The  (IRAP) and the  (IPIC) have partnered to offer the  program for Canadian small and medium-sized enterprises (“SMEs”). IPilogue readers may have seen ’s&�Բ�����; of another CIC program, , which provides funding for a similar purpose through a different government channel. That article outlined the motivation behind these types of programs and summed up that  Canadian SMEs often lack access to the means to protect intellectual property (IP) and highlighted a clear economic need for innovative Canadian businesses to improve their IP commercialization.

NRC IRAP, CIC, and IPIC

The NRC IRAP provides a range of innovation support services for Canadian SMEs. The program offers funding, advisory services, and networking opportunities to help SMEs undertake research and development (“R&D”) and to commercialize, and improve their competitiveness in domestic and global markets. IRAP also provides support for technology adoption, productivity improvement, and business expansion. On February 16, 2023, the Government of Canada announced that NRC IRAP will be integrated into the  (CIC).

The CIC will be a new, operationally independent organization solely dedicated to supporting business R&D across all regions and all sectors of the economy. It is a federal initiative that will be  that aims to “play an important role in building a stronger and more innovative Canadian economy for generations to come.” The CIC will include an umbrella of programs, including both IP Assist and ElevateIP, to support the development and exploitation of IP.

IPIC is Canada’s professional association of patent agents, trademark agents and lawyers practicing in all areas of intellectual property (“IP”) law and is comprised of over 1700 members.  is to match SMEs with IPIC members who practice in their specific industry. The IP professional will help SMEs better understand the key aspects of IP and how it can support their business goals.

The IP Assist Program

There are three levels to the IP Assist Program — levels 1, 2 and 3 (L1, L2, L3, respectively). Each level brings :L1 – up to $1k, L2 – up to $20k, L3 – up to $20k+), as well as increasing engagement with an IP professional matched to the SME:

The L1 IP Awareness is a one-to-one IP awareness session during which an IP professional will provide industry-specific IP information and guidance to an SME. Engagement at L1 provides IP professionals with an opportunity to connect, support and guide innovative Canadian SME to help them achieve their business goals. Engagements with SMEs will take, on average, up to 3 hours and include an IP awareness presentation followed by Q&As.

The L2 IP Strategy relates to the IRAP SME’s specific technology space, aligns with the IRAP SMEs business objectives, and provides IRAP SMEs with specific prioritized IP actions. The IP Strategy must be informed by key relevant information relating to the technology and competitor landscapes relevant to the IRAP SMEs.

The L3 IP Implementation relates to detailed IP asset assessments, such as IP audits, trademark clearance searches, prior art searches and analysis, advice on branding strategy, legal analysis of IP landscaping, patentability analysis, licensing strategy formulation, and other activities. However, some patent and trademark preparation services and filing fees may not be covered.

Conclusion

Canada’s investment in the CIC indicates that there is an increased focus on innovation as a driver of economic growth. There is also a clear aim through programs like IP Assist and ElevateIP to ensure that IP generated by innovative SMEs in Canada are carefully strategized for and well-protected. Hopefully, this increases Canadian presence in innovation and brings greater investment in R&D into Canada.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School

BetterHelp is a mental health platform that provides online mental health services, “the largest therapy platform in the world. We change the way people approach their mental health and help them tackle life’s challenges by providing accessible and affordable care. With BetterHelp, you can message a professional therapist anytime, anywhere”. reads: “Making professional therapy accessible, affordable, and convenient — so anyone who struggles with life's challenges can get help, anytime and anywhere”. Their primary business is online counseling and therapy provided online through web-based interaction and phone/text communication with professional counselors.

Privacy Misrepresentation

According to the , BetterHelp requires a questionnaire that asks for sensitive mental health information – “such as whether they have experienced depression or suicidal thoughts and are on any medications” – along with personal information. details BetterHelp’s dubious privacy practices, many of which display an egregious lack of concern for privacy interests. The complaint also details the privacy representations made by BetterHelp, some of which have been altered over time. An example of these changes was seen in the intake questionnaire, where a question asking “Are you currently taking any medication?” included a privacy statement that went through a few iterations (emphasis on alteration added in the complaint):

Up to Dec 2020: “Rest assured—any information provided in this questionnaire will stay private between you and your counselor.”

Dec 2020: “Rest assured—this information will stay private between you and your counselor”

Jan 2021: “Rest assured—your health information will stay private between you and your counselor”

Oct 2021: The statement was removed altogether

Revealing Private Information to Advertisers

The FTC release indicates that BetterHelp “did not obtain consumers’ affirmative express consent before disclosing their health data” and “failed to place any limits on how third parties could use consumers’ health information—allowing Facebook and other third parties to use that information for their own internal purposes, including for research and development or to improve advertising”. According to the complaint, BetterHelp used and revealed consumers’ email addresses, IP addresses, and health questionnaire information to Facebook, Snapchat, Criteo, and Pinterest for advertising purposes”, including “identify[ing] similar consumers and target[ing] them with advertisements for BetterHelp’s counseling service.”

The Punishment

The FTC has issued a (a legal document that outlines the terms and conditions for resolving a complaint or an investigation related to unfair or deceptive business practices) requiring that BetterHelp return funds – amounting to $7.8 million – to customers whose health data was compromised. ��The proposed order also bans BetterHelp from disclosing health information for advertising, prohibits misrepresenting its sharing practices and requires several changes to company practices regarding health and personal data. BetterHelp writes in that this settlement is “no admission of wrongdoing” and that their “industry-standard practice is routinely used by some of the largest health providers, health systems, and healthcare brands”. The says that this enforcement action is not the first of its kind, as it follows the , and that “the FTC has made it clear of its intent to crack down on the trafficking in sensitive health data by businesses not strictly classified as health care providers and thus not covered by HIPAA, the federal privacy rules that govern the health care industry”. Hopefully, this sets a precedent for more stringent enforcement of good privacy practices, particularly regarding the sale of personal and health information.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

The Institute of Electrical and Electronics Engineers (IEEE), a professional organization for engineers and technology experts, recently announced the launch of the IEEE GET Program, aimed at providing free access to AI ethics and governance standards. The program is part of IEEE's ongoing efforts to promote responsible AI practices and help organizations develop and implement ethical AI systems.

The opened seven standards for public access:

1. Age-Appropriate Digital Services
2. Addressing Ethical Concerns during System Design
3. Transparency of Autonomous Systems
4. Data Privacy
5. Transparent Employer Data Governance
6. Ethically Driven Robotics and Automation Systems
7. Assessing the Impact of Autonomous and Intelligent Systems (“A/IS”) on Human Well-Being

Assessing the Impact of A/IS

The most cited of the standards is the , which addressed the growing concern of how autonomous and intelligent systems may affect society. This standard provides a structured approach to evaluating the impact of A/IS on individuals, communities, and society, and helps organizations ensure that their systems are developed and deployed in a manner that supports human well-being. Recommended practices aim to bring an increased awareness about well-being concepts and indicators for A/IS and an increased capacity to monitor, evaluate, and address the well-being impacts of A/IS. Successful application of the standard includes implementing the ability to evaluate the ongoing well-being impact of A/IS on users and stakeholders while continuing to improve the system to safeguard human well-being, resulting in a greater ability to avoid unintentional harm.

The Standard also suggested numerous domains of well-being and accompanying indicators that system designers should be concerned with. These domains pertain to individual well-being —satisfaction with life, affect/feelings, and psychological well-being, social well-being —community, culture, education, economy, health, and work, and regulatory domains —environment, government, and human settlements. The Standard noted that these suggestions are a starting point for selecting indicators and that “indicators should be adapted to fit the circumstances of measuring and gathering data about the well-being impacts for an A/IS on user(s).”

Ethics and Systems Design

The is also frequently cited. This Standard provides a set of guidelines and best practices for organizations that engage in system and software engineering to make value-based ethical system design and investment decisions.

A number of interesting points to consider are included within the Standard Model Process. The standard provides guidance to organizations on establishing key roles in Ethical Value Engineering Project teams. These teams are then tasked with defining how a system is expected to operate from the users’ perspective Concept of Operations — identifying stakeholders and determining the context of use and potential for ethical benefit or harm (Context Exploration). There is also an Ethical Values Elicitation and Prioritization Process, which aims to obtain and rank values and value demonstrators, followed by an Ethical Requirements Definition Process that guides the defining of value-based system requirements to reflect the prioritized core values and their value demonstrators. Finally, the Standard also sets out an Ethical Risk-Based Design Process and a Transparency Management Process, guiding the realization of ethical values and required functionality in designing a system and how to inform stakeholders of the system’s implementation of ethics.

Impact on AI

that these IEEE standards are already being incorporated into AI governance. For instance, the European Union's Artificial Intelligence Act (“EU AI Act”) references many of the components that the IEEE makes available in this package. This will likely continue to be relevant both for regulators and AI developers —“TÜV SÜD sees a strategic advantage for those looking to demonstrate eventual compliance to human-centric regulatory measures or market pressures to leverage these IEEE standards and certifications.” Developing ethical AI systems is a multifaceted problem which requires extensive deliberation by organizations involved with AI systems development. The release of free standards by an authoritative governing body will likely immensely benefit everyone involved.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

One contentious point from the session was synthetic data’s potential to solve the privacy concerns surrounding the datasets needed to train AI algorithms. In light of its increasing popularity, I will explore the benefits and dangers of this potential solution.

Concept

The data privacy concern that synthetic data aims to address is very similar to the purpose of — protecting anonymized data from being de-identified without reducing data utility. This is distinct from data augmentation, which is the process of adding new data to an existing real-world dataset in order to provide more training data, and could include rotating images or combining two images to create a new one. Data augmentation is typically not useful in the privacy context.

In a , the Office of the Privacy Commissioner of Canada (“OPC”) describes synthetic data as “fake data produced by an algorithm whose goal is to retain the same statistical properties as some real data, but with no one-to-one mapping between records in the synthetic data and the real data.” Synthetic data consists of real-world source data that is put through a generative statistical model, which is evaluated for statistical similarity to the source alongside privacy metrics. Critically, there is no need to remove quasi-identifying data, that is, data vulnerable to de-anonymization. This results in more complete datasets.

Benefits

Synthetic data uses a highly automated process to provide protection from de-identification using a highly automated process. This results in datasets that can be readily shared between AI developers without the dangers of privacy concerns. also points out that there are substantial cost savings. The points to how a synthetic data service company founder estimated that “a single image that could cost $6 from a labeling service can be artificially generated for six cents.” Synthetic data can also be manufactured to reduce bias by deliberately including a wide variety of rare but crucial edge-cases. Nvidia uses machine vision for autonomous vehicles as their example, but I think this concept should translate to improving representation of marginalized and under-represented groups in large datasets in healthcare or facial recognition. Many of the Bracing for Impact panelists shared this concern.

Dangers

The OPC notes in their blog many issues and concerns, particularly regarding de-identification. This is especially true if the synthetic data is not generated with sufficient care and if the “generative model learns the statistical properties of the source data too closely or too exactly”. In other words, if it “overfits” the data, then the synthetic data will simply replicate the source data, making re-identification easy.” Moreover, there is also concern with membership inference, where the fact that some individual data exists is an inherent risk. A also demonstrated that “synthetic data does not provide a better tradeoff between privacy and utility than traditional anonymization techniques” and “the privacy-utility tradeoff of synthetic data publishing is hard to predict.” This indicates that the characterization of synthetic data as a “silver bullet” is likely overselling its capabilities. ��

Implementations

Nvidia is using synthetic data in computer vision, but its primary purpose is not privacy — that there are other important functions for the technology. is a leading platform for synthetic data in healthcare and is . It is only beginning: it is predicted that “.”

Conclusion

Synthetic data has the potential to be highly beneficial, as it may be the answer to the many challenges AI developers face in sharing sensitive data. However, like many developments in AI technology, it requires caution and careful implementation to be effective and is potentially dangerous if relied upon haphazardly.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

The (NIST) has been tasked with promoting “U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology.” On January 26, 2023, NIST released their alongside a suggesting ways to use the AI RMF to “incorporate trustworthiness considerations in the design, development, deployment, and use of AI systems”. Both the framework and playbook are intended to help organizations understand and manage the potential risks and benefits of AI. The framework is also meant to ensure that AI systems are developed, deployed, and used in a responsible and trustworthy manner. The framework is intended to be a flexible and adaptable tool that can be applied to a wide range of AI systems, including those used in various industries such as healthcare, finance, and transportation.

NIST describes a trustworthy AI to have a set of characteristics: valid and reliable, safe, secure, and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair – with harmful bias managed.

Valid and reliable: Produces accurate and consistent results. Its performance should be evaluated and validated through ongoing testing and experimentation, with risk management prioritizing the minimization of potential negative impacts.

Safe: Does not cause harm to people or the environment and should be designed, developed, and deployed responsibly with clear information for responsible use of the system

Secure and resilient: Maintains confidentiality, integrity, and availability through protection against common security such as data poisoning, and the exfiltration of ��other intellectual property through AI system endpoints.

Accountable and transparent: Provides appropriate levels of information to AI actors to allow for transparency and accountability of its decisions and actions.

Explainable and interpretable: representing the underlying AI systems’ operation and the meaning of its output in the context of its designed functional purposes. Explainable and interpretable AI systems offer information that will help end users understand their purposes and potential impact.

Privacy-enhanced: Protects the privacy of individuals and organizations in compliance with relevant laws and regulations.

Fair – with harmful bias managed: ��NIST has identified three major categories of AI bias to be considered and managed: systemic (broad and ever-present societal bias), computational and statistical (typically due to non-representative samples), and human-cognitive (perceptions of AI system information in deciding or filling in missing information).

AI RMF’s core is organized around four specific functions to help organizations address the risks of AI systems in practice: Govern, Map, Measure, and Manage.

Govern: This includes establishing policies, procedures, and standards for AI systems, key decision-makers, developers, and end-users.

Map: AI RMF is intended to contextualize and frame risks by identifying the system's components, data sources, and external dependencies, as well as to understand how the system is used and by whom.

Measure: AI RMF evaluates the potential risks and benefits of the AI system by assessing the system's vulnerabilities and potential social impacts.

Manage: AI RMF allocates risk resources to mitigate identified risks and continuously monitor the system and its environment by establishing monitoring processes and procedures to detect and respond to incidents, as well as updating controls as needed.

NIST’s AI risk management framework is a voluntary but very important prompt for organizations and teams who design, develop, and deploy AI to think more critically about their responsibilities to the public. Understanding and managing the risks of AI systems will help to enhance trustworthiness, and in turn, cultivate public trust in AI – a critical part in AI adoption and advancement.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

The AI revolution has brought about significant concerns about the privacy of big data. Thankfully, over the past decade, big tech has found a solution to this problem: differential privacy, which actors have . The technology is not limited to big tech anymore either; the . Furthermore, the European Union is – indicating that policymakers are on board with differential privacy as a standard means of protecting large, tabulated datasets.

What problem does differential privacy aim to solve?

Differential privacy was created to combat the , which states that “overly accurate answers to too many questions will destroy privacy in a spectacular way.” For instance, in a striking example,

�� showed that gender, date of birth, and zip code are sufficient to uniquely identify the vast majority of Americans. By linking these attributes in a supposedly anonymized healthcare database to public voter records, she was able to identify the individual health record of the Governor of Massachusetts.

, which at the time contained anonymous movie ratings of 500,000 Netflix subscribers. The attacker compared this to the Internet Movie Database (IMDb) and ��successfully identified the Netflix records of known users, uncovering information such as their apparent political preferences.

How does one defend against such an attack?

De-anonymization attacks follow the principle that overly accurate answers to too many questions will destroy privacy. Defending a database against too many questions is impractical, thus there must be a method to make answers inaccurate without affecting the data’s utility. Per , this method is achieved by introducing “statistical noise”. The noise () is significant enough to protect the individual’s privacy, but small enough that it will not impact the extracted answers’ accuracy.

Why is this relevant to law?

protects an individual’s information by presenting the impression that their information were not used in the analysis at all, which is more likely to comply with legal requirements for privacy protection. Differential privacy also masks individual contributions to ensure that using an individual’s data will not reveal any personally identifiable information, making it impossible to infer any information specific to an individual.

raised (and voluntarily dismissed) legal arguments against differential privacy by alleging that “the defendants’ decision to produce “manipulated” census data to the states for redistricting would result in the delivery of inaccurate data for geographic regions beyond the state's total population in violation of the Census Act”. As the plaintiff voluntarily dismissed the case, we will need to wait to see if this argument is successful in the future. However, it is obvious that the courts find the addition of statistical noise to violate the data’s integrity, which would be a serious problem for differential privacy.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

Photo by Buda Photography

On November 9, IP Osgoode, Reichman University and Microsoft hosted the first in-person Bracing for Impact Conference since 2019. The conference focused on “The Future of AI for Society.” While AI is full of exciting possibilities, real-world application and integration are relatively nascent. Implementing AI technology in society requires complex interdisciplinary engagement between engineers, social scientists, application area experts, policymakers, users, and impacted communities. At the conference, an esteemed lineup of speakers across disciplines discussed the forms that interdisciplinary collaboration could take and how AI can help shape a more just, equitable, healthy, and sustainable future.

After their panel discussion, the AI for the Future of Health session held a spirited question & answer period. Attendees and panelists discussed several interesting ideas about advancing AI in healthcare.

Government’s role in providing access to high-quality data

Dr. Gaon argued that government involvement is key for creating necessary infrastructure for facilitating data access, specifically in gathering the relevant solution-implementing groups. Ms. Bawa responded with concern that state-managed data collection would introduce bias, as the segment of people who interact with government poorly represents the general population. Ms. Dykeman added that a robust regulatory framework may be too slow, and that we need guidance in the meantime so as to not impede AI development.

Synthetic data as a workaround for privacy issues

As a potential means for government to provide easy access to data, Dr. Gaon proposed using to facilitate access to data and solve privacy problems, citing a government-led project in Israel. Dr. Singh pointed out that synthetic data is typically used when there is a lack of data, such as for rare diseases. He claimed that using synthetic data when real data is available is a crutch for not figuring out solutions to the problems that were discussed throughout the panel.

Will AI make healthcare less expensive?

Dr. Singh expressed optimism for AI to simultaneously improve costs, time efficiency and care. As software is cheap relative to human labor, AI could achieve all three of these improvements at once. He pointed out that costs still arise from developing and maintaining ��models and, speaking from his experience trying to allocate salaried time for testing AI solutions at Sick Kids, they are difficult to account for.

Handling IP to appropriately incentivize collaboration

Dr. Singh, wearing both clinician and developer hats, expressed concern for patient data IP potentially being moved inappropriately. He identified a viable solution to this problem: ensuring that hospitals own the IP while the developer owns only the AI delivery mechanism. This would specifically prevent IP from being exported to third parties through investors. Ms. Pio, as a platform advisor from Microsoft, endorsed this solution as ownership of the IP also comes with difficult questions about transparency, bias, and uses. She also reminded us that many of these AI solutions could be used by a multitude of institutions and thus it is prudent to protect the data from being a part of the product being sold.

Transition from research to clinic

Dr. Singh pointed out 3 issues with translating work from Toronto research hospitals to smaller local hospitals: how to get access to the data without violating privacy, how will it be funded, and how will it be regulated. Ms. Dykeman expressed concerns about how much of the development of AI for health is under the research designation, and the challenges that that may introduce down the road.

From the audience, Prof. David Vaver put forth concerns about IP ownership. He proposed a model where patients license their data to the hospital, rather than assigning ownership to the hospital directly. Dr. Singh acknowledged the philosophical correctness of this model but points out the difficulties of implementation – besides technical limitations, he also worried about biases from patients removing their data from the pool.

Conclusion

The panelists shared optimism for AI in the future – and Ms. Pio indicates that many institutions are on board with this optimism as well. There are still many problems that need answering and frameworks to be built – both in terms of building appropriate regulation and the necessary multidisciplinary culture – but the process has started and will only continue forward.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

Photo by Buda Photography

On November 9, IP Osgoode, Reichman University and Microsoft hosted the first in-person Bracing for Impact Conference since 2019. The conference focused on “The Future of AI for Society.” While AI is full of exciting possibilities, real-world application and integration are relatively nascent. Implementing AI technology in society requires complex interdisciplinary engagement between engineers, social scientists, application area experts, policymakers, users, and impacted communities. At the conference, an esteemed lineup of speakers across disciplines discussed the forms that interdisciplinary collaboration could take and how AI can help shape a more just, equitable, healthy, and sustainable future.

IP Osgoode’s recent Bracing for Impact Conference: The Future of AI for Society 2022 included the third panel discussion predicted AI’s impact on healthcare. In his first appearance in his new role, Mr. Konstantinos Georgaras (CEO, Commissioner of Patents & Registrar of Trademarks, Canadian Intellectual Property Office) chaired the session and opened by asking the panelists two questions:

1. What has been the biggest success in healthcare AI over the past few years?
2. What is the biggest barrier we still need to overcome if we are to see real, successful progress in the space?

Dr. Devin Singh - Staff Physician & Lead, Clinical AI & Machine Learning, Pediatric Emergency Medicine, Hospital for Sick Children

Dr. Singh identified an obvious use for AI – leveraging predictive analytics for common patient test orders during waiting time to drive efficiency. Dr. Singh is optimistic about the future of this type of innovation as electronic health records become widespread, creating the data that fuels AI innovations. However, he points out a few issues surrounding AI. First, confusion surrounds the regulatory landscape, based on his experience implementing adaptive models which Health Canada does not yet regulate. Second, equitability requires tackling bias in the data that feeds into these AI models. Specifically, there is a lack of data that ensures that a model is equitable regardless of ethnicity, race, religion, or culture. Finally, the advent of AI-powered innovations may widen healthcare gaps caused by inconsistent internet access across Canada.

Dr. Aviv Gaon, Senior Lecturer, Harry Radzyner Law School, Reichman University

Dr. Gaon focused his discussion on the legal difficulties of getting access to high-quality data, primarily concerning copyright and privacy. The copyright issue is that we all own our data – thus, Dr. Gaon proposed that data needs to be governed through a different mechanism, arguing that lowering copyright standards is key to AI development. Dr. Gaon also argued that privacy, as with any other right, has a price which must be acknowledged. He advocates for these costs to be considered in policy decisions, as they may stifle AI development.

Ms. Mary Jane Dykeman, Co-Founder & Managing Partner, INQ Law

Ms. Dykeman’s discussion centers around two main questions: what data do we have? And what can be done to ensure that data privacy is not a barrier to AI?

Ms. Dykeman wondered if we really know what data we hold and what we can do to clean up the data and make it useful. She encouraged AI developers to consider the specific use case as unfocused data problems are difficult to manage. Ms. Dykeman advocated for a need to develop a legal framework that acts as guardrails to AI, keeping it on track rather than hindering it.

Ms. Naseem Bawa, Counsel, Norton Rose Fulbright LLP

Ms. Bawa drew from her experience developing AI for the mental health space before her current role as counsel at Norton Rose Fulbright. Ms. Bawa is primarily concerned with bias in implementing AI models and the data that feeds them. She uses data gained from wearable devices as an example to ask pertinent questions – does the data collected from a specific wearable device have some bias? Now that wearable devices are widespread, can we rely on the data? Ms. Bawa concluded by advocating for multidisciplinary teams coupled with appropriate regulation and training concerning biases in the development of AI.

Ms. Laura Pio, Azure Data & AI Solution Specialist, Public Sector & Healthcare, Microsoft

Ms. Pio raised an important concern regarding data standardization: our healthcare system is highly fragmented by jurisdiction and by specialty (i.e., CAMH, hospitals, and long-term care are systematically separate). However, Ms. Pio is optimistic that all of these institutions will collaborate to improve healthcare through AI. Based on her experience implementing AI solutions, Ms. Pio also asked some important and necessary questions regarding AI in healthcare:����������

• Is it ethical to collect health data?
• Do you need consent for the use of lifesaving AI?
• Who can use the data, and how can they use it?
• Is de-identifying health records sufficient?
• Should researchers be able to use the data?

Overall, the panel demonstrated a need for clarity in regulation and improvements in data quality and availability. The panelists echoed concerns about the effect on equitability of data biases and issues surrounding privacy. However, they shared optimism for realizing AI’s potential to radically improve healthcare in the near future.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

This past summer, I had the privilege, as my final act as a graduate student, to attend a major magnetic resonance imaging (MRI) conference in London, UK (ISMRM). At this conference, GE Healthcare used its plenary session to . The other major MRI manufacturers, and , also have AI suites. Computed tomography (CT) had joined the AI party even earlier than MRI, with , , and products. The widespread adoption of AI in medical imaging products is significant because it is one of the first commercial applications of AI in healthcare.

What are MRI and CT?

MRI and CT are the workhorses of most hospitals’ radiology departments. CT and MRI both allow for a 3D image to be taken of internal anatomy, making them invaluable for diagnosing many diseases. Unfortunately, they both have at least one critical downside. CT is an extension of x-ray and thus exposes patients to ionizing radiation, with a CT image often depositing more than 10x the effective radiation dose of an x-ray image. MRI is lauded for, among other benefits, avoiding this radiation; however, MRI is both expensive to run and comparatively very time-consuming.

How does AI come into play?

The primary goal of AI in MRI and CT applications is mitigating the downsides – radiation dose in CT, and scan time in MRI. In both cases, this goal is achieved by “training” an AI through machine learning – or, more specifically, deep learning algorithms – by feeding it an enormous amount of data consisting of previously acquired images. Trained AI allows MRI and CT to acquire less data as the AI is used to fill in the data shortfall – almost analogous to the Hollywood idea of zooming in on a pixelated picture and seeing a clear image. Acquiring less data means less views in CT, leading to less radiation dose and shorter MRI scan times. The resulting AI-enhanced images are used for diagnostic purposes in the same way that conventionally acquired images are.

Why does it matter?

Directly related to healthcare, Canadian , and any improvements to MRI and CT will aid in alleviating that pileup to some extent. It is also significant that radiologists and medical physicists approve of AI in diagnostic imaging. There may not be any group in the medical field more qualified to have at least some grasp of the (disclaimer: I do not fully understand the title of this thesis). It also represents one of the first applications of AI that directly affects medical decisions, which may open the door for other AI applications in healthcare. Lastly, using AI in a commercially available product is interesting on its own – the pathway toward deploying AI in such a high-stakes application may be a useful example for future AI-based products.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School,

Many of you are likely familiar with 3D printing. The process is as follows:

1. Add a digital model of a 3D model to a slicer;
2. The slicer “slices” the model and generates toolpaths layer by layer;
3. Upload the toolpaths to the 3D printer; and
4. Start the machine and wait!

Within hours, you will have a physical rendition of the object you modelled. During the early days of the pandemic, supply issues forced society to adopt any available means to source medical supplies. 3D-printing was able to fill some of the supply gaps faced in medicine, in an important but limited capacity. ��It began with simpler things like ��face shield holders, but over time, the 3D-printing community expanded its applications to things like , , and even a whole .

A mounting concern, however, was that the ability for anyone to download a physical 3D object may pose problems for IP rights. While the pandemic made it , it was important to consider possible avenues to ensure that IP does not hinder emergency response.

I will summarize here two published views on existing solutions to this IP problem: in a May 2022 article in The Journal of World Intellectual Property, explored exceptions that potentially allow IP to be freely used in emergencies: the right to repair exception, the private and noncommercial use exception, and the experimental use exception, while in a in International Review of Intellectual Property and Competition Law (IIC), Ballardini et al. examined two existing ways to tackle the problem: compulsory licensing and voluntary licensing.

Compulsory licensing is the straightforward authorization of licensing by force outlined in the World Trade Organization’s (WTO) Agreement on Trade-Related Aspects of Intellectual Property Rights . It is regarded, as it should be, as a last resort — administrative burdens make this route largely unviable as a fast way to enable and deploy an emergency response. Voluntary licensing, then, should be ��the best existing means of promoting patented ideas in an emergency. Ballardini et al. looks specifically at IP pools and IP pledges as successful means in the response to the pandemic. Two very important examples of IP pools include the and the , which includes 3D-printing repositories. An IP pledge is effectively a patent holder announcing a limited-time public license of their patent. A prominent example of this is the , where many large technology companies have pledged “to make our intellectual property available free of charge for use in ending the COVID-19 pandemic and minimizing the impact of the disease.” While IP pledges are powerful tools, it is still limited by patent holder goodwill. This is a problem because pledges are conditional and limited in scope, leading to confusion and uncertainty.

The right to repair exception is the right of an owner of a patented article to make repairs to preserve its useful life. Abbas argues that “A more robust and explicit right to repair exemption needs to be incorporated in patent law in response to the COVID-19 health emergency… This clear exemption is important so that consumers of medical devices and 3D maker communities can confidently engage in humanitarian efforts to repair critical life-saving medical equipment without risking patent infringement.”

The private and noncommercial use exception varies and is generally intended to prevent a patentee’s rights from restricting non-commercial activities that don’t conflict with the legitimate interests of the rights holder. Abbas also asked WTO Member-States to “adopt a reasonably broad noncommercial use exception to make it practically significant,” so as to allow medical device users to repair their devices without undue concern for IP problems.

The experimental use exception exists to support the advance of science and technology. Unfortunately, in Madey v. Duke, the Court ��limited this exception’s scope significantly. Furthermore, as a common-law rule, experimental use is not defined by international agreement. Abbas calls for WTO members to clarify the experimental use defense and extend coverage to repairs, arguing that “facilitating increased experimental and repair activity by creating a safe harbor for experimentation with medical devices will better prepare countries to deal with a future pandemic.”

The pandemic has shown possible benefits to relaxing patent enforcement in emergency situations. It is thus imperative to explore how the IP system can avoid standing in the way of saving lives. The ideas outlined by Ballardini et al. and Abbas are a start – hopefully more ideas will be expressed and adopted going forward.

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