August 8, 2024
None
Office of Strategic Coordination (Common Fund)
Clinical imaging can play critical roles in prediction, diagnosis, treatment planning, and response evaluation across a wide range of health conditions. Artificial Intelligence (AI) approaches have catalyzed new opportunities in precision, or personalized, medicine, to integrate clinical imaging data with a variety of other health data, including data from electronic health records (EHRs), genomics, laboratory diagnostics, and other experimental imaging systems. The purpose of this RFI is to solicit public comments on the current challenges and opportunities in developing trustworthy, cost-effective, accessible, ethical, and sustainable precision medicine AI algorithms that integrate medical images with other patient-related data to support clinical decision-making.
Background
The NIH Common Fund (https://commonfund.nih.gov/) supports high-impact, cross-disciplinary programs designed to address challenging problems and seize new biomedical opportunities. The NIH seeks to gather input on the current state and potential of precision medicine algorithms that integrate multimodal, medical, image-centric data.
Recent advances in the computational field of image analysis, including machine learning (ML)- and deep learning (DL)-centered methods, have emerged as critical components of biomedical data analysis, leveraging the abundance and complexity of medical data. ML refers to computer algorithms (a set of rules and procedures) developed to analyze and make predictions from data that are fed into the system. DL refers to a type of ML that can glean hidden intricacies in data by utilizing more (and more sophisticated) processing layers. As an example, DL-enabled computer vision has been shown to capture information typically requiring a higher-fidelity modality for human interpretation. This is a promising source of information for precision medicine initiatives, as medical images contain dense, objective data that could be useful for phenotyping and personalized medicine.
In general, applications of AI in medicine have addressed narrowly defined tasks using a single data modality, such as a computed tomography (CT) scan or retinal imaging. However, health data is inherently multimodal, and a patients health status encompasses many domains (e.g., clinical, social, biological/genetic, environmental) that influence well-being in complex ways. Additionally, each of these domains is hierarchically organized, with data being abstracted from the macro level (for example, disease presence or absence) to the micro level (for example, biomarkers, proteomics, genomics). Furthermore, current healthcare systems add to this multimodal approach by generating data in multiple ways: radiology and pathology images are, for example, paired with natural language data from their respective reports, while disease states are also documented in natural language and tabular data in the electronic health records (EHRs). For the management of any disease, clinicians process data from multiple sources and modalities when diagnosing, making prognostic evaluations, and deciding on treatment plans. Thus, an avenue to transform disease prevention, detection, diagnosis, and treatment is the integration of rich multimodal data sets across the human phenome through the development of advanced AI/ML models.
It is anticipated that advanced AI/ML models will be able to:
By harnessing these advanced tools for clinical applications and disease prevention, the potential of multimodal data can be fully realized, leading to advancements in medical science and ultimately improving patient outcomes.
This RFI seeks comments from the community on the current challenges and opportunities in developing trustworthy, cost-effective, accessible, and sustainable algorithms integrating medical images with other patient-related data for precision medicine that support clinical decision-making. The NIH invites AI/ML and imaging researchers, healthcare providers, patients, industry representatives, patient advocates, funders, regulators, and representatives of populations that experience health disparities (as defined in section 464z-3(d)(1) of the Public Health Service Act, 42 U.S.C. 285t(d)(1) as health disparity populations) to comment on individualized, image-centric patient care through advanced ML techniques and data integration.
Information Requested
The NIH is seeking input on relevant aspects of multimodal AI systems, AI datasets, and AI algorithms that utilize diverse data types, and implementation techniques for these types of systems. There is no word limit for responses, and responses may include but are not limited to the following categories:
Imaging and related multimodal data integration approaches:
Developing AI based clinical decision support tools that leverage imaging and multimodal data integration:
Demonstrating clinical utility of multimodal algorithms for precision medicine:
Other comments, suggestions, or considerations relevant to this RFI.
Submitting a Response
All responses must be submitted electronically on the submission website: https://commonfund.nih.gov/ai-rfi. Responses must be received by 5:00 pm (ET) on September 23, 2024.
Responses to this RFI are voluntary. Responders are free to address any or all the categories listed above. Please do not include any information you do not wish to be made public. Proprietary, classified, confidential, or sensitive information must not be included in your response.
NIH staff will carefully review all responses and will not provide comments to any individual respondents submission. Any identifiers (e.g., names, institutions, e-mail addresses, etc.) will be removed when responses are compiled. The NIH will use the information submitted in response to this RFI anonymously at its discretion (e.g., on public websites, in reports, in summaries of the state of the science, in possible resultant solicitation(s), or in the development of future notices of funding opportunity).
This RFI is for information and planning purposes only and is not a solicitation for applications or an obligation on the part of NIH to provide support for any ideas identified in response to it. Please note that NIH will not pay for the preparation of any information submitted or for use of that information.
We look forward to receiving your response. Please share this request broadly with your colleagues and community.
Please direct all inquiries to:
Michele Ferrante, PhD
National Institute of Mental Health (NIMH)
Telephone: 301-793-2634
Email: michele.ferrante@nih.gov
Karlie Sharma, PhD
National Center for Advancing Translational Sciences (NCATS)
Telephone: 301-451-4965
Email: Karlie.sharma@nih.gov