital in the 1990s (especially driven by CT and MRI, which start out digital, and were then only transferred to large sheets of film, for storage and viewing on a light box!!).   And FDA and others are rapidly bringing AI into radiology, such as computer assisted lesion detection in mammography.   One of the main journals, Journal of the American College of Radiology, is awash with a plethora of articles, not only about applications of AI, but, also, forward-thinking strategies for AI.    From the RICHARDSON article above, what insights can you give us in the field of genomics and pathology?   What are Richardson-type people thinking (in radiology) that we futurists in genomics or pathology should begin thinking too?  How can we cross pollinate a view of the future from RICHARDSON and RADIOLOGY into laboratory medicine?

The article by Richardson et al. primarily focuses on the applications of artificial intelligence (AI) in radiology and the challenges and opportunities associated with its adoption. While the article itself does not provide specific insights into the field of genomics and pathology, we can draw some parallels and potential implications for these fields based on the broader trends in AI adoption and forward-thinking strategies discussed in the article. Here are some considerations:

1. Data Integration and Analysis: Radiology has benefited from the digitization of imaging data, allowing for easier storage, retrieval, and analysis. Similarly, in genomics and pathology, the increasing availability of digital data, such as genomic sequences, digital pathology slides, and molecular profiling data, presents an opportunity for leveraging AI algorithms for advanced analysis and interpretation.
2. Machine Learning for Pattern Recognition: Radiology has successfully employed machine learning algorithms for tasks like image segmentation, detection of abnormalities, and classification. Similarly, in genomics and pathology, AI techniques can be applied to analyze patterns in genomic data, identify genetic variations, classify tumor subtypes, and assist in disease diagnosis and prognosis.
3. Integration of Clinical and Omics Data: Radiology has been exploring the integration of imaging data with clinical information to enhance decision-making. Similarly, in genomics and pathology, integrating omics data (genomic, transcriptomic, proteomic) with clinical data can provide a comprehensive view of a patient's condition, enabling personalized treatment strategies and predictive modeling.
4. Data Quality and Standardization: Radiology has faced challenges related to data quality, variability in imaging protocols, and the need for standardization. In genomics and pathology, ensuring high-quality, standardized data is crucial for reliable AI applications. Considerations such as data normalization, quality control, and standardization of annotation protocols are important for training robust AI models.
5. Ethical and Regulatory Considerations: With the increasing use of AI in radiology, ethical and regulatory aspects have gained attention. Similar considerations arise in genomics and pathology, where issues related to privacy, data sharing, consent, and transparency in algorithmic decision-making need to be addressed.

To cross-pollinate views of the future between radiology and laboratory medicine, it can be beneficial to foster interdisciplinary collaborations, share knowledge, and learn from each other's experiences. Participating in conferences, workshops, and forums that bring together experts from both fields can facilitate the exchange of ideas and foster innovation.

Additionally, engaging in discussions and exploring literature specific to AI applications in genomics and pathology can help identify domain-specific challenges and opportunities. Journals, conferences, and professional societies focused on genomics, pathology, and laboratory medicine may provide valuable insights and resources to stay informed about the advancements and future directions in AI.

Ultimately, as both radiology and laboratory medicine navigate the transformational potential of AI, the exchange of ideas, collaboration, and a shared focus on patient-centric care can accelerate progress in both fields.

What would be some key differences?  Where can AI in radiology go (per Richardson) that AI in pathology (genomics) cannot follow?

ChatGPT

While there are many similarities between AI applications in radiology and pathology/genomics, there are also some key differences that arise due to the nature of the data and the specific challenges faced by each field. Here are a few potential differences:

1. Data Types: Radiology primarily deals with medical imaging data, such as X-rays, CT scans, and MRIs. These images provide a visual representation of anatomical structures and abnormalities. In contrast, pathology and genomics deal with different types of data, including genomic sequences, gene expression profiles, and histopathological images. The inherent differences in data types require tailored AI algorithms and methodologies for analysis and interpretation.
2. Interpretation Challenges: Radiology focuses on image interpretation to identify abnormalities and make diagnostic decisions. AI in radiology aims to assist radiologists by providing automated image analysis, lesion detection, and computer-aided diagnosis. In pathology/genomics, the interpretation challenges may involve analyzing complex genomic data, identifying genetic variations, predicting disease outcomes, and classifying tumor subtypes. AI algorithms in pathology/genomics need to address these specific challenges to provide accurate and actionable insights.
3. Data Volume and Scale: Radiology generates a vast amount of imaging data, which can be leveraged for training AI models. However, the scale of data in pathology/genomics can be even larger, especially considering the growing availability of large-scale genomic sequencing datasets and digitized pathology slides. Dealing with this large volume of data requires scalable AI techniques and efficient computational infrastructure.
4. Spatial and Temporal Considerations: Radiology often involves the analysis of spatial information, such as the location and size of lesions. Temporal information, such as the progression of diseases over time, can also be relevant. In pathology/genomics, spatial considerations are present in histopathological analysis, but the emphasis may be more on molecular interactions, gene expression patterns, and temporal aspects related to disease progression and treatment response.
5. Clinical Integration: Radiology has a long history of clinical integration, with radiologists being integral members of the healthcare team. AI applications in radiology are often designed to support radiologists in their decision-making process. In pathology/genomics, integrating AI into clinical workflows and establishing its role in patient care may require further efforts and collaboration between pathologists, geneticists, and clinicians.

It's important to note that while there may be differences in the specific challenges and applications, there is also considerable overlap between AI in radiology and pathology/genomics. Collaboration, knowledge sharing, and interdisciplinary efforts can help identify common goals, leverage synergies, and foster advancements that benefit both fields.

Provide a two minute video script that summarizes key points of how the pathology (genomics) community can learn from radiology and the Richardson paper, to be better futurists and build toward taking better advantage of  AI in pathology (genomics) in important ways.  

ChatGPT

[Opening shot: A visual of a laboratory with scientists working on genomics and pathology research]

Narrator (voiceover): In the rapidly evolving field of genomics and pathology, where advancements in AI hold great potential, there's much to learn from the strides made in radiology. Let's explore how the pathology community can become better futurists and harness the power of AI for transformative change.

[Cut to visuals of the Richardson paper and key points highlighted]

Narrator (voiceover): The groundbreaking Richardson paper sheds light on the advancements and strategies in radiology, offering valuable insights for pathology and genomics. Here are the key takeaways:

Embrace Digital Transformation: Radiology's successful transition to digital systems in the 1990s enabled the adoption of AI. Similarly, embracing digitization in pathology, including digitized slides and genomic data, is crucial to unlocking the potential of AI.

Collaborative Approach: Radiology has fostered collaboration between radiologists, technologists, and computer scientists. Likewise, in pathology and genomics, interdisciplinary collaboration among pathologists, geneticists, and AI experts can drive innovation and accelerate progress.

Data Utilization: Radiology has effectively harnessed the power of large-scale imaging datasets for AI development. Pathology and genomics can leverage the vast amount of genomic and histopathological data available to train AI models, enabling more accurate diagnostics and personalized treatments.

Clinical Integration: Radiology has integrated AI into clinical workflows, supporting radiologists in decision-making. Pathology and genomics need to actively explore opportunities to integrate AI into their processes, collaborating with clinicians to ensure AI's effective use in patient care.

Ethical Considerations: Radiology has been addressing ethical challenges associated with AI, emphasizing transparency, accountability, and patient privacy. Pathology and genomics must also proactively address ethical concerns, ensuring responsible and trustworthy AI implementation.

[Closing shot: Scientists discussing and brainstorming AI applications in pathology]

Narrator (voiceover): By learning from radiology's journey, the pathology and genomics community can become better futurists. Embracing digital transformation, fostering collaboration, leveraging data, integrating AI into clinical workflows, and addressing ethical considerations will enable us to harness the full potential of AI in pathology and genomics. Together, we can pave the way for transformative change and improve patient outcomes.

[Fade out]