# Joining Forces for Pathology Diagnostics with AI Assistance:
The EMPAIA Initiative

Norman Zerbe<EMAIL_ADDRESS>Lars Ole Schwen  Christian Geißler
Katja Wiesemann  Tom Bisson  Peter Boor  Rita Carvalho  Michael Franz
Christoph Jansen  Tim-Rasmus Kiehl  Björn Lindequist  Nora Charlotte Pohlan
Sarah Schmell  Klaus Strohmenger  Falk Zakrzewski  Markus Plass  Michael Takla
Tobias Küster  André Homeyer  Peter Hufnagl  Charité – Universitätsmedizin
Berlin, corporate member of Freie Universität Berlin and Humboldt Universität
zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359
Bremen, Germany  Technische Universität Berlin, DAI-Labor, Ernst-Reuter-Platz
7, 10587 Berlin, Germany  QuIP GmbH, Reinhardtstraße 1, 10117 Berlin, Germany
Institute of Pathology, University Hospital RWTH Aachen, Pauwelsstraße 30,
52074 Aachen, Germany  Institute of Pathology, Carl Gustav Carus University
Hospital Dresden (UKD), TU Dresden (TUD), Fetscherstrasse 74, 01307 Dresden,
Germany  Medical University of Graz, Diagnostic and Research Center for
Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Neue
Stiftingtalstrasse 6, 8010 Graz, Austria  vitasystems GmbH, Gottlieb-Daimler-
Straße 8, 68165 Mannheim, Germany

###### Abstract

Over the past decade, artificial intelligence (AI) methods in pathology have
advanced substantially. However, integration into routine clinical practice
has been slow due to numerous challenges, including technical and regulatory
hurdles in translating research results into clinical diagnostic products and
the lack of standardized interfaces.

The open and vendor-neutral EMPAIA initiative addresses these challenges.
Here, we provide an overview of EMPAIA’s achievements and lessons learned.
EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e.,
pathologists, computer scientists, and industry. In close collaboration, we
developed technical interoperability standards, recommendations for AI testing
and product development, and explainability methods. We implemented the
modular and open-source EMPAIA platform and successfully integrated 11 AI-
based image analysis apps from 6 different vendors, demonstrating how
different apps can use a single standardized interface. We prioritized
requirements and evaluated the use of AI in real clinical settings with 14
different pathology laboratories in Europe and Asia. In addition to technical
developments, we created a forum for all stakeholders to share information and
experiences on digital pathology and AI. Commercial, clinical, and academic
stakeholders can now adopt EMPAIA’s common open-source interfaces, providing a
unique opportunity for large-scale standardization and streamlining of
processes.

Further efforts are needed to effectively and broadly establish AI assistance
in routine laboratory use. To this end, a sustainable infrastructure, the non-
profit association EMPAIA International, has been established to continue
standardization and support broad implementation and advocacy for an AI-
assisted digital pathology future.

##  1 Overview of EMPAIA

Advances in precision medicine, enabled by improved diagnostic accuracy and
novel therapeutics, have significantly improved clinical outcomes for
patients. Pathology, a central diagnostic part of precision medicine, thus
faces a substantial increase in workload [1, 2, 3]. At the same time, there is
a global shortage of pathologists [4, 5, 6]. AI-assisted image and data
analysis could increase pathologists’ productivity [7, 8, 9, 10]. Researchers
have published many promising algorithmic solutions [11, 12]. However, the
path to wide clinical adoption is difficult. A core problem is a lack of
standardization and interoperability for the seamless integration of image
analysis methods into diverse image management and laboratory information
systems. Commercialization and clinical implementation of pathology AI must
overcome additional hurdles [13, 14], namely the transformation of an idea
into an AI prototype (which requires data acquisition), a validation process
towards market readiness, and certification as a medical product. Finally,
reimbursement and billing issues must be solved to generate revenue.

The EcosysteM for Pathology diagnostics with AI Assistance (EMPAIA) consortium
was established in response to the 2019 innovation competition “Artificial
Intelligence as a Driver for Economically Relevant Ecosystems” by the German
Federal Ministry for Economic Affairs and Climate Action. EMPAIA’s mission was
to promote the ecosystem for AI in pathology by involving all relevant
stakeholders and addressing issues of digitalization, standardization, legal
and regulatory requirements, and billing. In addition, the sister project
“EMPAIA Austria” was funded by the Austrian Research Promotion Agency to
conduct related research on user interfaces and explainability.

The specific objectives of EMPAIA were:

  1. 1.

Specify open interfaces for interoperable AI apps in pathology, taking into
account requirements of different vendors.

  2. 2.

Collaborate with diverse pathology labs (“reference centers”) to evaluate and
obtain practical feedback on the EMPAIA developments.

  3. 3.

Support AI vendors by open-source reference implementations of the interfaces
and by recommendations on product development and regulatory affairs.

  4. 4.

Develop explainable AI (XAI) approaches specifically for pathologists.

  5. 5.

Transfer knowledge and facilitate exchange between all stakeholders.

The core EMPAIA team comprised 25 members, including pathologists,
informaticians, mathematicians, technicians, designers and supporting staff.
Many international stakeholders have been involved from the beginning,
including vendors of AI solutions/pathology software systems/scanners,
diagnosticians from large and small clinical institutions, pharmaceutical
companies, and AI researchers. Having started with 26 commercial partners and
5 partner associations, the network has grown substantially to 59 commercial
partners, 5 associations, and 16 reference centers (Fig. 1). An updated list
of consortium members, industry partners, and reference centers can be found
on the project webpage [15].

Figure 1: Locations of national and international EMPAIA partners. (World map
adapted from Wikimedia Commons [16]).

Here, we discuss the achievements of EMPAIA, including the corresponding
lessons learned and their future continuation. We describe technical
developments, such as the specification of unified interfaces for AI apps
together with multiple industry partners and how the EMPAIA platform can
facilitate their adoption. In this context, we also describe how we
collaborated with reference centers to test the developments in real workflow
scenarios. We summarize our research findings for XAI in pathology to make
analysis results more understandable and gain user acceptance. We describe how
EMPAIA supported AI providers with regulatory issues, for instance, by
publishing guidelines [17] and initiating a service for the validation of AI
solutions. We describe how we reached out to diverse audiences of the digital
pathology ecosystem to increase visibility and engage relevant stakeholders.
Last but not least, we provide an outlook on how the newly-founded non-profit
EMPAIA International association will drive forward these activities in the
future.

##  2 Technical Developments and Evaluation

###  2.1 Standardization

For efficient use in the diagnostic workflow, AI solutions must interact with
various systems in the laboratory’s IT infrastructure. The basis of this
infrastructure is an AP-LIS, which manages case and sample information,
including diagnostic results. An IMS handles slide scans for whole slide
images (WSIs), which can be implemented as a picture archiving and
communication system (PACS) or vendor-neutral archive (VNA) [14]. In addition,
there are pathology workstations with graphical user interfaces for WSI
viewing and interacting with AI solutions, compute infrastructure for high-
performance AI processing, AI application registries, and billing systems.

The use of AI solutions is severely hampered by a lack of interoperability
[18]. Some software systems already offer their own application programming
interfaces (APIs) for executing AI solutions and exchanging input and output
data. In this process, WSIs are usually represented according to proprietary
file format specifications from various scanner vendors. AI developers must
adapt their solutions to all these different APIs and WSI formats, leading to
repetitive integration work and delays in the availability of AI applications,
which drives up their costs. Many systems currently do not even offer any
interfaces for AI solutions [19].

####  2.1.1 The EMPAIA Platform

To improve this situation, we specified the EMPAIA App Interface, an open and
vendor-neutral standard for integrating AI solutions into pathology software
systems [18]. The API was developed in close cooperation with associated
industry partners, specifically considering their requirements. To facilitate
the adoption of the EMPAIA App Interface, we published comprehensive
documentation describing the technical implementation from the perspective of
developers of AI apps and pathology software systems [20]. In addition, we
released the EMPAIA App Test Suite, an open-source toolbox for automatically
checking the compliance of a given AI app [21]. With the documentation and
test suite, third-party developers were able to integrate and test their
applications without further advice.

Figure 2: EMPAIA Platform architecture. Simplified diagram of the EMPAIA
Platform architecture, showing the main APIs serving as abstractions over
clinical system software components.

The EMPAIA App Interface is embedded in the EMPAIA Platform (Fig. 2). Its
highly modular architecture demonstrates how the different systems of the
laboratory IT infrastructure can be integrated effectively to enable the use
of AI [22]. The platform provides ready-to-use implementations of critical
components, such as a user workbench with a virtual microscope viewer and data
management services. The platform is purely prototypical, neither certified as
a medical product nor intended to compete with commercial pathology software
systems. However, it is open source and subject to the industry-friendly MIT
license [23], so both software system providers and laboratory IT
administrators can use the platform as a blueprint and testbed for custom
implementations.

The EMPAIA Platform comprises several APIs that represent abstractions of
different software systems (Fig. 2). These abstractions allow for flexibility
in the implementation of the technical components and enable combining
different programming languages and database technologies while maintaining
compatibility between the systems. The heart of each AI app is a model that
takes image data and other parameters as input, processes the image and
produces output data. EMPAIA specifies how such AI models are encapsulated
using container technologies for uniform execution. AI models communicate via
the EMPAIA App Interface to transmit input and output data. Equally important
are user interfaces that allow pathologists to view WSI, interact with the AI
solution, e.g., by drawing a region of interest (ROI), and render processing
results. The EMPAIA App UI (user interface) concept allows AI vendors to
bundle a custom web-based user interface with their apps, which can provide an
optimized user experience for the specific diagnostic task. It also
potentially reduces the effort required for regulatory compliance when
integrating into new systems because AI models are usually validated in the
context of the full diagnostic workflow. This diagnostic workflow includes the
graphical user interface that displays AI results in a specific way and,
depending on the AI app, might enable user interactions like drawing an ROI or
selecting hotspots for further analysis. EMPAIA allows custom app user
interfaces to be seamlessly embedded into browser-based pathology workstations
and to connect with the underlying clinical system via the EMPAIA Workbench
API [20].

####  2.1.2 Lessons Learned

Since the utility of the EMPAIA Platform is highly dependent on the acceptance
and contributions of external AI companies, it was essential to pursue an
agile development approach. First, a detailed requirements analysis was
conducted together with AI companies to identify a small subset of features
covering the minimum requirements of many products on the market. This allowed
us to quickly release a minimum viable version of the platform. While this
version was limited to the analysis of ROIs drawn by users in a WSI viewer, it
enabled AI developers to create first usable prototypes of their apps and to
provide early feedback for future development. Building on the minimum viable
version, the platform was iteratively expanded and improved, for instance, by
further annotation types, configuration options, or preprocessing
functionality. The development process also revealed previously unknown
requirements. In particular, the need to enable custom app user interfaces was
only identified through feedback from regulatory experts. It made our original
concept of a unified user interface for all apps obsolete [22].

The inconsistent implementation of apps from different vendors proved to be
particularly challenging. On the one hand, the apps were implemented based on
different computing platforms and paradigms, e.g., as Windows-based client
applications or Linux-based cloud applications. Only container technologies
allowed all apps to be integrated uniformly into the EMPAIA Platform. On the
other hand, the apps expected different data inputs: While some apps could
open WSIs of different formats by themselves, others depended on corresponding
functionality in the host platform. Therefore, the platform had to be highly
modular to supplement missing functionality as needed. This requirement is
fulfilled with a layered design of APIs that serve as abstractions of the
underlying implementations. Furthermore, the reference implementation follows
a microservice architecture that allows parts of the implementation to be
replaced, updated or improved as necessary, without affecting unrelated parts
of the platform.

####  2.1.3 Perspective

The EMPAIA Platform is continuously being extended to new requirements. To
coordinate future developments, we set up the EMPAIA Community Group, an open
communication forum for all stakeholders [24]. Changes and extensions to the
platform are discussed in a transparent and formal manner through EMPAIA Mod
Proposals [25]. One of the most important current proposals is adopting
DICOMweb (see below). Another highly requested extension is the possibility of
storing and visualizing pixel-wise data overlays on WSIs [26]. Such extensions
would improve communication with DICOM-based PACS systems and enable AI apps
to provide explainability functionality, thus simplifying integration and
increasing the adoption of AI in clinical practice.

Clinical systems (AP-LIS, IMS) currently used in practice still have to work
with various proprietary WSI file formats because the DICOM WSI standard has
yet to be widely adopted. The EMPAIA team designed a common API that serves as
an abstraction of such file formats (including DICOM WSI files). This approach
allowed for a quick start in development to deliver a practical solution but
resulted in an API that is not specifically optimized for DICOM. Since DICOM
is widely considered to be the future standard for WSI storage and
transmission [19], the EMPAIA team is investigating a shift towards the
DICOMweb standard on the API level as part of an upcoming platform release
while still supporting the usage of proprietary formats in the backend [27].

The IHE PaLM Technical Committee provides guidelines for Pathology and
Laboratory Medicine (PaLM). [28] For instance, the committee published the
Digital Pathology Image Acquisition (DPIA) profile [29], describing the
digitization and storage process for WSIs. The EMPAIA specifications
complement the IHE proposals by specifying how AI apps can access image data
after storage in a clinical system.

Adopting the open EMPAIA App Interface and building on the EMPAIA Platform is
in the hands of the industry. As of Dec. 2023, 14 AI apps from eight companies
have been integrated into the EMPAIA platform [30], and more integrations are
being actively developed. Four international vendors of pathology software
systems are in the process of enabling the APIs in their products. A growing
catalog of compatible AI apps has the potential to encourage more software
system providers to undertake integration efforts to enable their customers to
use such apps, which, in turn, will trigger network effects that will
encourage more AI companies to join the initiative. The EMPAIA specifications
impose strict requirements and a separation of concerns between clinical
systems and AI apps. EMPAIA, therefore, offers the advantage of a unified
approach for interoperability, reducing the multiplied efforts of integrating
many heterogeneous AI apps of different companies with various clinical
systems.

###  2.2 Reference Centers

For an ecosystem project, it was crucial to partner with a variety of
laboratories that used apps via EMPAIA. These partnerships enabled a better
understanding of the requirements for adopting and using AI in clinical
routine, testing the platform in practical settings, identifying and
understanding issues, and discussing the potential for improvement.

For this reason, EMPAIA collaborated with multiple national and international
reference centers, including seven university hospitals, two hospital chains
and six larger private practice groups. The reference centers implemented and
tested the EMPAIA platform on-site and provided feedback from a
clinical/technical perspective. They also participated in a user study
evaluating the image analysis apps available via the platform. In addition,
the reference centers performed their own research in the context of EMPAIA
[31, 32, 33, 34, 35]. The centers were at very different stages of the
digitization process, which created additional challenges but was crucial for
accurately representing real-world conditions.

####  2.2.1 Platform Deployment and Usage

To evaluate the EMPAIA platform, reference centers were provided either access
to a deployment in the EMPAIA cloud or an on-premises offline deployment. The
deployment included a prototypical data management application that is part of
the EMPAIA platform. This way, the evaluation could begin even before a full
LIS and IMS integration was completed. The data management application
performed client-side anonymization before data transfer, mitigating data
protection concerns and avoiding unnecessary transfer of patient data [36].
For the evaluation, the reference centers could run selected analyses on
demand, i.e., by drawing ROIs to trigger computations. The example apps
available comprised a mix of research use-only, non-approved, and CE-IVD-
approved apps. For legal and licensing reasons, app usage had to be restricted
to evaluation and clinical use was prohibited

To familiarize pathologists and qualified staff with AI at the reference
centers, we asked them to evaluate multiple apps integrated into the EMPAIA
platform. In this context, we collected structured feedback from these users
on the perceived correctness of histopathological tissue analysis with AI-
assisted assessment compared to human-only assessment and feedback about
usability and bugs. Pathologists, medical students, and researchers, including
users with extensive routine experience in pathology and digital natives, were
included to cover a diverse range of users. Depending on the preferences of
the reference centers, human-only assessment was performed using a routine
microscope or pathology viewer software. This collection of user feedback
focused on obtaining actionable feedback for iterative improvement and thus
provides anecdotal results rather than unbiased quantitative performance data.

####  2.2.2 Lessons Learned

Despite obvious advantages, such as reducing efforts in procuring hardware or
deploying in-house infrastructure, using cloud services posed a significant
problem for some institutions due to IT security vulnerability and privacy
concerns. For cloud-based deployments, a privacy agreement had to be reached
between reference centers and EMPAIA, ensuring the secure transfer of
anonymized medical data. As some apps depend on external cloud services, such
apps were unavailable in the on-premises deployments. This indicates a lose-
lose situation known from the ongoing controversy over local vs. cloud
solutions: vendors of cloud-only apps lose potential clients, while labs using
only offline apps lose potentially valuable tools.

User feedback provided clues as to why satisfaction with app assistance still
has room for improvement. Besides issues with image quality, which need to be
avoided by optimized lab workflows, and outages of the EMPAIA infrastructure,
feedback concerned issues concerning app results, app usability and app usage
as part of the current platform. After categorization, we clarified details
with the reference centers, curated the feedback, and forwarded it, as
appropriate, within EMPAIA or to the app developers for improvements.

One app yielded results that were obvious to be inadequate in several labs,
and another yielded inadequate results in a single lab, possibly due to the
incompatibility of WSIs generated with a specific scanner. One app was
criticized for not yet computing diagnostically relevant quantities from the
image analysis results, and one app was criticized for poor usability. On the
other hand, one app was praised for its capability to detect and quantify weak
signals barely visible to the human eye. These issues and feature requests
were followed up with the vendors.

The process of drawing an ROI and waiting for the computation of analyses was
often perceived as excessively slow, which occasionally compelled users to
create ROIs that were too small for any meaningful analysis. This underlines
the need for unobtrusively fast implementations and, e.g., running
computationally expensive WSI-wide analyses before a pathologist starts
interacting with the case. The latest EMPAIA API version supports such
preprocessing for compatible apps. Manual correction of algorithmic results
was viewed as cumbersome. App results that did not align with criteria present
in pathological analyses comprehensible and assessable by pathologists (e.g.,
glandular units) were not trusted, underlining the necessity of suitable
explainable AI approaches. This indicates the need for more communication and
a collaborative app development process between developers and users. This
should lead to a clearer alignment of user expectations, app capabilities, and
presentation of algorithmic results. Moreover, users considered integration
with LIS indispensable and this issue will need to be evaluated once
available. Operating apps as isolated tools was not considered useful for
routine diagnostics.

These findings provide valuable user feedback both for the improvement of
individual apps and for the next steps of integration in an iterative manner.
The evaluation so far has focused on single apps. Once combinations of apps
are available to assess complete clinical cases with different stainings,
usability studies will need to investigate to which extent user interaction
should be homogenized across apps by different vendors. App performance should
be evaluated in more detail, considering real patient cases with slides of
different staining, i.e., requiring a targeted study setup for app validation.
For this purpose, providing multiple apps via the same platform permits head-
to-head comparisons and allows users to choose the optimal apps for their
requirements.

##  3 Explainability

Apps using AI algorithms are typically intransparent with respect to how
results are obtained from the input images. There is an agreement on the need
to control and understand AI solutions, especially when being used in
potentially life-threatening domains such as medical diagnostics [37, 38].
However, there are multiple facets to explainability, e.g., which aspects
should be explained to whom. Currently, no consensus exists on terminology and
definitions of explainability of AI.

In EMPAIA, an AI application typically performs deep learning-based image
analysis on WSIs. It, therefore, consists of one or more deep neural networks
(models) and additional algorithms for pre-, intermediate and post-processing
tasks. While such deep learning models can achieve very high task performance
(e.g., classification accuracy, intersection over union for segmentation
tasks), model size and complexity are barriers to understanding their inner
workings, making them a “back box.” Since their high-dimensional decision
space and stochastic origin provide no inherent guarantee for correctness, one
needs other means of estimating their function and reliability. Since the
qualities of an explanation are highly dependent on the recipient of the
explanation, the situation in which it is given and the goal it is expected to
support, one needs to define this context in order to effectively discuss,
select and measure an explainability method [39].

We systematically investigated aspects of explainability within and around the
context of using AI-based diagnostic support software in a clinical setting
[40]. This includes detailed knowledge of the stakeholders to provide
customized explainability [41]. We address different stakeholders, with a
focus on operators of the AI software (pathologists), developers of AI
software, decision-makers, and validators who need to make high-level
decisions about the use of AI software. In the following paragraphs, we will
lay out their use cases, goals and our efforts towards providing explanations
for each of these use cases.

The first and most prominent user group are the pathologists or technical
assistants who are directly operating the AI application within tight time
restrictions and with the goal of providing the correct medical diagnosis. In
this group, explainability is a potential source of trust and safety. As
pathologists are the ones who have to decide and are responsible for their
decisions, they have an inherent need for information that helps them with
their judgment.

We surveyed pathologists to learn about their general preferences and
understandings regarding explainability methods and explanations [40]. Besides
favoring explanations that are close in style and content to what another
pathologist would provide them with, we also noticed that some misinterpreted
the explanations for segmentations. Besides the machine learning-related part
of XAI, we also looked at human-centric evaluations. We found that user-
friendly interfaces, enabling pathologists to explore causal relationships,
play an essential role in keeping the human in the loop. However, such
interfaces require more research and innovation [41].

Pathologists require explanations on a specific slide, sample or case. These
instance-based explanations are called local explanations. To provide XAI on
the pathologists’ desk, it has to be available and easy to use, e.g., by
integrating it directly into the applications and user interfaces they use in
their daily work, alongside the actual results of the ML algorithms. This can
be achieved in many ways (Fig. 3), with varying degrees of explanatory value
and understandability or the potential for misinterpretation if unfamiliar
with the underlying method.

Figure 3: XAI Approaches. Overview of different XAI approaches, the level they
address and the project deliverables that have been affected or designed to
support them. Local approaches provide explanations during usage of AI
applications and directly address pathologists. Global approaches are
supplements of AI applications that help decide whether they should be
acquired in general.

The second stakeholder and use case that we address are application
developers. They aim to provide trustworthy, e.g., reliable, well-performing
applications that follow legal and ethical regulations. Explainability methods
can play a crucial role in reaching these goals. On the regulatory side, over
the last few years, the EU published a set of directives and drafts for
guidelines that already motivate and will create a strong demand for
explainable AI. On the technical side, deep learning works stochastically and
by example; if there were any unexpected and unwanted correlations (bias) in
the training data, developers need ways to find out about such effects in
order to address them.

Besides surveys, we investigated existing explainability methods applicable on
image-based deep learning models. We developed a state-of-the-art model-
agnostic explainability framework, including efficient optimization algorithms
that speed up many existing model-agnostic methods by drastically reducing
sampling efforts [39, 42]. We released these results together with
implementations for several sampling-based XAI methods as an open-source
library [43]. Model-agnostic methods can be used to generate feature
attribution heatmaps in cases without access to the model (external
evaluation), or the model itself is not differentiable. Another reason can be
the case of introducing new model architectures or transfer functions for
which no reversible approaches exist yet. Model-agnostic methods come at a
higher cost of execution. Still, they are cheaper to adapt to many different
models, so they can help mass-validate vast ranges of different ML models when
evaluating them before use in practical applications.

When several AI applications are run next to each other, consistent heatmap
color gradients across apps should be used to avoid confusing users. To make
the results of explainability methods, such as sensitivity maps, customizable,
we created concepts and a software prototype for a standard of transmitting
explainability information in a resolution and type similar to the original
WSI. A crucial sub-concept in this API proposal [26] was the distinction
between content and its visual representation. As our previous observations
highlighted, there is a need for customizable visualization depending on the
shown information, stain color of the underlying WSI, etc. The second
motivation are standardized user interfaces to enable the customizability
mentioned above and consistency regarding color gradients. To realize this,
our standard transmits only physically meaningful values together with
semantic information required to select the appropriate color scheme to map
to. Once implemented, these concepts must be verified in a usability study.

Finally, the third stakeholders we addressed are those who decide whether an
AI application will be acquired or is safe to use in medical practice, e.g.,
hospital administrators, purchasing managers, and regulators. They typically
require global explanations and information about how well an AI application
performs on all of the patients rather than a single patient. This can also
include information about the data that was being used for training the
underlying models or the data that has been used to evaluate the models.
Typically, this information is collected in model cards and can be shown in a
portal like the prototype we developed in EMPAIA. A reliable and well-
performed validation is the basis for supporting these stakeholders with such
global explanations.

##  4 Regulatory Perspective

Apps in pathology used for diagnostic or therapeutic purposes are subject to
the In Vitro Diagnostic Medical Device Regulation (IVDR) [44] in the EU and
the Code of Federal Regulations [45] in the USA. Meeting these regulatory
requirements is a major challenge when building AI products in pathology.
Additional challenges include software quality, integration in laboratory IT
infrastructure, business models, and reimbursement. To provide software
vendors with advice on how to meet these challenges, we conducted extensive
research and published an open guidance paper [14]. In addition, EMPAIA is
monitoring the pathology AI apps available on the market and their regulatory
approval status in different jurisdictions and provides this directory online
[46].

To demonstrate the practical utility and obtain regulatory approval, AI apps
must be validated appropriately [44]. A major challenge in validating AI apps
is compiling suitable test datasets. These must be sufficiently diverse to
cover the extensive biological and technical variability of WSIs. They must
also be sufficiently large to obtain statistically meaningful performance
estimates. Moreover, test datasets must cover relevant subsets, be unbiased,
and be sufficiently independent of datasets used for development.

In the past, there was little guidance available on how to compile test data
sets, which was a major barrier to adopting AI solutions in laboratory
practice. To change this, we organized the committee “Validation of AI
Solutions,” consisting of more than 40 representatives from different
stakeholder groups of the EMPAIA ecosystem, including pathologists, AI
vendors, pathology software system vendors, and scanner vendors. The committee
met in regular video conferences to discuss and define recommendations for
creating and using test datasets based on literature research and experience.
The results were published as an open guidance paper for AI developers and
pathologists [17].

To ensure that performance estimates are unbiased, AI apps must be tested on
datasets that are independent of their training data and originate from
different clinics and patients. Testing should ideally be conducted by an
independent body with no conflicts of interest. For this reason, the EMPAIA
App Validation Service was initiated in late 2022\. AI vendors can submit
their solutions to this service to have them tested against independent
datasets curated by EMPAIA according to the proposed guidelines. [17]

In a first prototypical phase, the service provides validation of AI apps for
PD-L1 immunohistochemistry (IHC) on WSIs in non-small cell lung cancer
(NSCLC), detecting positive and negative tumor cells and calculating the Tumor
Proportion Score (TPS). The dataset used for this purpose consists of over 200
slides from 22 cases that were stained with eight different antibodies.
Reference TPS were provided by panels of three institutes. All slides were
digitized using five different scanners, resulting in over 1000 WSIs. AI
vendors do not receive the validation dataset itself, as sharing the dataset
would no longer ensure an unbiased performance assessment for future AI
solution submissions. Instead, AI vendors make their solutions compatible with
the open EMPAIA Platform infrastructure [22], so that testing can be carried
out autonomously by the EMPAIA App Validation Service. As a result, vendors
are provided with a report of the specific strengths and shortcomings of their
apps with regard to different tissue regions, stains, and scanner types.

##  5 Communication and Knowledge Transfer

###  5.1 Public Relations

For an ecosystem project, it is crucial to be visible to find additional
partners, in our case app developers, LIS developers, and laboratories, and to
attract the attention of future end-users, i.e., pathologists and decision-
makers. These primary target groups are a diverse audience with backgrounds in
computer science, management, and medicine. Five main channels were used to
disseminate information: website, newsletter, social media, peer-reviewed
publications, and conferences. Project news included events, publications, API
updates, software, documentation releases, and the availability of EMPAIA
Academy material.

Besides communication to the general public, we also disseminated project
results to the scientific community in articles providing a project overview
[13], describing aspects implemented in the EMPAIA platform [18, 36, 47],
giving recommendations to different stakeholders [14, 17, 22, 48], and
presenting other EMPAIA-related research [39, 40, 41, 42, 49, 50, 51, 52].
Furthermore, congresses and events worldwide presented opportunities to
showcase EMPAIA. These events allowed talking to media representatives,
pathologists, and AI experts and to gain visibility and exchange information
about the project and current developments in the field of digital pathology
to establish and intensify networks.

###  5.2 EMPAIA Academy

Activities involving stakeholders to deepen their shared knowledge of digital
pathology were deemed crucial since the initiative intends to link people and
software systems. We have grouped these programs together under the name
“EMPAIA Academy”. We provided medical experts with an understanding of the
basics of data science and AI development. Conversely, we showed AI developers
the challenges of quality diagnostics and the legal considerations involved in
creating advanced software and interfaces for a medical field. The EMPAIA
Academy offered two types of workshops: Lectures by selected invited domain
experts and hands-on sessions at conferences, which provided insight into the
technical activities behind data preparation and training of machine learning
models.

Two rounds of half-day courses, IT/AI for pathologists and pathology for IT/AI
specialists, were delivered as online webinars with free registration and
recordings made available online to facilitate attendance. Basic courses
covered laboratory workflow, pathologists’ diagnostic work, introduction to
WSI scanning, examples of AI applications in pathology, and best practices for
algorithm development (for IT/AI specialists), as well as an introduction to
machine learning, application of such techniques in pathology, and examples of
AI use in different subspecialties (for pathologists). Advanced courses
covered the productization of AI algorithms, an overview of existing products,
and the regulatory and legal challenges of using AI in pathology. The hands-on
sessions were held as pre-ECDP workshops to facilitate attendance with
material made available online afterwards [53, 54, 55, 56, 57]. They consisted
of several parallel tutorials, each covering different facets of computational
pathology. Designed as activities requiring little prior knowledge of
programming or pathology, the tutorials included interactive analysis of WSIs,
automated image processing techniques, approaches for patch-based image
analysis, and AI-based image classification. During the workshops,
collaboration and discussions in small groups led to a lively atmosphere and
networking. These workshops also stood out from the industry pre-conference
workshops in that they provided knowledge to participants with no commercial
interest.

##  6 Discussion

Expectations for AI support in diagnostic pathology are high but are currently
not being met. This is due to technical and regulatory challenges in
translating research results into clinical diagnostic products and the lack of
standardized interfaces between systems, among other reasons. We aimed to
overcome these hurdles by developing standard interfaces for integrating AI
apps into the workflow and by using these apps in routine clinical
diagnostics, by supporting the validation and certification of AI apps, by
fostering the exchange of knowledge between stakeholders (pathologists,
algorithm developers, vendors, scanner vendors, and AI app vendors), and by
improving the explainability of the results.

Starting from a digital pathology landscape with only a few proprietary
interfaces to connect individual pathology workstations or IMS with AI apps
from selected vendors, EMPAIA created the first open and vendor-independent
interface specification. This was subsequently adopted by multiple vendors and
tested in reference centers with encouraging results, providing a proof of
concept of how different AI solutions can be integrated in a standardized way.
The EMPAIA interface specification is being continuously extended to better
integrate AI solutions into the laboratory workflow, e.g., by further
improving DICOM interoperability and supporting different licensing models for
billing (e.g., cloud-based and on-premises, time-based and usage-based).

By collaborating with reference centers, a number of pathologists were able to
familiarize themselves and gain experience with AI. In addition, many
pathologists benefited from the knowledge transfer at the EMPAIA Academy.
Similar to previous introductions of new methods in pathology (e.g., IHC,
fluorescence in situ hybridization, molecular diagnostics), it will be
important to expand training programs for clinical users in cooperation with
pathologists’ committees and organizations.

We have emphasized our position as an open and vendor-independent ecosystem by
making the educational materials freely available and releasing the software
developed for the EMPAIA platform under the industry-friendly MIT open-source
license. As a side-effect, this also allows the reuse of components and the
adoption of interfaces beyond the intended purpose of making commercial AI
apps available for routine pathology. It is conceivable that our developments
could be used to create a platform for automated comparison of algorithms, to
enable side-by-side comparison of apps in terms of quality of results,
computational performance, and user experience, and to provide access to
research apps, e.g., for clinical studies.

Obtaining FDA or IVDR approval for AI apps in DP is a major hurdle, especially
for small vendors. This is sometimes circumvented by marketing apps for
research use only, making it even more important to support the steps
necessary for clinical use. While the first AI applications have been
certified by the FDA or according to the IVDR in recent years, it is still not
very clear what specific requirements vendors must meet. Therefore, we aimed
to remove uncertainties about regulatory processes and, in particular, support
the compilation of test datasets by deriving recommendations from the
information available so far. We expect that the standardization of
interfaces, as provided by EMPAIA, will simplify and accelerate certification.
In addition to recommendations, we have created a first validation dataset
(>1000 WSIs from >200 slides) as the basis for a service where AI vendors can
obtain independent, external validation of their image analysis apps.

###  6.1 Lessons Learned

There was a great deal of interest from the industry, as all market
participants were facing more or less the same problems. EMPAIA succeeded in
opening up a pre-competitive space for interface development. This concept has
already been successfully applied many times, e.g., with DICOM in the medical
domain or the USB interface in consumer electronics. In this process, we
underestimated the persistence with which proprietary approaches are pursued
to achieve market advantages. It took considerable effort, repeatedly
emphasizing that “we are not selling anything,” and demonstrating cooperation
with various vendors before EMPAIA was no longer perceived as a potential
competitor.

Setting up the reference centers also proved to be a significant
administrative burden. Hardware acquisition, whether for a research project or
not, must be processed according to the applicable European, national, and
local tendering rules. This, combined with hardware availability bottlenecks
due to the SARS-CoV-2 pandemic and the efforts of integration in local IT
infrastructure, led to very long delays in establishing the reference centers.
In addition, complex data protection agreements were required. Although no
identifying patient data (including case numbers) was exposed to the image
analysis apps, cloud use was not acceptable to all reference centers.
Conversely, not all vendors provided on-premises implementations of their
solutions. These difficulties are characteristic of the introduction of
digital pathology in connection with AI apps by commercial providers and often
result in stalled or abandoned deployments.

Once installations were established in the reference centers, getting thorough
user feedback proved to be an additional challenge. Diagnosticians are under a
lot of time pressure and are only able and willing to participate in
evaluations, e.g., if the effort is reasonable. Even when communicated
clearly, the fact that the platform, the apps provided, and the workflow
integration are still under development can lead to a negative overall
impression of AI and image analysis support. Combined with delays in
implementation and improvements, this may also reduce enthusiasm for
participating in further evaluations.

###  6.2 Outlook

Despite the technical advances and guidelines developed over the past years,
three major hurdles remain for the widespread adoption of AI in routine
pathology. Obtaining regulatory approval remains a resource-consuming process
for AI vendors. This will also be important to encourage updating products
with the newest technologies, which is particularly relevant in the AI field.
The process can be simplified through better guidance but will continue to
require some effort. Digitization of pathology laboratories is a prerequisite
for applying AI and remains a necessary major up-front investment in equipment
and workflow adaptation by the lab. Enhancing existing guidelines with more
experience and evidence can help to simplify the implementation to some
extent. Reimbursement for using digital or AI-assisted pathology to cover
additional costs, which could exceed what can be saved by more efficient
workflows, will typically need to be decided nationally. Evidence of value and
lobbying work can help make the use of AI financially viable.

Several standardization and regulatory issues require further development and
sustainable engagement beyond the end of the publicly funded project. It is
for this purpose that we have established the non-profit EMPAIA International
Association. Open to all stakeholders in the digital pathology ecosystem, this
association will remain vendor-independent. Its funding will come from
membership fees, participation in publicly funded research projects, and the
provision of services to its members, such as scanner benchmarking, provision
of validation data, and interface implementation support. The association will
focus on solving problems that require the cooperation of different
stakeholders and neutrality towards all stakeholders, a prime example being
the successful implementation of open interfaces for pathology-AI solutions.
Therefore, a key goal of the association will be to further develop the EMPAIA
Interfaces to meet new or expanded requirements of AI solutions and pathology
software systems.

The high number of participants, i.e., pathologists, industry, researchers,
legal and quality assurance associations, etc., in the EMPAIA Academy events,
has demonstrated the demand for continuing education on AI in pathology. The
Academy format, open to all interested parties, will be continued. In
addition, the association will offer open online training programs for using
and developing AI solutions with contributions from leading AI researchers and
companies.

EMPAIA International works closely with a growing number of international
reference centers and the stakeholder ecosystem to advance the use of AI and
ensure the practicality of future developments, in particular by supporting
the commercialization of AI prototypes and the validation of AI solutions.
This way, the association will facilitate the development, marketing, and
establishment of AI solutions in pathology.

## Acknowledgments

We thank the following investigators for their numerous contributions to the
results presented in this paper: Sahin Albayrak, Theodore Evans, Aray Karjauv,
Eric Kolibacz, Carl Orge Retzlaff, Weijia Shao (all at Technische Universität
Berlin, DAI-Labor); Marcus Barann, Horst Karl Hahn, Henning Höfener, Johannes
Lotz, Daniel Romberg, Daniela Schacherer, Ruben Stein, Nick Weiss (all at
Fraunhofer MEVIS); Isil Dogan O, Stefan Manthey, Arend Müller, Karl Roth,
Katharina Veitengruber (all at Charité – Universitätsmedizin Berlin), Andreas
Holzinger, Michaela Kargl, Heimo Müller, Peter Regitnig (all at Medical
University of Graz), Thomas Pilz (at QuIP GmbH), Maren Riemann, Katharina
Wagner (both at vitasystems GmbH).

Funding: This work was supported by the German Federal Ministry for Economic
Affairs and Climate Action via the EMPAIA project (grant numbers 01MK20002A,
01MK20002B, 01MK20002C, 01MK20002E, 01MK20002F) and from the Austrian Research
Promotion Agency under grant agreement No. 879881 (EMPAIA). CG has received
additional funding via the European Union’s Horizon 2020 research and
innovation programme under grant agreement No. 101079183 (BioMedAI). TB has
received additional funding from the German Federal Ministry of Research and
Education in the projects PROSurvival (01KD2213D) and Inter-Agent
(16DHBKI048). PB has received additional funding from the European Research
Council (Consolidator Grant No 101001791), the German Federal Ministry of
Education and Research (STOP-FSGS-01GM2202C) and the Innovation Fund of the
Federal Joint Committee (Transplant.KI, No. 01VSF21048). MP has received
funding via the European Union’s Horizon 2020 research and innovation
programme under grant agreement No. 101079183 (BioMedAI). TK has received
additional funding from the German Federal Ministry of Labour and Social
Affairs (Go-KI project DKI.00.00032.21).

## Conflicts of Interest

SS and FZ are employed by an AI vendor of which one app was included in the
evaluation. All other authors declare that they have no conflict of interest.

## Author Contributions

Conceptualization: NZ, TRK, PH; Methodology: NZ, LOS, TK, KW, TB, PB, RC, MF,
CJ, TRK, BL, NCP, KS, FZ, MP, CG, AH, PH; Software: TB, CJ ,MF, BL, KS, TK;
Investigation: LOS, SS, FZ; Writing – Original Draft: NZ, LOS, TK, KW, TB, RC,
MF, CJ, TRK, BL, NCP, KS, MP, CG, AH, PH; Writing – Review & Editing: NZ, LOS,
TK, KW, TB, PB, RC, MF, CJ, TRK, BL, NCP, SS, KS, FZ, MP, MT, CG, AH, PH;
Visualization: LOS, CJ, MP, CG; Project administration: PH, NZ, AH, CG, KW,
MT; Funding acquisition: PH, NZ, TRK, AH, CG, KW. All authors read and
approved the final version of the paper.

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