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.

The post Synthetic Data: The Next Solution for Data Privacy? appeared first on IPOsgoode.

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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Photo by Pixabay ()

Jasmine Yu is an��IPilogue��Writer and a��1L JD Candidate at the University of Toronto.

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After numerous delays over two years, amendments to the Patented Medicines Regulations, which governs Canada’s Patented Medicine Prices Review Board (), is finally set to come into force this year, on — at least for now.

Who is the Patented Medicine Prices Review Board?

The PMPRB is a Canadian that regulates the prices of patented pharmaceuticals to avoid excessive pharmaceutical prices. The board’s objective is to balance encouraging pharmaceutical research and development (R&D) investment with the public health interest of reducing pharmaceutical prices for consumers.

The PMPRB derives its legal authority partly from the , which specifies the information and documents patentees must provide to the Board, price requirements for drugs sold in Canada, and more.

Why Amend?

The PMPRB saw it fit to introduce changes to its legal framework because it was under the current scheme — Canadian patented drug prices were the third highest globally, while pharmaceutical R&D investment has declined.

The Amendments

The PMPRB first published the amendments on . The first substantive revision to the Regulations since the Board’s establishment in 1987, it will bring major changes to the PMPRB’s current legal framework.

There are :

First, the amendments added three new price regulatory factors to consider whether a patented drug’s price is excessive.

Second, the amendments updated the list of comparator countries, whose drug prices serve as a “benchmark” to establish the range of prices that the pharmaceutical companies find acceptable for their patented medicines. According to the PMPRB, the updated countries have similar consumer protection policies, economic wealth, and marketed medications as Canada. The United States, for example, was removed for having vastly different consumer protection priorities — its drug prices are 247% higher than Canada’s.

Third, there were changes in patentees’ pricing and sales reporting requirements. For instance, the reporting requirement was reduced for medicines at a low risk of excessive pricing, such as veterinary drugs and generics.��

The amendments, once in effect, will apply to all drugs issued a drug identification number (DIN) on or after the amendments’ publication — August 21, 2019. Those that received a DIN before this date must still comply with certain other provisions.

A double-edged sword?

The PMPRB views that these amendments will have a and decrease total spending on patented medicines by 5.8% over the next 10 years. These changes are perhaps much needed, as Canada is the only developed country in the world with universal healthcare that . Meanwhile, Canadians pay the highest price for generic drugs in the world and the second-highest prices per capita for prescription drugs — after the US. en Canadians cannot afford their prescription drugs.��

However, there are also opposing voices. Lobby groups, such as , argue that these amendments will have significant negative impacts R&D investment in Canada and on the number of new drugs available to Canadians and on R&D investment in Canada.

IMC and several Canadian pharmaceutical companies brought a judicial review application to challenge numerous provisions. The Federal Court of Canada, in Innovative Medicines Canada v Canada, , struck down subsection 3(4) of the amendments as it was ultra vires the . In a separate case, the Quebec Superior Court declared section 4(4) invalid and unconstitutional.[1]

A group of physicians also voiced their opposition in an . Their arguments mirror that of IMC: the proposed regulations will make Canada an unattractive market for companies launching new drugs, resulting in medications that could alleviate suffering being denied access to Canadians.

Conclusion

Much like how the original intentions of the PMPRB did not actualize into tangible results, and hindsight revealed flaws in its original policy, the actual consequences of these new amendments may not surface immediately. Canadian Pharmacare policy certainly seems to require an iterative process, as numerous stakeholders and market factors are at play. We perhaps need to examine the faults in Canada’s current patchwork system of private and public insurance plans for potential solutions — to strike a balance between affordable medicine and a vibrant R&D environment.

[1] Merck et al, c Le Procureur Général du Canada,��Québec Superior Court File No. 500-17-109270-192.

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Mitacs designed the BSI program for students to find employment this summer and to help participating partner organizations to restore or modify their business operations in response to the COVID-19 pandemic. This is the first year that Mitacs will run this program. Our students will be able to further their legal knowledge beyond the classroom through practical work on real world projects.

Here is some more information about each project:

3D Bridge Patenting and Commercialization Research

has developed a secure digital medication platform through which health practitioners and other authorized care providers can connect with patients remotely. The device will help to ensure that the medication is properly administered to patients, specifically the elderly, patients with comorbidities, and those who take addictive or high-cost medication. Krystel Ametepeh, 1L student and IP Innovation Clinic Fellow, will work under the supervision of Karima Bawa, CEO and in-house lawyer of 3D Bridge, to provide legal research services in intellectual property, health and privacy law and to address the legal requirements associated with commercialization amidst this product’s pre-commercial rollout.

Innovative Educational and Marketing Tools for Prospective IP Rights Owners (Bereskin & Parr)

(B&P) has struggled to market their services and educate clients without their usual in-person events. With funding from Mitacs, B&P will hire Bonnie Hassanzadeh, 2L student and IP Innovation Clinic Fellow, to create educational and marketing tools for various purposes, including training for new staff and educating prospective and existing clients. Under the supervision of B&P Partner Reshika Dhir, Bonnie will also research marketing strategies about tailoring these materials to various clientele and determine the most effective media to convey the relevant information. Some of these materials will be available for use by our IP Innovation Clinic.

Interaxon Scaling Project

is a Toronto-based company that helps customers to build rewarding meditation and sleep practices to lead healthier and happier lives. As the COVID-19 pandemic has increased their need to address mental health concerns, Interaxon is looking to increase the commercialization of its products and services. The funding will allow Androu Waheeb, 1L student and IP Innovation Clinic Fellow, to work alongside Naseem Bawa, General Counsel at Interaxon, in developing licensing arrangements for the company’s relevant IP and seeking FDA approval to make Interaxon’s devices more accessible to users.

We are very grateful to Mitacs for approving IP Osgoode's proposals in this inaugural year of their Business Strategy Internship program. We would also like to thank our supervisors at 3D Bridge, Bereskin & Parr, and Interaxon for working hard on such short notice to make space for our students and put our proposals together. We look forward to seeing how our students grow and what they accomplish with our partner organizations.

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Q1 Do you believe that it is important to have more women involved in the IP system?

Most definitely! As businesswomen we are equal contributors to economic growth, and it is critical that we understand the importance of protecting our ideas, brands, and status in the world of business.

Q2 Have you noticed a gender gap in your industry? Is the situation changing?

In terms of IP systems there is definitely a gender-gap. It is clear that the technology and innovation sector is highly male dominated. However, I believe as we are seeing more women stepping into entrepreneurial roles there will be a change. According to an from Forbes, report shows that even though women are less likely to start tech companies, they are just as innovative when creating products and services that are as technologically advanced as their male counter parts.

Q3 Do you think it is more difficult for female innovators and entrepreneurs to secure funding (and, therefore, be able to afford IP costs)?

Yes, it can be quite difficult. From my own experience as a female entrepreneur in the service sector, I have had my share of challenges. I had to first overcome my fear of starting a business and secondly, I was uncertain about how I would protect my company, my brand, and my hard work. Thirdly, it can be very difficult to communicate the value of what you are offering when the majority of funding opportunities are allocated to innovation in the technology sector, which is male dominated versus the service sector. I encourage other female entrepreneurs to find creative ways to protect their ideas/brands until they can afford to do so in the proper way.

Q4 Are there unique challenges that female inventors and entrepreneurs face?

Yes, in my personal experience as a female entrepreneur, one of my biggest challenges was the fear of failure. I have always felt like friends, family, and colleagues have expected so much of me. I was held in high regard and this added pressure to ensure success. My fear of failure escalated to the point where I didn’t want to start my business because of worry that I could not meet the necessary expectations of others.�� In addition to my fear of failure, I have found that there is a lack of quality resources within the community to educate, support and encourage female entrepreneurs in their business endeavours. I also had challenges finding a suitable mentor because again there was the fear of not being respected in the field, the worry of being exploited by a more experienced individual and the concern that a male mentor could possibly sexually harass me.

As a female entrepreneur, we are sometimes asked to choose between our careers and family and if we decide to not have a family and focus on our career we suffer judgement by others. In addition to dealing with society expectations and judgements, we sometimes struggle to get a “seat at the table” or have opportunities readily accessible to us due to gender and societal bias. Some individuals may assume that we are not equipped to the do our job based simply on gender, and this can subsequently lead to methods that undermine our efforts and increase challenges. I encourage my fellow female entrepreneurs to have faith, trust the process, and to step out boldly into the light. Never give up on your dreams.

Q5 How can the innovation and IP ecosystems become more inclusive for under-represented groups, such as female entrepreneurs?

Wow! This is a very good question. I believe the first step would be to talk openly with those that are affected and to get the policy makers, government and the wider society involved in the conversation. Secondly it is important to ensure we understand the challenges that underrepresented groups such as female entrepreneurs face. With the proper data gathered we could then create measurable actions that are intentional about reaching out and creating change. I believe in order for change to be sustainable there needs to be a system of accountability, as such, there would need to be continuity in the measures put in place to ensure that inclusivity is being maintained.

Q6 What types of assistance will benefit female entrepreneurs?

As female entrepreneurs, I believe these are would be very beneficial.

1) More access to funding, grants, resources and support for female ventures.
2) Workshops/Seminars geared towards navigating entrepreneurship as a female.
3) More nationwide policies to protect the interests of women who want to go into business.

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A recent filed by Myriad Genetics involving the alleged infringement of their controversial breast cancer screening tool has included the prestigious Toronto SickKids hospital as a co-plaintiff. This lawsuit has been a source of criticism for the hospital and has reinvigorated the debate on the merits of public-private research collaborations in health care innovation.

Aftermath of SCOTUS Ruling - Myriad Genetics Sues Archrival

This , the US Supreme Court rendered a landmark , ruling out the patentability of some genetic sequences in patents developed by Myriad Genetics for their breast cancer screening tests. (You can view some of the IPilogue’s extensive coverage of the international litigation around this issue , and .) The results of this decision and the invalidation of the Myriad patents were seen by many as a development that of breast cancer screening methods due to the possibility of cheap alternatives being produced without being hindered by patent licensing. A sound prediction, as , companies started releasing breast cancer screening tools at a lower cost than the Myriad products. A few weeks after the ruling, however, Myriad in the Utah District Court against Ambry Genetics, claiming that the company owned or licensed to Myriad that had not been a part of the US Supreme Court case. In response to the lawsuit, Ambry has recently Myriad for antitrust violations.

This case is interesting from a Canadian point of view since some of the patents at issue involve ownership agreements between Myriad and various public health organizations, . In the , it is stated that the public health organizations will lose "significant amounts of revenue" from lost royalties, and that the revenue stream from their ownership to date has brought in around $57 million. Although listing SickKids as a plaintiff in the lawsuit due to their ownership agreement, the story has nevertheless garnered . This lawsuit has been seen by some as an attack on affordable breast cancer screening for patients, with SickKids seemingly in a position that is opposite its role as a health care provider. Patent arrangements like the one between SickKids and Myriad can be seen as paradoxical; preventing patients from being able to access the medical discoveries that have been made possible by .

Public-Private Research Collaboration: A Complex Issue

Discussions arising from situations such as this ultimately stem from the relationship between public institutions and profit-seeking entities in health research collaboration. Analyses of the merits of such collaboration has reached conclusions in different fields and , but a recent seemed to suggest a statistically significant increase in pharmaceutical industry innovation from publically-funded scientific research.

Critics of these joint research ventures frequently cite the through a “tragedy of the anticommons” effect, where other research entities choose not to enter a field of innovation due to the fear of their production ultimately getting blocked by existing patents. Luigi Palombi, an Australian lawyer that has written a book about the Myriad breast cancer screening saga, criticized the SickKids incident and that “[i]t is our outrageous for any publicly funded research institution to be put in this situation” and that “[o]nce you have publicly funded institutions getting into bed with these guys, well then that raises a whole series of other questions and issues”. Other commentators, such as McGill University law professor Richard Gold, that "[t]here's always been a relationship obviously between industry and hospitals and universities. And it's not all bad because, of course, universities do basic research." Professor Gold also went on to state that he hopes public institutions “will be wiser in the way they set up their agreements in order to have a choice to whether to be involved in ligation". Although the majority of commentators seem to agree that SickKids is currently occupying an unfavourable position from its public involvement in the lawsuit, the debate on the merits of these joint health-care research ventures is certainly an unresolved issue.

The Future of Public-Private Health Research Collaboration in Canada

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