Improve healthcare data capture at the source to build a learning health system

Studies estimate that only one in 10 recommendations made by major professional societies are supported by high-quality evidence. Medical care that is not evidence-based can result in unnecessary care that burdens public finances, harms patients, and damages trust in the medical profession. Clearly, we must do a better job of figuring out the right treatments, for the right patients, at the right time. To meet this challenge, it is essential to improve our ability to capture reusable data at the point of care that can be used to improve care, discover new treatments, and make healthcare more efficient. To achieve this vision, we will need to shift financial incentives to reward data generation, change how we deliver care using AI, and continue improving the technological standards powering healthcare.

The Challenge and Opportunity of health data

Many have hailed health data collected during everyday healthcare interactions as the solution to some of these challenges. Congress directed the U.S. Food and Drug Administration (FDA) to increase the use of real-world data (RWD) for making decisions about medical products. However, FDA’s own records show that in the most recent year for which data are available, only two out of over one hundred new drugs and biologics approved by FDA were approved based primarily on real-world data.

A major problem is that our current model in healthcare doesn’t allow us to generate reusable data at the point of care. This is even more frustrating because providers face a high burden of documentation, and patients report repetitive questions from providers and questionnaires. 

To expand a bit: while large amounts of data are generated at the point of care, these data lack the quality, standardization, and interoperability to enable downstream functions such as clinical trials, quality improvement, and other ways of generating more knowledge about how to improve outcomes. 

By better harnessing the power of data, including results of care,  we could finally build a learning healthcare system where outcomes drive continuous improvement and where healthcare value leads the way.  There are, however, countless barriers to such a transition. To achieve this vision,  we need to develop new strategies for the capture of high-quality data in clinical environments, while reducing the burden of data entry on patients and providers. 

Efforts to achieve this vision follow a few basic principles:

  1. Data should be entered only once– by the person or entity most qualified to do so – and be used many times.
  2. Data capture should be efficient, so as to minimize the burden on those entering the data, allowing them to focus their time on doing what actually matters, like providing patient care.
  3. Data generated at the point of care needs to be accessible for appropriate secondary uses (quality improvement, trials, registries), while respecting patient autonomy and obtaining informed consent where required. Data should not be stuck in any one system but should flow freely between systems, enabling linkages across different data sources.
  4. Data need to be used to provide real value to patients and physicians. This is​ achieved by developing data visualizations, automated data summaries, and decision support (e.g. care recommendations, trial matching) that allow data users to spend less time searching for data and more time on analysis, problem solving, and patient care– and help them see the value in entering data in the first place.

Barriers to capturing high-quality data at the point of care:

Plan of Action

Recommendation 1. Incentivize generation of reusable data at the point of care

Financial incentives are needed to drive the development of workflows and technology to capture high-quality data at the point of care. There are several payment programs already in existence that could provide a template for how these incentives could be structured.

For example, the Centers for Medicare and Medicaid Services (CMS) recently announced the Enhancing Oncology Model (EOM), a voluntary model for oncology providers caring for patients with common cancer types. As part of the EOM, providers are required to report certain data fields to CMS, including staging information and hormone receptor status for certain cancer types. These data fields are essential for clinical care, research, quality improvement, and ongoing care observation  involving cancer patients. Yet,  at present, these data are rarely recorded in a way that makes it easy to exchange and reuse this information. To reduce the burden of reporting this data, CMS has collaborated with the HHS Assistant Secretary for Technology Policy (ASTP) to develop and implement technological tools that can facilitate automated reporting of these data fields.

CMS also has a long-standing program that requires participation in evidence generation as a prerequisite for coverage, known as coverage with evidence development (CED). For example, hospitals that would like to provide Transcatheter Aortic Valve Replacement (TAVR) are required to participate in a registry that records data on these procedures.

To incentivize evidence generation as part of routine care, CMS should refine these programs and expand their use. This would involve strengthening collaborations across the federal government to develop technological tools for data capture, and increasing the number of payment models that require generation of data at the point of care. Ideally, these models should evolve to reward 1) high-quality chart preparation (assembly of structured data) 2) establishing diagnoses and development of a care plan, and 3) tracking outcomes.  These payment policies are powerful tools because they incentivize the generation of reusable infrastructure that can be deployed for many purposes.

Recommendation 2. Improve workflows to capture evidence at the point of care

With the right payment models, providers can be incentivized to capture reusable data at the point of care. However, providers are already reporting being crushed by the burden of documentation and patients are frequently filling out multiple questionnaires with the same information. To usher in the era of the learning health system (a system that includes continuous data collection to improve service delivery), without increasing the burden on providers and patients, we need to redesign how care is provided. Specifically, we must focus on approaches that integrate generation of reusable data into the provision of routine clinical care. 

While the advent of AI is an opportunity to do just that, current uses of AI have mainly focused on drafting documentation in free-text formats, essentially replacing human scribes. Instead, we need to figure out how we can use AI to improve the usability of the resulting data. While it is not feasible to capture all data in a structured format on all patients, a core set of data are needed to provide high-quality and safe care. At a minimum, those should be structured and part of a basic core data set across disease types and health maintenance scenarios.

In order to accomplish this, NIH and the Advanced Research Projects Agency for Health (ARPA-H) should fund learning laboratories that develop, pilot, and implement new approaches for data capture at the point of care. These centers would leverage advances in human-centered design and artificial intelligence (AI) to revolutionize care delivery models for different types of care settings, ranging from outpatient to acute care and intensive care settings. Ideally, these centers would be linked to existing federally funded research sites that could implement the new care and discovery processes in ongoing clinical investigations.

The federal government already spends billions of dollars on grants for clinical research- why not use some of that funding to make clinical research more efficient, and improve the experience of patients and physicians in the process?

Recommendation 3. Enable technology systems to improve data standardization and interoperability

Capturing high-quality data at the point of care is of limited utility if the data remains stuck within individual electronic health record (EHR) installations. Closed systems hinder innovation and prevent us from making the most of the amazing trove of health data. 

We must create a vibrant ecosystem where health data can travel seamlessly between different systems, while maintaining patient safety and privacy. This will enable an ecosystem of health data applications to flourish. HHS has recently made progress by agreeing to a unified approach to health data exchange, but several gaps remain. To address these we must

Conclusion

The treasure trove of health data generated during routine care has given us a huge opportunity to generate knowledge and improve health outcomes. These data should serve as a shared resource for clinical trials, registries, decision support, and outcome tracking to improve the quality of care. This is necessary for society to advance towards personalized medicine, where treatments are tailored to biology and patient preference. However, to make the most of these data, we must improve how we capture and exchange these data at the point of care.

Essential to this goal is evolving our current payment systems from rewarding documentation of complexity or time spent, to generation of data that supports learning and improvement. HHS should use its payment authorities to encourage data generation at the point of care and promote the tools that enable health data to flow seamlessly between systems, building on the success stories of existing programs like coverage with evidence development. To allow capture of this data without making the lives of providers and patients even more difficult, federal funding bodies need to invest in developing technologies and workflows that leverage AI to create usable data at the point of care. Finally, HHS must continue improving the standards that allow health data to travel seamlessly between systems. This is essential for creating a vibrant ecosystem of applications that leverage the benefits of AI to improve care.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

A Quantitative Imaging Infrastructure to Revolutionize AI-Enabled Precision Medicine

Medical imaging, a non-invasive method to detect and characterize disease, stands at a crossroads. With the explosive growth of artificial intelligence (AI), medical imaging offers extraordinary potential for precision medicine yet lacks adequate quality standards to safely and effectively fulfill the promise of AI. Now is the time to create a quantitative imaging (QI) infrastructure to drive the development of precise, data-driven solutions that enhance patient care, reduce costs, and unlock the full potential of AI in modern medicine.

Medical imaging plays a major role in healthcare delivery and is an essential tool in diagnosing numerous health issues and diseases (e.g., oncology, neurology, cardiology, hepatology, nephrology, pulmonary, and musculoskeletal). In 2023, there were more than 607 million imaging procedures in the United States and, per a 2021 study, $66 billion (8.9% of the U.S. healthcare budget) is spent on imaging.  

Despite the importance and widespread use of medical imaging like magnetic resonance imaging (MRI), X-ray, ultrasound, computed tomography (CT), it is rarely standardized or quantitative. This leads to unnecessary costs due to repeat scans to achieve adequate image quality, and unharmonized and uncalibrated imaging datasets, which are often unsuitable for AI/machine learning (ML) applications. In the nascent yet exponentially expanding world of AI in medical imaging, a well-defined standards and metrology framework is required to establish robust imaging datasets for true precision medicine, thereby improving patient outcomes and reducing spiraling healthcare costs.

Challenge and Opportunity 

The U.S. spends more on healthcare than any other high-income country yet performs worse on measures of health and healthcare. Research has demonstrated that medical imaging could help save money for the health system with every $1 spent on inpatient imaging resulting in approximately $3 total savings in healthcare delivered. However, to generate healthcare savings and improve outcomes, rigorous quality assurance (QA)/quality control(QC) standards are required for true QI and data integrity.   

Today, medical imaging suffers two shortcomings inhibiting AI: 

Both result in variability impacting assessments and reducing the generalizability of, and confidence in, imaging test results and compromise data quality required for AI applications.

The growing field of QI, however, provides accurate and precise (repeatable and reproducible) quantitative-image-based metrics that are consistent across different imaging devices and over time. This benefits patients (fewer scans, biopsies), doctors, researchers, insurers, and hospitals and enables safe, viable development and use of AI/ML tools.  

Quantitative imaging metrology and standards are required as a foundation for clinically relevant and useful QI. A change from “this might be a stage 3 tumor” to “this is a stage 3 tumor” will affect how oncologists can treat a patient. Quantitative imaging also has the potential to remove the need for an invasive biopsy and, in some cases, provide valuable and objective information before even the most expert radiologist’s qualitative assessment. This can mean the difference between taking a nonresponding patient off a toxic chemotherapeutic agent or recognizing a strong positive treatment response before a traditional assessment. 

Plan of Action 

The incoming administration should develop and fund a Quantitative Imaging Infrastructure to provide medical imaging with a foundation of rigorous QA/QC methodologies, metrology, and standards—all essential for AI applications.

Coordinated leadership is essential to achieve such standardization. Numerous medical, radiological, and standards organizations support and recognize the power of QI and the need for rigorous QA/QC and metrology standards (see FAQs). Currently, no single U.S. organization has the oversight capabilities, breadth, mandate, or funding to effectively implement and regulate QI or a standards and metrology framework.

As set forth below, earlier successful approaches to quality and standards in other realms offer inspiration and guidance for medical imaging and this proposal:

Recommendation 1. Create a Medical Metrology Center of Excellence for Quantitative Imaging. 

Establishing a QI infrastructure would transform all medical imaging modalities and clinical applications. Our recommendation is that an autonomous organization be formed, possibly appended to existing infrastructure, with the mandate and responsibility to develop and operationally support the implementation of quantitative QA/QC methodologies for medical imaging in the age of AI. Specifically this fully integrated QI Metrology Center of Excellence would need federal funding to:

Once implemented, the Center could focus on self-sustaining approaches such as testing and services provided for a fee to users.

Similar programs and efforts have resulted in funding (public and private) ranging from $90 million (e.g., Pathogen Genomics Centers of Excellence Network) to $150 million (e.g., Biology and Machine Learning – Broad Institute). Importantly, implementing a QI Center of Excellence would augment and complement federal funding currently being awarded through ARPA-H and the Cancer Moonshot, as neither have an overarching imaging framework for intercomparability between projects.  

While this list is by no means exhaustive, any organization would need input and buy-in from:

International organizations also have relevant programs, guidance, and insight, including:

Recommendation 2. Implement legislation and/or regulation providing incentives for standardizing all medical imaging. 

The variability of current standard-of-care medical imaging (whether acquired across different sites or over a period of time) creates different “appearances.” This variability can result in different diagnoses or treatment response measurements, even though the underlying pathology for a given patient is unchanged. Real-world examples abound, such as one study that found 10 MRI studies over three weeks resulted in 10 different reports. This heterogeneity of imaging data can lead to a variable assessment by a radiologist (inter-reader variability), AI interpretation (“garbage-in-garbage-out”), or treatment recommendations from clinicians. Efforts are underway to develop “vendor-neutral sequences” for MRI and other methods (such as quantitative ground truth references, metrological standards, etc.) to improve data quality and ensure intercomparable results across vendors and over time. 

To do so, however, requires coordination by all original equipment manufacturers (OEMs) or legislation to incentivize standards. The 1992 Mammography Quality Standards Act (MQSA) provides an analogous roadmap. MQSA’s passage implemented rigorous standards for mammography, and similar legislation focused on quality assurance of quantitative imaging, reducing or eliminating machine bias, and improved standards would reduce the need for repeat scans and improve datasets. 

In addition, regulatory initiatives could also advance quantitative imaging. For example, in 2022, the Food and Drug Administration (FDA) issued Technical Performance Assessment of Quantitative Imaging in Radiological Device Premarket Submissions, recognizing the importance of ground truth references with respect to quantitative imaging algorithms. A mandate requiring the use of ground truth reference standards would change standard practice and be a significant step to improving quantitative imaging algorithms.

Recommendation 3. Ensure a funded QA component for federally funded research using medical imaging. 

All federal medical research grant or contract awards should contain QA funds and require rigorous QA methodologies. The quality system aspects of such grants would fit the scope of the project; for example, a multiyear, multisite project would have a different scope than single-site, short-term work.

NIH spends the majority of its $48 billion budget on medical research. Projects include multiyear, multisite studies with imaging components. While NIH does have guidelines on research and grant funding (e.g., Guidance: Rigor and Reproducibility in Grant Applications), this guidance falls short in multisite, multiyear projects where clinical scanning is a component of the study.  

To the extent NIH-funded programs fail to include ground truth references where clinical imaging is used, the resulting data cannot be accurately compared over time or across sites. Lack of standardization and failure to require rigorous and reproducible methods compromises the long-term use and applicability of the funded research. 

By contrast, implementation of rigorous standards regarding QA/QC, standardization, etc. improve research in terms of reproducibility, repeatability, and ultimate outcomes. Further, confidence in imaging datasets enables the use of existing and qualified research in future NIH-funded work and/or imaging dataset repositories that are being leveraged for AI research and development, such as the Medical Imaging and Resource Center (MIDRC). (See also: Open Access Medical Imaging Repositories.)  

Recommendation 4. Implement a Clinical Standardization Program (CSP) for quantitative imaging. 

While not focused on medical imaging, the CDC’s CSPs have been incredibly successful and “improve the accuracy and reliability of laboratory tests for key chronic biomarkers, such as those for diabetes, cancer, and kidney, bone, heart, and thyroid disease.” By way of example, the CSP for Lipids Standardization has “resulted in an estimated benefit of $338M at a cost of $1.7M.” Given the breadth of use of medical imaging, implementing such a program for QI would have even greater benefits.  

Although many people think of the images derived from clinical imaging scans as “pictures,” the pixel and voxel numbers that make up those images contain meaningful biological information. The objective biological information that is extracted by QI is conceptually the same as the biological information that is extracted from tissue or fluids by laboratory assay techniques. Thus, quantitative imaging biomarkers can be understood to be “imaging assays.” 

The QA/QC standards that have been developed for laboratory assays can and should be adapted to quantitative imaging.  (See also regulations, history, and standards of the Clinical Laboratory Improvement Amendment (CLIA) ensuring quality laboratory testing.)

Recommendation 5. Implement an accreditation program and reimbursement code for quantitative imaging starting with qMRI.

The American College of Radiology currently provides basic accreditation for clinical imaging scanners and concomitant QA for MRI. These requirements, however, have been in place for nearly two decades and do not address many newer quantitative aspects (e.g., relaxometry and ADC) nor account for the impact of image variability in effective AI use. Several new Current Procedural Terminology (CPT) codes have been recently adopted focused on quantitative imaging. An expansion of reimbursement codes for quantitative imaging could drive more widespread clinical adoption.

QI is analogous to the quantitative blood, serum and tissue assays done in clinical laboratories, subject to CLIA, one of the most impactful programs for improving the accuracy and reliability of laboratory assays. This CMS-administered mandatory accreditation program promulgates quality standards for all laboratory testing to ensure the accuracy, reliability, and timeliness of patient test results, regardless of where the test was performed. 

Conclusion

These five proposals provide a range of actionable opportunities to modernize the approach to medical imaging to fit the age of AI, data integrity, and precision patient health. A comprehensive, metrology-based quantitative imaging infrastructure will transform medical imaging through:

With robust metrological underpinnings and a funded infrastructure, the medical community will have confidence in the QI data, unlocking powerful health insights only imaginable until now.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.

Frequently Asked Questions
Is scanner variability and lack of standardization really an issue?

Yes. Using MRI as an example, numerous articles, papers, and publications acknowledge qMRI variability in scanner output can vary between manufacturers, over time, and after software or hardware maintenance or upgrades.

What is in-vivo imaging metrology, and why is it the future?

With in-vivo metrology, measurements are performed on the “body of living subjects (human or animal) without taking the sample out of the living subject (biopsy).” True in-vivo metrology will enable the diagnosis or understanding of tissue state before a radiologist’s visual inspection. Such measurement capabilities are objective, in contrast to the subjective, qualitative interpretation by a human observer. In-vivo metrology will enhance and support the practice of radiology in addition to reducing unnecessary procedures and associated costs.

What are the essential aspects of QI?

Current digital imaging modalities provide the ability to measure a variety of biological and physical quantities with accuracy and reliability, e.g., tissue characterization, physical dimensions, temperature, body mass components, etc. However, consensus standards and corresponding certification or accreditation programs are essential to bring the benefits of these objective QI parameters to patient care. The CSP follows this paradigm as does the earlier CLIA, both of which have been instrumental in improving the accuracy and consistency of laboratory assays. This proposal aims to bring the same rigor to immediately improve the quality, safety and effectiveness of medical imaging in clinical care and to advance the input data needed to create, as well as safely and responsibly use, robust imaging AI tools for the benefit of all patients.

What are “phantoms,” or ground truth references, and why are they important?

Phantoms are specialized test objects used as ground truth references for quantitative imaging and analysis. NIST plays a central role in measuring and testing solutions for phantoms. Phantoms are used in ultrasound, CT, MRI, and other imaging modalities for routine QA/QC and machine testing. Phantoms are key to harmonizing and standardizing data and improve data quality needed for AI applications.

What do you mean by “precision medicine”? Don’t we already have it?

Precision medicine is a popular term with many definitions/approaches applying to genetics, oncology, pharmacogenetics, oncology, etc. (See, e.g., NCI, FDA, NIH, National Human Genome Research Institute.) Generally, precision (or personalized) medicine focuses on the idea that treatment can be individualized (rather than generalized). While there have been exciting advances in personalized medicine (such as gene testing), the variability of medical imaging is a major limitation in realizing the full potential of precision medicine. Recognizing that medical imaging is a fundamental measurement tool from diagnosis through measurement of treatment response and toxicity assessment, this proposal aims to transition medical imaging practices to quantitative imaging to enable the realization of precision medicine and timely personalized approaches to patient care.

How does standardized imaging data and QI help radiology and support healthcare practitioners?

Radiologists need accurate and reliable data to make informed decisions. Improving standardization and advancing QI metrology will support radiologists by improving data quality. To the extent radiologists are relying on AI platforms, data quality is even more essential when it is used to drive AI applications, as the outputs of AI models rely on sound acquisition methods and accurate quantitative datasets.


Standardized data also helps patients by reducing the need for repeat scans, which saves time, money, and unnecessary radiation (for ionizing methods).

Does quantitative imaging improve accessibility to healthcare?

Yes! Using MRI as an example, qMRI can advance and support efforts to make MRI more accessible. Historically, MRI systems cost millions of dollars and are located in high-resource hospital settings. Numerous healthcare and policy providers are making efforts to create “accessible” MRI systems, which include portable systems at lower field strengths and to address organ-specific diseases. New low-field systems can reach patient populations historically absent from high-resource hospital settings. However, robust and reliable quantitative data are needed to ensure data collected in rural, nonhospital settings, or in Low and Middle Income Countries, can be objectively compared to data from high-resource hospital settings.


Further, accessibility can be limited by a lack of local expertise. AI could help fill the gap.
However, a QI infrastructure is needed for safe and responsible use of AI tools, ensuring adequate quality of the input imaging data.

What is a specific example of the benefits of standardization?

The I-SPY 2 Clinical Breast Trials provide a prime example of the need for rigorous QA and scanner standardization. The I-SPY 2 trial is a novel approach to breast cancer treatment that closely monitors treatment response to neoadjuvant therapy. If there is no immediate/early response, the patient is switched to a different drug. MR imaging is acquired at various points during the treatment to determine the initial tumor size and functional characteristics and then to measure any tumor shrinkage/response over the course of treatment. One quantitative MRI tumor characteristic that has shown promise for evaluation of treatment response and is being evaluated in the trial is ADC, a measure of tissue water mobility which is calculated from diffusion-weighted imaging. It is essential for the trial that MR results can be compared over time as well as across sites. To truly know whether a patient is responding, the radiologist must have confidence that any change in the MR reading or measurement is due to a physiological change and not due to a scanner change such as drift, gradient failure, or software upgrade.


For the I-SPY 2 trial, breast MRI phantoms and a standardized imaging protocol are used to test and harmonize scanner performance and evaluate measurement bias over time and across sites. This approach then provides clear data/information on image quality and quantitative measurement (e.g., ADC) for both the trial (comparing data from all sites is possible) as well as for the individual imaging sites.

What are the benefits of a metrological and standards-based framework for medical imaging in the age of AI?

Nonstandardized imaging results in variation that requires orders of magnitude more data to train an algorithm. More importantly, without reliable and standardized datasets, AI algorithms drift, resulting in degradation of both protocols and performance. Creating and supporting a standards-based framework for medical imaging will mitigate these issues as well as lead to:



  • Integrated and coordinated system for establishing QIBs, screening, and treatment planning.

  • Cost savings: Standardizing data and implementing quantitative results in superior datasets for clinical use or as part of large datasets for AI applications. Clinical Standardization Programs have focused on standardizing tests and have been shown to save “millions in health care costs.”

  • Better health outcomes: Standardization reduces reader error and enables new AI applications to support current radiology practices.

  • Support for radiologists’ diagnoses.

  • Fewer incorrect diagnoses (false positives and false negatives).

  • Elimination of millions of unnecessary invasive biopsies.

  • Fewer repeat scans.

  • Robust and reliable datasets for AI applications (e.g., preventing model collapse).


It benefits federal organizations such as the National Institutes of Health, Centers for Medicare and Medicaid Services, and Veterans Affairs as well as the private and nonprofit sectors (insurers, hospital systems, pharmaceutical, imaging software, and AI companies). The ultimate beneficiary, however, is the patient, who will receive an objective, reliable quantitative measure of their health—relevant for a point-in-time assessment as well as longitudinal follow-up.

Who is likely to push back on this proposal, and how can that hurdle be overcome?

Possible pushback from such a program may come from: (1) radiologists who are unfamiliar with the power of quantitative imaging for precision health and/or the importance and incredible benefits of clean datasets for AI applications; or (2) manufacturers (OEMs) who aim to improve output through differentiation and are focused on customers who are more interested in their qualitative practice.


Radiology practices: Radiology practices’ main objective is to provide the most accurate diagnosis possible in the least amount of time, as cost-effectively as possible. Standardization and calibration are generally perceived as requiring additional time and increased costs; however, these perceptions are often not true, and the variability in imaging introduces more time consumption and challenges. The existing standard of care relies on qualitative assessments of medical images.


While excellent for understanding a patient’s health at a single point in time (though even in these cases subtle abnormalities can be missed), longitudinal monitoring is impossible without robust metrological standards for reproducibility and quantitative assessment of tissue health. While a move from qualitative to quantitative imaging may require additional education, understanding, and time, such an infrastructure will provide radiologists with improved capabilities and an opportunity to supplement and augment the existing standard of care.


Further, AI is undeniably being incorporated into numerous radiology applications, which will require accurate and reliable datasets. As such, it will be important to work with radiology practices to demonstrate a move to standardization will, ultimately, reduce time and increase the ability to accurately diagnose patients.


OEMs: Imaging device manufacturers work diligently to improve their outputs. To the extent differentiation is seen as a business advantage, a move toward vendor-neutral and scanner-agnostic metrics may initially be met with resistance. However, all OEMs are investing resources to improve AI applications and patient health. All benefit from input data that is standard and robust and provides enough transparency to ensure FAIR data principles (findability, accessibility, interoperability, and reusability).


OEMs have plenty of areas for differentiation including improving the patient experience and shortening scan times. We believe OEMs, as part of their move to embrace AI, will find clear metrology and standards-based framework a positive for their own business and the field as a whole.

What is the first step to get this proposal off the ground? Could there be a pilot project?

The first step is to convene a meeting of leaders in the field within three months to establish priorities and timelines for successful implementation and adoption of a Center of Excellence. Any Center must be well-funded with experienced leadership and will need the support and collaboration across the relevant agencies and organizations.


There are numerous potential pilots. The key is to identify an actionable study where results could be achieved within a reasonable time. For example, a pilot study to demonstrate the importance of quantitative MRI and sound datasets for AI could be implemented at the Veterans Administration hospital system. This study could focus on quantifying benefits from standardization and implementation of quantitative diffusion MRI, an “imaging biopsy” modality as well as mirror advances and knowledge identified in the existing I-SPY 2 clinical breast trials.

Why have similar efforts failed in the past? How will your proposal avoid those pitfalls?

The timing is right for three reasons: (1) quantitative imaging is doable; (2) AI is upon us; and (3) there is a desire and need to reduce healthcare costs and improve patient outcomes.


There is widespread agreement that QI methodologies have enormous potential benefits, and many government agencies and industry organizations have acknowledged this. Unfortunately, there has been no unifying entity with sufficient resources and professional leadership to coordinate and focus these efforts. Many organizations have been organized and run by volunteers. Finally, some previously funded efforts to support quantitative imaging (e.g., QIN and QIBA) have recently lost dedicated funding.


With rapid advances in technology, including the promise of AI, there is new and shared motivation across communities to revise our approach to data generation and collection at-large—focused on standardization, precision, and transparency. By leveraging the existing widespread support, along with dedicated resources for implementation and enforcement, this proposal will drive the necessary change.

Is there an effort or need for an international component?

Yes. Human health has no geographical boundaries, so a global approach to quantitative imaging would benefit all. QI is being studied, implemented, and adopted globally.


However, as is the case in the U.S., while standards have been proposed, there is no international body to govern the implementation, coordination, and maturation of this process. The initiatives put forth here could provide a roadmap for global collaboration (ever-more important with AI) and standards that would speed up development and implementation both in the U.S. and abroad.

Which organizations currently recognize the urgent need for QI standards?

Some of the organizations that recognize the urgent need for QI standards are National Institute of Standards and Technology (quantitative MR project); National Cancer Institute’s Quantitative Imaging Network; Radiological Society of North America (through its Quantitative Imaging Committee); Quantitative Imaging Biomarker Alliance; International Society of Magnetic Resonance in Medicine; Object Management Group; American Association of Physicists in Medicine; National Institutes of Health; Food and Drug Administration (“guidance” – ground truth references for quantitative imaging algorithms).