NASDAQ: ONMD

OneMedNet
provides innovative solutions that unlock the significant value contained within clinical image archives. With a growing network of 2,130
healthcare sites, OneMedNet has the immediate ability to quickly search and extensively curate multi-layer data from a federated group
of healthcare facilities. The term “healthcare sites” refers specifically to the hospitals, integrated delivery networks
and imaging centers that provide imaging to OneMedNet, which represent the core source of our data. At present, OneMedNet has access
to more than 2,130 sites who provide regulatory grade data to us.

Initially,
it was all about solving the diverse access needs of patient care providers. This focus systematically evolved to addressing the rapidly
growing needs of image analysis and researchers, clinicians, regulators, scientists and more. The federated network allows OneMedNet
to access the following data to provide to research as RWD.

Real
World Data is any data that is collected in the context of the routine delivery of care, in contrast to data collected within a clinical
trial where study design controls variability in ways that are not representative of real world care and outcomes.

A
key component driving its mission is that OneMedNet believes we have a unique opportunity to affect a material positive impact on the
lives of tens of millions of people while improving our customers’ business productivity. First and foremost, OneMedNet’s
iRWDTM offering plays a significant role in enabling life science companies to bring safer and more effective patient care to market
sooner. Using our highly curated de-identified clinical data in our iRWDTM offering in life science product development, validation,
and regulatory approval processes, life science companies contribute to patient care advancements in more meaningful ways. Moreover,
life sciences improve life science companies’ product development and validation processes, which benefits all parties.

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Significant
documentation exists that shows that Real World Data can provide expanded insights across broader and more representative patient populations.
For this reason, the Food and Drug Administration (“FDA”) has instituted Real World Data guidelines for regulatory approvals.
Utilization of highly reliable and quality Real World Data that strictly adheres to all of the very specific data stratification requirements
can supplement or supplant clinical trials.

OneMedNet
covers the complete value chain in imaging Real World Data; it begins with our 10+ year federated network of providers and is supported
by a multi-faceted data curation process managed by an expert in-house clinical team. Additionally, we work hand-in-hand with our life
science partners regarding the case selection protocol and, when required, producing case report forms for regulatory clearance. We are
focused on delivering value by supporting life science advancements with OneMedNet’s iRWDTM which holds the key to unlocking boundless
patient care advances. We believe we unleash the power of research-grade image-centric iRWDTM that is curated to meet every cohort requirement
and stand up to the rigors of prospective clinical trials.

Today,
life science companies, including pharmaceutical companies, artificial intelligence (“AI”) developers, medical device businesses,
and clinical research organizations, share the same widespread challenge in obtaining insight-rich, high-quality patient data that explicitly
matches their precise cohort specifications. A substantial portion of patient diagnosis involves clinical imaging, and approximately
90% of healthcare data, by size, is associated with imaging. Historically, much of imaging value has been derived from its initial review,
and further gains from the image archives have been very limited.

We
help providers to Unlock the Value in Imaging Archives. By utilizing OneMedNet’s iRWDTM offering, providers can greatly improve
their research efforts with streamlined data access. Health care providers such as hospitals, clinics, and imaging centers can also accelerate
life science patient care innovations by sharing de-identified data in a well-defined and de-identified and secure manner. In return
for doing so, income is generated and applied to critical and possibly unfunded provider projects.

The
OneMedNet Difference

We
believe OneMedNet has been a leader in the business of extracting, securing, and transferring medical data for 10+ years. Doing so requires
specialized expertise in:

●
Compliance (HIPAA (as defined
below), GDPR, 21 CFR Part11)

●
Advanced privacy &
security measures

●
Clinical patient condition(s)
and hospital processes

●
Radiology interpretation

●
Artificial Intelligence
and Machine Learning (“AI/ML”) technology

Attaining
in-house expertise in all essential elements is a challenge and we believe it deters many organizations from attempting such a venture.
We take pride in this ambitious achievement while continually working to maintain state-of-the-art expertise. OneMedNet strictly adheres
to the highest level of professional and ethical standards and applicable regulations throughout all interactions and activities.

We
believe OneMedNet is a leader in the field of regulatory-grade imaging RWD curation. Doing so requires specialized expertise in AI/ML
technology, data privacy/security, as well as expertise in clinical patient condition(s) and healthcare record keeping. Having, or achieving,
expertise in all essential disciplines is a challenging achievement. OneMedNet had a significant head start with our clinical image exchange
solution which served to launch the Company over a decade ago. All data remains “native” within the federated OneMedNet iRWDTM
provider network - meaning all the data remains locally onsite until specific de-identified data is licensed for a particular life science
research opportunity.

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OneMedNet’s
Competitive Advantages

We
believe that OneMedNet iRWDTM offers the best of advanced technology, clinical expert curation, and service. Medical imaging and associated
clinical data are indexed at each network site using state-of-the-art AI/ML technology. This typically includes electronic health records
(“EHR”), radiology, cardiology, lab, pathology and more. Our in-house clinical team performs intensive curation of the data
so that results meet the specifications and requirements of life science Data Collection Protocol - regardless of the complexity.

We
believe that OneMedNet unlocks the value in imaging and EHR data in the following three principal ways:

●
Regulatory Grade - Our
imaging results serve as proof of effectiveness for regulatory agencies, meeting requirements for quality & diversity.

●
On Demand - Our powerful
indexing platform accesses and harmonizes complete patient profiles across fragmented data silos, delivering images and records on-demand.

●
Expertly Curated - We curate
to the most stringent multi-level stratified requirements, providing unmatched data accuracy and completeness.

OneMedNet’s
data is fully de-identified using a multi-step quality control process and goes beyond protected health information (“PHI”)
to include personally identifiable information (“PII”), site identifiable information, and more. Importantly, life science
users receive the data in the exact format that they require. No data sifting or manipulation is needed. The data is simply ready for
use. Moreover, OneMedNet has the unique combination of knowledge, tools, and experience to:

●
Access and harmonize complete
patient profiles across fragmented data silos;

●
Provide unmatched data
accuracy and completeness;

●
Ensure the security and
privacy of patients’ PHI;

●
Imaging RWD is our singular
passion and focus and no one does it better.

Finally,
OneMedNet has a team of highly experienced and clinically trained data curators. This team appreciates the complexity and criticality
of clinical data and can effectively communicate with both providers and life science specialists.

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Industry
Background

A
2016 analysis published in the Journal of Health Economics and authored by the Tufts Center for the Study of Drug Development placed
the cost of bringing a drug to market, including post-approval research and development, at a staggering $2.87 billion. Meanwhile, a
2018 study from the Tufts Center for the Study of Drug Development noted that the timeline for new drug development ranged from 12.8
years for the average drug to 17.2 years for ultra-orphan drugs that only affect several hundred patients. This places the onus on life
science organizations to find ways to deliver treatments to patients faster - especially those who cannot wait 17 years
for a potentially life-saving treatment. Knowing how a medicinal product is actually used by patients can help stakeholders across the
healthcare ecosystem make important and potentially life-saving real-time decisions.

Real
World Data is observational data typically gathered when an approved medical product is on the market and used by “real”
patients in real life, as opposed to clinical trials or real world images for real patients. The FDA cites several potential sources
of Real World Data, including EHR, claims, and disease and product registries. There are multiple types of data including structured
and unstructured data, clinical and billing data, transactional and claims data, patient-generated data, and data gathered from additional
sources that can shed light on a patient’s health status and more. As reliance on healthcare data grows exponentially, OneMedNet
has observed that the reliance on information has increased coming from multiple additional sources including EHR, claims, registries,
clinical trials, patient and provider surveys, wearable devices and more. These additional sources include the internet of things, social
media forums and blogs. Real World Data has the potential to break down inefficiencies and fill gaps in information silos among stakeholders
throughout the healthcare ecosystem of providers, payers, manufacturers, government entities and patients. This information sharing,
in turn, enables all parties to derive new insights, support value-based care and deliver better health outcomes.

Commercializing
a drug requires its developer to harness various sources of Real World Data to identify patient populations and refine sales and marketing
strategies for those populations among many other undertakings. Historically, this practice involved purchasing large amounts of data
from data aggregators or data platforms, if not directly from the source itself, sometimes without much knowledge about the quality of
the data. Preparing this data for analysis is both expensive and time-consuming; thus, many organizations would outsource the process
to consultants or third-party vendors. Moreover, the process of preparing this data for analysis by untrained consultants can yield a
static analysis that is difficult to modify or rerun in response to follow-up questions or potential discrepancies.

Definitions
of Real World Data and Real World Evidence

Real
World Data has become a powerful tool in the life sciences industry. After decades of relying on clinical data as the gold standard for
decision making, industry leaders now recognize how data collected in the real world adds valuable context and insight to their efforts.
From identifying unmet medical needs and defining the patient journey, to supporting regulatory submissions, proving value to payers,
and shaping market strategies, Real World Data adds value at every stage of the drug development lifecycle. Real World Data also sets
the foundation for Real World Evidence, and while the terms are often used interchangeably, they are distinct, and they are changing
health care. Here’s how it happens:

1.
First, Real World Data
are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources. Real
World Data is aggregated and transformed, such as through OneMedNet’s robust analytics. Real World Data are the data relating
to patient health status and/or the delivery of health care routinely collected from a variety of sources. There are many different
types, sources and uses of Real World Data, for example:

●
Clinical Data -
For example, clinical data from EHR and case report forms (“eCRF”) including biopsies and other pathology tests,
diagnostic imaging, social determinants of health, cancer organoids, which provide patient demographics, family history, comorbidities,
procedure and treatment history, and outcomes.

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●
Patient Generated Data
- For example, patient-generated data from patient-reported outcome surveys, which data provide insights directly from the patient
and help researchers understand what happens outside of clinic visits, procedures, and hospital stays.

●
Cost and Utilization
Data (Qualitative Studies) - For example, cost and utilization data from claims and public datasets, which data
provide information regarding healthcare services utilization, population coverage, and prescribing patterns.

●
Public Health Data
- For example, public health data from various government data sources, which add critical information to enable stakeholders
to best serve the needs of the populations they serve.

The
availability of medical imaging in Real World Data such as that provided by OneMedNet is facilitated by the development of digital image
analysis to increase the accuracy of diagnostics and conduct passive screening on large databases of medical images using AI algorithms
such as those applied by OneMedNet. Algorithms can also help identify additional diagnostic tests of value from medical images with pathology.

Real
World Evidence is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis
of Real World Data, as defined by the FDA. Real World Evidence can be generated by different study designs or analyses, including but
not limited to randomized trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective).
The difference in Real World Evidence and Real World Data focuses on the end use case. Real World Data can take the form of claims, EHR,
labs, data etc. Often this insight is used to better understand a patient’s journey or a natural history of a disorder (how does
a disease progress if left untreated.)

Real
World Evidence in contrast builds upon many of these data sets and prepares them for submission, as part of regulatory review such as
to the FDA or the European Medicines Agency (“EMA”), for example, in support of a customer’s clinical trial application.
When data and, in particular, imaging data is submitted to the FDA, the agency requires the following:

●
Guard against bias - evidence
must align with the patient population being studied - expectations focus on the similar patient demographics, comorbidities,
disease severity, etc.;

●
Traceability - confirm
the chain of custody, the source of the data is known and can be validated if required; and

●
Go-forward basis - regulatory
agencies seek evidence that aligns with the trial’s timeframe and, when possible, collect evidence that mirrors the clinical
trial’s timeline.

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One
area where Real World Evidence has been relied on heavily relates to oncology approvals. The FDA’s Oncology Center of Excellence
presented an analysis of this at the American Society of Clinical Oncology in 2021, looking at oncology applications containing Real
World Data and Real World Evidence. That analysis looked at 94 applications that were submitted from 2011–2020 and showed that
inclusion of Real World Data to support regulatory decision-making has increased dramatically over that period. In 2020 alone, there
were 28 submissions for oncology products that contained Real World Data. Outside of the oncology context, probably the most notable
recent example of an approval relying on Real World Evidence is the FDA’s July 2021 approval of a new indication for Astella Pharma
Inc.’s’ drug program (or tacrolimus) for the prevention of organ rejection in lung transplant patients. The approval there
was based on a non-interventional study providing Real World Evidence of effectiveness. FDA’s press release announcing the approval
noted that the approval was “significant because it reflects how a well-designed, non-interventional study relying on fit-for-purpose
real-world data, when compared to a suitable control, can be considered adequate and well-controlled under FDA regulations.”

An
additional recent approval of note was the FDA’s December 2021 approval of the supplemental BLA (Biological License Application)
for Orencia® to prevent graft versus host disease. The application included data from a randomized clinical trial, with additional
evidence of effectiveness provided by a registry-based clinical study that was conducted using Real World Data from the Center for International
Blood and Marrow Transplant Research. That registry study analyzed outcomes of 54 patients treated with Orencia® for the prevention
of graft versus host disease, in combination with standard immunosuppressive drugs, versus 162 patients treated with the standard immunosuppressive
drugs alone and showed efficacy in that indication.

AI
is employed in Real World Data to enhance data anomaly detection, standardization, and quality checking at the pre-processing stage.
AI is expected to offer pharma and biotech companies the ability to increase meaningful Real World Evidence output, decrease time to
insights, and make the most of the available vast data sources. A Real World Evidence technology platform that delivers smart data processing,
analysis, and outcomes offers an unparalleled opportunity to capitalize on these computing advancements.

When
used as part of an overall comprehensive Real World Evidence strategy, AI innovations can enhance drug development, improve patient treatment
and access, and drive valuable new business opportunities.

In
post-marketing studies, adverse events reporting is an area where AI is used, creating greater automation and efficiency in historical
data sets. Techniques like natural language processing (“NLP”) enable AI to scan tens of thousands of records and quickly
find adverse event details. AI-integrated analytics and automation provide access to crucial insights from historical clinical trial
Real World Data and Real World Evidence, expanding end-to-end clinical trial capabilities:

●
Data ingestion - publicly/historically
available Real World Data

●
Text extraction - NLP
used to extract key entities from clinical trial documents

●
Data transformation &
standardization - data standardization using pre-built models

●
AI model deployment - predicting
trial design impacts on costs, feasibility, cycle times, and quality risk

AI
is driving ground-breaking leaps in protein structure identification, and advances in regulations are providing healthcare research organizations
with access to Real World Data to accelerate clinical trial processes. We believe that AI-enabled technologies have unparalleled potential
to offer innovative trial design and collection, organizing, and analyzing the increasing amount of data generated by clinical trials.
AI has many applications in clinical trials, both short and long-term. AI technologies make possible innovations crucial for transforming
clinical trials, such as seamlessly combining Phases I and II, developing novel patient-centered endpoints, and collecting and analyzing
Real World Data.

OneMedNet
believes that AI tools also have wider benefits for hospitals and health systems. Professor Alexander Wong, University of Waterloo Canada
Research Chair in AI and Medical Imaging, points out that AI benefits include the potential to ease the burden on radiology departments
in terms of assessing scans and predicting upcoming demand for general hospital and intensive care beds, and demand for equipment such
as respirators and ventilators, medicines, masks, and ventilator mouthpieces, as well as aiding workforce planning.

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Across
a diverse set of imaging modalities, digital images typically include metadata and/or annotations that may include protected health information
(e.g., patient name, date of birth). Although diagnostic images generally do not warrant the same level of privacy concerns as
genomic data, researchers must also remove facial characteristics or other features that could identify a patient.

Digital
image analysis can be used to support research and development by analyzing large volumes of tissue specimens or other medical images
to run molecular screens that model biomarkers and treatment responses by transplanting a portion of a patient’s tumor into humanized
mice or 3D tissue cultures derived from stem cells that resemble miniature organs. These models allow researchers to conduct controlled
laboratory experiments that can inform treatment approaches and link predicted treatment response to actual clinical outcomes by linking
this data to EHR, claims, and other sources of Real World Data. Similarly, preclinical studies can be informed by safety assessments
conducted in animal models or studies of animal molecular biomarkers or anatomic abnormalities to minimize the burden on human study
participants. Findings can also inform clinical trial optimization by stratifying participants according to predicted response and determining
appropriate eligibility criteria.

2.
Second, Real World Evidence
is the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of Real World
Data. Real World Evidence provides clinically-rich insights into what actually happens in everyday practice and why. The U.S. Federal
Food, Drug, and Cosmetic Act of 1938 (“FD&C Act”) defines Real World Evidence as “data regarding the usage,
or the potential benefits or risks, of a drug derived from sources other than traditional clinical trials.” In developing
its Real World Evidence program, the FDA believes it is helpful to distinguish between the sources of Real World Data and the evidence
derived from that data.

Evaluating
Real World Evidence in the context of regulatory decision-making depends not only on the evaluation of the methodologies used to generate
the evidence but also on the reliability and relevance of the underlying Real World Data; these constructs may raise different types
of considerations. Real World Evidence refers to evidence about the risks and benefits of a product derived from analysis of the Real
World Data. For example, the FDA has used Real World Data and Real World Evidence, derived from its Sentinel System, the largest multi-site
distributed database in the world dedicated to medical product safety, for monitoring the safety of regulated products, in place of post-marketing
studies. It has carried this out for nine potential safety issues involving five products.

Real
World Evidence is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis
of Real World Data. Real World Evidence can be generated by different study designs or analysis, including but not limited to, randomized
trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective).

Unlike
traditional clinical trials, where necessary data elements can be curated and collection mandated, the creation of Real World Evidence
requires assessing, validating and aggregating various, often disparate, sources of data available through routine clinical practice.
Real World Evidence is used by different stakeholders in many different ways.

●
It gives life sciences
companies insight into how their drugs are being used.

●
It helps providers improve
the delivery of care.

●
It enables regulatory authorities
to monitor post-market safety and adverse events.

●
It helps payers assess
outcomes from treatments.

From
Real World Data to Real World Evidence

The
creation of Real World Evidence requires a combination of high-powered analytics, a validated approach and a robust knowledge of available
Real World Data sources (e.g., what data is captured within existing quality registries, what data can be captured through EHR
and case report forms or claims, and which patient organizations capture data on relevant patient cohorts). This process includes several
steps, which are summarized here:

1.
Defining a study protocol
answering relevant clinical questions.

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2.
Defining which data elements
can be collected from which Real World Data sources.

3.
Establishing data capture
arrangements and protocols with existing Real World Data sources.

4.
Blending disparate data
sources through probabilistic record matching algorithms.

5.
Validating and supplementing
blended data through editable eCRFs.

6.
Defining and calculating
clinically relevant outcomes and measures.

7.
Appropriately assessing
and controlling for variability in data quality, availability and confounding patient factors affecting measured outcomes.

8.
Real World Evidence can
provide a holistic view of patients that in many cases cannot be studied through traditional clinical trials.

Real
World Evidence has been proven to fill a gap between research (what we learn) and everyday practice (what we do) in healthcare, and it
creates a difference between what is expected to happen and what really happens. Driving measurable improvements in healthcare requires
us all to be rooted in the reality of what actually happens before, during, and after clinical procedures, interventions, and office
visits. Real World Evidence fills those gaps and documents the truth by establishing definitively what really happens when doctors treat
a wide range of patients that do not look like the homogeneous patient groups in a clinical trial. Because of this, Real World Evidence
serves many uses and provides many benefits across the healthcare ecosystem.

As
more countries battle to contain healthcare costs, and as the population ages and the number of patients with chronic diseases increases,
the need to remove inefficiencies and upgrade the delivery of coordinated care that improves outcomes is more pressing. At the same time,
life sciences companies are facing tumultuous times. Industry globalization, the end of the blockbuster era, and an increasingly complex
regulatory environment all add to the difficulty of bringing products to market. And across the board, companies are moving toward a
patient-centric and outcome-focused model. In this environment, Real World Evidence can be transformative for the industry when Real
World Data is combined with the right technology framework and the regulatory intelligence to make sense of it. As data is consumed across
life sciences in different ways and by different stakeholders, it can provide valuable insights and “evidence” across the
product life cycle. In addition, stakeholders across the healthcare ecosystem use this new knowledge to support decision-making and improve
safety and effectiveness, and ultimately, patient outcomes.

Uses
of Real World Evidence in Life Sciences, Among Regulators, Clinicians, Researchers and Healthcare Systems

According
to repeated studies by Deloitte, the importance of Real World Evidence continues to rise as it promises to accelerate regulatory decision-making
and support the approval of new indications for drugs already on the market. Life sciences, pharmaceutical and medical device companies
are significant consumers of Real World Evidence because it can provide value across the entire product lifecycle from pre-trial design
to clinical studies and trials to post-market surveillance. Medical product developers are using Real World Evidence to support clinical
trial designs (e.g., large simple trials, pragmatic clinical trials) and observational studies to generate innovative, new treatment
approaches.

Real
World Evidence can be used to make clinical trials more effective and efficient, for example in patient recruitment or label extension.
Real World Evidence gathered from other studies or from currently marketed products in a similar category, for example, can have a positive
effect on the product portfolio by exposing positive side effects as new potential indications. The most famous example is Viagra, which
was initially studied as a drug to lower blood pressure, but an unexpected side effect led to the drug ultimately being approved for
erectile dysfunction.

The
benefits of Real World Evidence derived from Real World Data are increasingly being recognized by regulatory authorities. The FDA released
a framework for using Real World Evidence to support the process of drug regulation and submission. This is a major step toward recognizing
that clinical trials, while still relevant, are not the only way to assess the efficacy and safety of a product. Indeed, the FDA is soon
expected to conduct its first full post-market safety approval using only Real World Evidence.

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Real
World Evidence is now accepted as a reliable source of information for regulatory decision making in certain circumstances. A primary
rationale for the FDA to use Real World Evidence is to help support the approval of a new or extended use for a drug approved under the
FD&C Act and to help support or satisfy post-approval study requirements, always with the condition that the data quality is up to
the standard required. In a recent statement, the FDA even noted how new tools for capturing data in the post-market period, including
more sophisticated use of Real World Data and Real World Evidence are providing new approaches to address important questions about the
safety and benefits of new drugs in real world settings and that these approaches have the potential to do to so more rapidly and with
greater efficiency than traditional methods.

Why
Do We Need Real World Evidence?

There
is a gap between research (what we learn) and everyday practice (what we do) in healthcare, and it creates a difference between what
is expected to happen and what really happens. But it is what really happens that matters. Driving measurable improvements in healthcare
requires us all to be rooted in the reality of what actually happens before, during, and after clinical procedures, interventions, and
office visits. Real World Evidence is here to fill those gaps and root us in truth. It tells us what really happens when doctors treat
a wide range of patients that don’t look like the homogeneous patient groups in a clinical trial. Because of this, Real World Evidence
serves many uses and provides many benefits across the healthcare ecosystem.

Uses
of Real World Evidence in Pharmaceutical and Device Companies

Pharmaceutical
and medical device companies are major consumers of Real World Evidence, as it can provide value across the entire product lifecycle.
Real World Evidence plays an important role for research across the product lifecycle for both pharmaceutical and device companies. It
can inform pre-trial study design by helping researchers identify potential patients and create proper inclusion criteria for clinical
trials. Much of medical innovation is driven by traditional clinical trials, where new pharmaceuticals and devices are rigorously studied
and tracked before they can be sold and widely distributed.

Although
clinical trials are incredibly important to determine the safety and efficacy of new technologies, when compared to Real World Evidence,
they do have some limitations. For example, a traditional clinical trial can have strict inclusion criteria that make it challenging
for providers to accurately extrapolate the results of a clinical trial to a broader population. Clinical trial participation is often
limited by who the study administrators are able to recruit, and various demographics are often not able to participate. This again challenges
the generalizability of clinical trial results across patient populations. Real World Evidence can help overcome the limitations of clinical
trials by providing information about a broader cross-section of society. This can help clinicians, researchers, and industry partners
better understand their products and how they work.

Once
a product is approved and marketed, Real World Evidence assists pharmaceutical or medical device companies understand their products’
relative safety, effectiveness, value, off-label use and more. This post-market surveillance, or post-marketing surveillance, is valuable
to stakeholders across the healthcare industry.

The
AI-enabled patient enrichment and recruitment process can improve suitable cohorts and increase clinical trial effectiveness, data management,
analysis, and interpretation of multiple Real World Data sources, including EHR and medical imaging data. This presents a unique opportunity
for NLP to perform the sophisticated analysis necessary to combine genomic data with electronic medical records (“EMR”) and
other patient data, present in various locations, owners, and formats - from handwritten paper copies to digital medical
images - to surface biomarkers that lead to endpoints that can be more efficiently measured, and thereby identify and characterize
appropriate patient subpopulations. AI-enabled systems can help to improve patient cohort composition and aid with patient recruitment.

AI
technologies can help biopharma companies identify target locations, qualified investigators, and priority candidates and collect and
collate evidence to satisfy regulators that the trial process complies with good clinical practice requirements. One of the most important
elements of a clinical trial is a selection of high-functioning investigator sites. Site qualities such as resource availability, administrative
procedures, and experienced clinicians with in-depth knowledge and understanding of the disease can shape study timelines and data quality,
accuracy, completeness, and consistency.

AI
integrated clinical trial programs can help monitor and manage patients by automating Real World Data capture, sharing data across systems,
and digitalizing standard clinical assessments. AI technologies and wearable technologies can help enable continuous patient monitoring
and generate real-time insights into the safety and effectiveness of treatment while predicting the possible risk of dropouts, thereby
enhancing patient engagement and retention. To comply with trial adherence criteria, patients must keep detailed records of their medication
intake and other data points related to their bodily functions, response to medication, and daily protocols. This can be an overwhelming
and tedious task, leading to 40% of patients becoming non-adherent after 150 days into a clinical trial. Wearable devices/sensors and
video monitoring are used to collect patient data automatically and continuously, thereby relieving the patient of this task. In combination
with wearable technology, AI techniques offer new approaches to developing real-time, power-efficient, mobile, and personalized patient
monitoring systems.

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Among
regulators, clinicians, academic researchers and healthcare systems, the reliance on curated Real World Evidence has grown significantly
because of the value it can provide, which is unique relative to each parties’ objectives and mandates. It also helps that the
FDA has also sharpened its focus on Real World Data and Real World Evidence. For example, in late 2022, the FDA published proposed guidance
related to data standards for product submissions with Real World Data and also weighed in on the use of Real World Data and Real World
Evidence to support regulatory decision-making for drugs and biological products with specific advice for data from EHR and medical claims.
In addition, the FDA uses Real World Data and Real World Evidence to monitor post-market safety and adverse events and to make regulatory
decisions. The health care community is using these data to support coverage decisions and to develop guidelines and decision support
tools for use in clinical practice.

AI
with deep-learning capability is also helpful in organizing and translating a vast amount of structured and unstructured data to Real
World Evidence. The human mind can possibly manage 4-5 variables; therefore, AI-enabled data mapping and integration and their normalization
into a common data model according to disease pathway and workflow will likely be useful for both quality management in clinical trials
and generating meaningful insight for human disease by providing a broader perspective based on Real World Data.

Market
Size

The
global Real World Evidence solutions market size was estimated at USD $2.6 billion in 2023 and is expected to grow at a compound annual
growth rate (CAGR) of 7.4% from 2024 to 2030. The market growth is driven by rising demand for enhanced Real World Evidence capabilities
within the life science industry, reflecting an increasing market shift from volume to value-based care. Advancements in data analytics
and Real World Evidence contribute to supporting regulatory compliance, research, and solution development efforts in medical device
and life sciences organizations. For instance, the increased demand for Real World Evidence solutions is prompting players to introduce
new products, fostering market growth. In October 2023, Maxis Clinical Sciences launched Real World Evidence Solutions, providing diverse
Real World Data capture and analysis to improve clinical research and care.

Government
initiatives supporting Real World Evidence programs, evolving regulations, and actionable Real World Data enable organizations to conduct
outcomes-based analyses, contributing to the overall market expansion. For instance, in December 2022, the FDA launched the Real World
Evidence Program. This program aims to raise awareness that Real World Evidence can support regulatory decisions, identify approaches
for generating Real World Evidence to meet post-approval study requirements or effectiveness labeling and develop agency processes that
foster consistent decision-making and shared learning regarding Real World Evidence.

The
COVID-19 pandemic further accelerated the adoption of Real World Evidence solutions, with governments collaborating with market players
to implement these solutions. For instance, in June 2021, ConcertAI and the FDA initiated a five-year collaborative research program,
Evaluation of Real World Outcomes and Safety in the Treatment of Cancer. The partnership leverages ConcertAI’s oncology
Real World Data and advanced AI technology solutions to generate Real World Evidence for various clinical and regulatory use cases.

Real
World Evidence solutions services allow pharmaceutical companies and healthcare providers as well as payers by providing efficient management
of operations and accelerating the process of drug development and its approval, which fuels market growth. Support from regulatory bodies
for using Real World Evidence solutions and an increase in research and development spending are anticipated to boost market growth.

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The
Real World Evidence solution providers are increasingly forming strategic partnerships with AI solution providers to offer integrated
solutions. For instance, in April 2023, ConcertAI, a player in AI SaaS technology and Real World Evidence solutions for healthcare and
life sciences, partnered with PathAI, an AI-powered pathology provider, to introduce a first-in-class quantitative histopathology and
curated clinical Real World Data solution. This collaboration integrates ConcertAI’s Patient360 and RWD360 products with PathAI’s
PathExplore tumor microenvironment panel. Based on end user, the global Real World Evidence solutions market is segmented into pharmaceutical,
biotechnology, and medical device companies; healthcare payers; healthcare providers; and other end-users (academic research institutions,
patient advocacy groups, regulators, and health technology assessment agencies). The large share of this segment is primarily attributed
to the increasing importance of Real World Evidence studies in drug development and approvals and the growing need to avoid costly drug
recalls and assess drug performance in real world settings.

With
the growing need for evidence generated from Real World Data, the increasing importance of epidemiological data in decision making, and
a shift from volume to value-based care, there has been an increased focus on patient registries, a rise in the adoption of EMR in hospitals,
and exponential growth in mobile health data and social media, which have resulted in the generation of huge amounts of medical data.
In 2021, the real world datasets segment is estimated to account for the larger share of 51.2% of the global Real World Evidence solutions
market. According to Coherent Market Insights, the global Real World Data market is estimated to be valued at $7.51 billion in 2024 and
is expected to exhibit a CAGR of 9.1% during the forecast period (2024-2031).

Our
Long-Term Growth Strategies

Our
long-term growth strategy is anchored on the following key pillars:

●
Increase
Global Reach to Meet Demand: Our strategy is to continue growing our global footprint into areas where we expect high demand
growth in the global Real World Evidence solutions
market, which is projected to grow from $2.62 billion in 2024 to $4.55 billion by 2030, at a CAGR of 8.2%. There
is a rise in emphasis on evidence-based medicine that relies on Real World Evidence, which comes from Real World Data. Market players
in healthcare industries, including regulators, healthcare providers, and payers are becoming more aware of the importance of using
Real World Data for making informed decisions regarding comparative effectiveness, treatment effectiveness, cost-effectiveness, and
safety. As a result, the demand for Real World Data solutions is increasing rapidly, which is further driving the growth of the market.
Regulatory agencies such as EMA and the FDA are making use of Real World Evidence in regulatory decision-making processes. These
regulatory authorities have frameworks and guidelines for using Real World Evidence and Real World Data in regulatory submissions,
post-market surveillance, and drug approvals. As a result, the demand for Real World Data is rising, which in turn is expected to
support growth of the market in the coming future. The use of Real World Evidence derived from Real World Data demonstrates value
and cost-effectiveness of medical devices and drugs for healthcare technology assessment agencies and payers. With this Real World
Evidence, market access becomes easier, and it also enables reimbursement negotiations. This further facilitates the inclusion of
new therapies in the coverage of healthcare, which in turn creates major opportunities in the global market.

●
Innovate
Our Commercial Approach to Drive Incremental Market Share: We intend to expand our sales network across the globe, while
simultaneously building out our sales infrastructure. We intend to focus on our target markets, which include (i) Imaging AI; (ii)
medical device companies; and (iii) pharmaceutical companies, as summarized here:

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●
Enhance
and Refine Our Service Offering: Building on our customer-centric mindset throughout our development, curation and commercial
processes, we plan to continue expanding and improving our service offering. As we continue to expand into additional geographies
globally, we plan to build upon these three pillars:

●
Expand
Our Product Offering: We plan to continually evaluate the benefits of expanding our portfolio into other high-growth, high-demand
Real World Data and Real World Evidence solutions in the future.

Corporate
Information

Data
Knights was originally incorporated in Delaware on February 8, 2021 under the name “Data Knights Acquisition Corp” as a special
purpose acquisition company, formed for the purpose of effecting a merger, capital stock exchange, asset acquisition, stock purchase,
reorganization or similar business combination with one or more businesses.

On
November 7, 2023, a subsidiary of Data Knights merged with and into OneMedNet Solutions Corporation (formerly named OneMedNet Corporation)
(“Legacy ONMD”), with Legacy ONMD surviving as a wholly-owned subsidiary of Data Knights (the “Business Combination”).
In connection with the Business Combination, Data Knights changed its name to “OneMedNet Corporation.”

We
are located at 6385 Old Shady Oak Road, Suite 250, Eden Prairie, MN 55344 and reachable by telephone on 800-918-7189.

Legacy
ONMD was incorporated in the State of Delaware on November 20, 2015. Its wholly-owned subsidiary, OneMedNet Technologies (Canada) Inc.
(“ONMD Canada”), was incorporated on October 16, 2015 under the provisions of the Business Corporations Act of British Columbia.
ONMD Canada’s functional currency is the Canadian dollar.

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Recent
Developments

Private
Placements

June
2025 Financing

On
June 19, 2025, we entered into a securities purchase agreement with James Sixsmith (“Mr. Sixsmith”), pursuant to which we
agreed to issue and sell to Mr. Sixsmith 3,390,923 shares of our Common Stock at a price of $0.42 per share and pre-funded warrants exercisable
for 2,561,457 shares of our Common Stock at an exercise price of $0.42 per share. Mr. Sixsmith
was required to prepay the exercise price for the pre-funded warrants, other than $0.0001 per share. The pre-funded warrants will be
exercisable at any time after the date of issuance and will not expire. Mr. Sixsmith is entitled to receive dividends on the pre-funded
warrants, if declared, on an as-if-converted-to-common-stock basis, and in the same form as dividends actually paid on shares of our
Common Stock.

We
received net proceeds of $2.5 million from the June 2025 private placement, after deducting an immaterial amount of offering expenses.

Subscription
Agreements – Related Parties

On
June 20, 2025, we entered into subscription agreements with Dr. Thomas Kosasa and Dr. Jeffrey Yu, pursuant to which we agreed to issue
1,190,476 and 1,666,667 shares of our Common Stock, respectively, at a price of $0.42 per share.

On
August 29, 2025, we entered into another subscription agreements with Dr. Thomas Kosasa, pursuant to which we agreed to issue 581,395
shares of our Common Stock, at a price of $0.86 per share.

We
received net proceeds of approximately $1.7 million from these subscription agreements, after deducting an immaterial amount of offering
expenses.

Debt
Reductions

On
June 17, 2025 and June 19, 2025, the holders of Pre-Closing PIPE Notes delivered notices to the Company of their respective elections
to convert an aggregate of $1.7 million of outstanding principal and accrued interest (carrying amount of $0.5 million at the time of
conversion) under the PIPE Notes (the “PIPE Notes Conversion”). Under the PIPE Notes Conversion, we issued an aggregate of
1,453,174 shares of Common Stock in full satisfaction of the PIPE Notes. The PIPE Notes were converted pursuant to their terms at a conversion
price of $1.14 per share.

Between
June and July 2025, we negotiated and settled certain trade payables and other amounts owed by the Company representing an aggregate
of approximately $6.3 million of current liabilities as reflected on the Company’s consolidated balance sheets as of December 31,
2024, which amount includes the settlement of approximately $3.3 million of deferred underwriter fees payable to EF Hutton (the “Settlements”).

We
entered into a letter agreement, dated May 19, 2025, with Slickage (the “Slickage Agreement”) to settle $177,500 of trade
accounts payable owed by the Company to Slickage through the issuance of 250,000 shares of Common Stock to Slickage (the “Slickage
Shares”), representing a conversion price of $0.71 per share. Pursuant to the Slickage Agreement, we agreed to register for resale
the Slickage Shares on our registration statement declared effective by the SEC on July 24, 2025.

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On
June 19, 2025, we entered into agreements with Dr. Kosasa and Dr. Yu to convert an aggregate of approximately $3.3 million of outstanding
principal and accrued interest under certain shareholder loans and business combination extension loans (collectively, the “Loans”)
made by Dr. Kosasa and Dr. Yu to the Company into an aggregate of 4,693,299 shares of Common Stock (the “Loan Conversions”).
The Loans were converted at a conversion price of $0.71 per share. Pursuant to Loan Conversions, (i) Dr. Kosasa converted Loans with
aggregate principal and accrued interest of approximately $3.6 million for 2,865,019 shares of Common Stock, and (ii) Dr. Yu converted
Loans with aggregate principal and accrued interest of approximately $1.3 million for 1,828,280 shares of Common Stock. The shares of
Common Stock issued under the Loan Conversions were issued in reliance on the exemptions from registration provided by Section 4(a)(2)
under the Securities Act.

On
June 19, 2025, Dr. Kosasa delivered notice to the Company of his election to convert in full the amounts of outstanding principal under
certain convertible shareholder loans (the “Kosasa Convertible Loans”) previously made by Dr. Kosasa to the Company, in an
aggregate principal amount of approximately $1.6 million, converting into an aggregate of 2,123,424 shares of Common Stock (the “Kosasa
Convertible Loan Conversions”). The Kosasa Convertible Loans were converted pursuant to their terms at a conversion price of $0.7535
per share. The shares of Common Stock delivered to Dr. Kosasa upon the Kosasa Convertible Loan Conversions were delivered in full satisfaction
of amounts owed to Dr. Kosasa under the Kosasa Convertible Loans.

Overall,
between May and July of 2025, as reflected above, the Company settled or converted an aggregate of approximately $11.9 million of current
liabilities of the Company in connection with the Settlements, the Loan Conversions, the Kosasa Convertible Loan Conversions and the
PIPE Notes Conversions, representing a 62% reduction in the Company’s total liabilities outstanding as of December 31, 2025.

Government
Regulation

Many
aspects of our businesses are regulated by federal and state laws, rules and regulations. Accordingly, we maintain a robust compliance
program aimed at ensuring we operate our business in compliance with all existing legal requirements material to the operation of our
businesses. There are, however, occasionally uncertainties involving the application of various legal requirements, the violation of
which could result in, among other things, fines or other sanctions. See “Risk Factors” for additional detail.

Regulation
of Patient Information. Our information management services relate to the processing of information regarding patient diagnosis
and treatment of disease and are, therefore, subject to substantial governmental regulation. In addition, the confidentiality of patient-specific
information and the circumstances under which such patient-specific records may be released for inclusion in our databases or used in
other aspects of our business is heavily regulated. Federal, state and foreign governments are contemplating or have proposed or adopted
additional legislation governing the possession, use and dissemination of personal data, such as personal health information and personal
financial data, as well as security breach notification rules for loss or theft of such data. Additional legislation or regulation of
this type might, among other things, require us to implement additional security measures and processes or bring within the legislation
or regulation deidentified health or other data, each of which may require substantial expenditures or limit our ability to offer some
of our services.

In
particular, personal health information is recognized as a special, sensitive category of personal information, subject to additional
mandatory protections. Violations of data protection regulations are subject to administrative penalties, civil money penalties and criminal
prosecution, including corporate fines and personal liability.

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Data
Privacy

Certain
of our operations are subject to regulation under the administrative simplification provisions of HIPAA. Federal regulations related
to HIPAA contain minimum standards for electronic transactions and code sets and for the privacy and security of protected health information.
Patient health information is among the most sensitive of personal information, and it is critically important that information about
an individual’s healthcare is properly protected from inappropriate access, use and disclosure. Real World Evidence - information
that allows us to examine actual practices and outcomes - is essential to increase access to care, improve outcomes, and
lower costs.

OneMedNet
uses a wide variety of privacy-enhancing technologies and safeguards to protect individual privacy while generating and analyzing information
on a scale that helps healthcare stakeholders identify disease patterns and correlate with the precise treatment path and therapy needed
for better outcomes. We employ a wide variety of methods to manage privacy requirements, including:

●
governance, frameworks,
models and training to promote good decision making and accountability;

●
a layered approach to privacy
and security management to avoid a single point of failure;

●
ongoing evaluation of privacy
and security practices to promote continuous improvement;

●
use of technical, administrative,
physical and organizational safeguards and controls;

●
collaboration with data
suppliers and trusted third parties for our syndicated market research and analytics offerings to remove identifiable information
or employ effective encryption or other techniques to render information non-identified before data is delivered to us; and

●
work with leading researchers,
policy makers, thought leaders and others in a variety of fields relevant to the application of effective privacy and security practices,
including statistical, epidemiological and cryptographic sciences, legal, information security and compliance, and privacy.

We
have relied on expertise in the industry with de-identifying data. Our capabilities allow us to render data non-identified while still
maintaining data utility, thus protecting privacy while still advancing innovation. Not only do we make use of de-identification techniques
with respect to the data we hold, but we also share our expertise in this area with policymakers, regulators and others to help them
understand de-identification methodologies and practical considerations to avoid re-identification risk. We operate in more than 100
countries around the world, many of which have data protection and privacy laws and regulations based on similar core principles (e.g.,
openness, accountability, security safeguards, etc.). We apply those principles globally and augment our practices to address local
laws, contractual obligations and other data privacy requirements.

Our
Compliance team, led by our Chief Compliance Officer, is comprised of privacy professionals and privacy law experts who drive our strategy
and develop and manage our policies and standards. The Compliance team provides subject matter expertise related to the proper management
of all data types. In addition, our Compliance team liaises with our Legal, Information Technology, Information Security and other teams
so that privacy requirements are addressed in technology, contracting, offerings and other business activities.

The
OneMedNet Privacy Policy is our foundational privacy policy. It explains how, when applicable, we collect, hold, use and disclose personal
information, including that of our personnel, consumers, healthcare professionals, patients, medical research subjects, clinical investigators,
customers, suppliers, vendors, business partners and investors.

Regulatory
Quality Compliance (FDA 21 CFR Part 11)

OneMedNet
provides high-quality, de-identified, regulatory-grade imaging and clinical data; as such OneMedNet adheres to all applicable local and
Federal regulatory quality requirements, including but not limited to FDA 21 CFR Part 11. OneMedNet maintains a rigorous and ongoing
internal quality management system to enable the organization to produce high quality regulatory compliant clinical data for our clients
and consumers. This program includes:

●
Ongoing internal audits,
policy reviews, and procedure testing to ensure validation, audit trails, legacy systems, and record handling and retention adhere
to the latest regulatory guidelines and best practices; and

●
Regular third-party or
client-initiated external audits to assess the compliance of OneMedNet to ensure operations are in accordance with the applicable
regulations, standards, policies, and standard operating procedures.

15

Human
Capital Resources

Our
workforce is comprised of approximately 23 employees (as of December 31, 2025), including approximately 1 part-time employee (references
herein to “employees” include to the employees of our subsidiaries). Our Board of Directors and its committees oversee human
capital matters through regular reporting from management and advisors.

Compensation
and Benefits

We
provide competitive compensation and benefits programs to help meet the needs of our employees. In addition to salaries, these programs
(which vary by location of the employee) include a 2022 Stock Option Plan, health care and insurance benefits, health savings and flexible
spending accounts, paid time off, family leave, family care resources, and employee assistance programs, among many others.

Implications
of Being an Emerging Growth Company

As
a company with less than $1.235 billion in revenues during our last fiscal year, we qualify as an emerging growth company as defined
JOBS Act enacted in 2012. As an emerging growth company, we expect to take advantage of reduced reporting requirements that are otherwise
applicable to public companies. These provisions include, but are not limited to:

●
being permitted to present
only two years of audited financial statements, in addition to any required unaudited interim financial statements, with correspondingly
reduced “Management’s Discussion and Analysis of Financial Condition and Results of Operations” disclosure;

●
not being required to comply
with the auditor attestation requirements of Section 404 of the Sarbanes-Oxley Act of 2002, as amended (“Sarbanes-Oxley Act”);

●
reduced disclosure obligations
regarding executive compensation in our periodic reports, proxy statements and registration statements; and

●
exemptions from the requirements
of holding a nonbinding advisory vote on executive compensation and stockholder approval of any golden parachute payments not previously
approved.

Available
Information

We
file with, or furnish to, the SEC reports, including our annual report on Form 10-K, quarterly reports on Form 10-Q, current reports
on Form 8-K and amendments to those reports pursuant to Section 13(a) or 15(d) of the Exchange Act. These reports are available free
of charge via EDGAR through the SEC website (www.sec.gov) and are also available free of charge on our corporate website (https://www.onemednet.com)
as soon as reasonably practicable after they are electronically filed with or furnished to the SEC. The foregoing website addresses are
provided as inactive textual references only. The information provided on our website (or any other website referred to in this Annual
Report) is not part of this report and is not incorporated by reference as part of this Annual Report.

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