White jigsaw puzzle as a human brain on blue. Concept for Alzheimer's disease.

Detecting Dementia

Cross-posted, with slight modification, from Harvard Law Today, where it originally appeared on November 21, 2020. 

By Chloe Reichel

Experts gathered last month to discuss the ethical, social, and legal implications of technological advancements that facilitate the early detection of dementia.

“Detecting Dementia: Technology, Access, and the Law,” was hosted on Nov. 16 as part of the Project on Law and Applied Neuroscience, a collaboration between the Center for Law, Brain and Behavior at Massachusetts General Hospital and the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School.

The event, organized by Francis X. Shen ’06 Ph.D. ’08, the Petrie-Flom Center’s senior fellow in Law and Applied Neuroscience and executive director of the Center for Law, Brain and Behavior at Massachusetts General Hospital, was one of a series hosted by the Project on Law and Applied Neuroscience on aging brains.

Early detection of dementia is a hopeful prospect for the treatment of patients, both because it may facilitate early medical intervention, as well as more robust advance care planning.

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AI concept art.

Health Care AI in Pandemic Times

By Jenna Becker

The early days of the COVID-19 pandemic was met by the rapid rollout of artificial intelligence tools to diagnose the disease and identify patients at risk of worsening illness in health care settings.

Understandably, these tools generally were released without regulatory oversight, and some models were deployed prior to peer review. However, even after several months of ongoing use, several AI developers still have not shared their testing results for external review. 

This precedent set by the pandemic may have a lasting — and potentially harmful — impact on the oversight of health care AI.

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AI concept art.

AI’s Legitimate Interest: Video Preview with Charlotte Tschider

The Health Law Policy, Bioethics, and Biotechnology Workshop provides a forum for discussion of new scholarship in these fields from the world’s leading experts.

The workshop is led by Professor I. Glenn Cohen, and presenters come from a wide range of disciplines and departments.

In this video, Charlotte Tschider gives a preview of her paper, “AI’s Legitimate Interest: Towards a Public Benefit Privacy Model,” which she will present at the Health Law Policy workshop on November 9, 2020. Watch the full video below:

Medicine doctor and stethoscope in hand touching icon medical network connection with modern virtual screen interface, medical technology network concept

Insufficient Protections for Health Data Privacy: Lessons from Dinerstein v. Google

By Jenna Becker

A data privacy lawsuit against the University of Chicago Medical Center and Google was recently dismissed, demonstrating the difficulty of pursuing claims against hospitals that share patient data with tech companies.

Patient data sharing between health systems and large software companies is becoming increasingly common as these organizations chase the potential of artificial intelligence and machine learning in healthcare. However, many tech firms also own troves of consumer data, and these companies may be able to match up “de-identified” patient records with a patient’s identity.

Scholars, privacy advocates, and lawmakers have argued that HIPAA is inadequate in the current landscape. Dinerstein v. Google is a clear reminder that both HIPAA and contract law are insufficient for handling these types of privacy violations. Patients are left seemingly defenseless against their most personal information being shared without their meaningful consent.

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Picture of doctor neck down using an ipad with digital health graphics superimposed

Is Data Sharing Caring Enough About Patient Privacy? Part II: Potential Impact on US Data Sharing Regulations

A recent US lawsuit highlights crucial challenges at the interface of data utility, patient privacy & data misuse

By Timo Minssen (CeBIL, UCPH), Sara Gerke & Carmel Shachar

Earlier, we discussed the new suit filed against Google, the University of Chicago (UC), and UChicago Medicine, focusing on the disclosure of patient data from UC to Google. This piece goes beyond the background to consider the potential impact of this lawsuit, in the U.S., as well as placing the lawsuit in the context of other trends in data privacy and security.

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Image of binary and dna

Is Data Sharing Caring Enough About Patient Privacy? Part I: The Background

By Timo Minssen (CeBIL, UCPH), Sara Gerke & Carmel Shachar

A recent US lawsuit highlights crucial challenges at the interface of data utility, patient privacy & data misuse

The huge prospects of artificial intelligence and machine learning (ML), as well as the increasing trend toward public-private partnerships in biomedical innovation, stress the importance of an effective governance and regulation of data sharing in the health and life sciences. Cutting-edge biomedical research strongly demands high-quality data to ensure safe and effective health products. It is often argued that greater access to individual patient data collections stored in hospitals’ medical records systems may considerably advance medical science and improve patient care. However, as public and private actors attempt to gain access to such high-quality data to train their advanced algorithms, a number of sensitive ethical and legal aspects also need to be carefully considered. Besides giving rise to safety, antitrust, trade secrets, and intellectual property issues, such practices have resulted in serious concerns with regard to patient privacy, confidentiality, and the commitments made to patients via appropriate informed consent processes.

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Artificial Intelligence for Suicide Prediction

Suicide is a global problem that causes 800,000 deaths per year worldwide. In the United States, suicide rates rose by 25 percent in the past two decades, and suicide now kills 45,000 Americans each year, which is more than auto accidents or homicides.

Traditional methods of predicting suicide, such as questionnaires administered by doctors, are notoriously inaccurate. Hoping to save lives by predicting suicide more accurately, hospitals, governments, and internet companies are developing artificial intelligence (AI) based prediction tools. This essay analyzes the risks these systems pose to safety, privacy, and autonomy, which have been under-explored.

Two parallel tracks of AI-based suicide prediction have emerged.

The first, which I call “medical suicide prediction,” uses AI to analyze patient records. Medical suicide prediction is not yet widely used, aside from one program at the Department of Veterans Affairs (VA). Because medical suicide prediction occurs within the healthcare context, it is subject to federal laws, such as HIPAA, which protects the privacy and security of patient information, and the Federal Common Rule, which protects human research subjects.

My focus here is on the second track of AI-based suicide prediction, which I call “social suicide prediction.” Though essentially unregulated, social suicide prediction uses behavioral data mined from consumers’ digital interactions. The companies involved, which include large internet platforms such as Facebook and Twitter, are not generally subject to HIPAA’s privacy regulations, principles of medical ethics, or rules governing research on human subjects.

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robotic hand placing a metal cylinder in the matching hold in a wooden box

Machine Learning as the Enemy of Science? Not Really.

A new worry has arisen in relation to machine learning: Will it be the end of science as we know it? The quick answer is, no, it will not. And here is why.

Let’s start by recapping what the problem seems to be. Using machine learning, we are increasingly more able to make better predictions than we can by using the tools of traditional scientific method, so to speak. However, these predictions do not come with causal explanation. In fact, the more complex the algorithms become—as we move deeper into deep neural networks—the better are the predictions and the worse are the explicability. And thus “if prediction is […] the primary goal of science” as some argue, then the pillar of scientific method—understanding of phenomena—becomes superfluous and machine learning seems to be a better tool for science than scientific method.

But is this really the case? This argument makes two assumptions: (1) The primary goal of science is prediction and once a system is able to make accurate predictions, the goal of science is achieved; and (2) machine learning conflicts with and replaces the scientific method. I argue that neither of these assumptions hold. The primary goal of science is more than just prediction—it certainly includes explanation of how things work. And moreover, machine learning in a way makes use of and complements the scientific method, not conflicts with it.

Here is an example to explain what I mean. Prediction through machine learning is used extensively in healthcare. Algorithms are developed to predict hospital readmissions at the time of discharge or to predict when a patient’s condition will take a turn for worse. This is fantastic because these are certainly valuable pieces of information and it has been immensely difficult to make accurate predictions in these areas. In that sense, machine learning methodology indeed surpasses the traditional scientific method in predicting these outcomes. However, this is neither the whole story nor the end of the story. Read More

AI Citizen Sophia and Legal Status

By Gali Katznelson

Two weeks ago, Sophia, a robot built by Hanson Robotics, was ostensibly granted citizenship in Saudi Arabia. Sophia, an artificially intelligent (AI) robot modelled after Audrey Hepburn, appeared on stage at the Future Investment Initiative Conference in Riyadh to speak to CNBC’s Andrew Ross Sorkin, thanking the Kingdom of Saudi Arabia for naming her the first robot citizen of any country. Details of this citizenship have yet to be disclosed, raising suspicions that this announcement was a publicity stunt. Stunt or not, this event raises a question about the future of robots within ethical and legal frameworks: as robots come to acquire more and more of the qualities of human personhood, should their rights be recognized and protected?

Looking at a 2016 report passed by the European Parliament’s Committee on Legal Affairs can provide some insight. The report questions whether robots “should be regarded as natural persons, legal persons, animals or objects – or whether a new category should be created.” I will discuss each of these categories in turn, in an attempt to position Sophia’s current and future capabilities within a legal framework of personhood.

If Sophia’s natural personhood were recognized in the United States, she would be entitled to, among others, freedom of expression, freedom to worship, the right to a prompt, fair trial by jury, and the natural rights to “life, liberty, and the pursuit of happiness.” If she were granted citizenship, as is any person born in the United States or who becomes a citizen through the naturalization process, Sophia would have additional rights such as the right to vote in elections for public officials, the right to apply for federal employment requiring U.S. citizenship, and the right to run for office. With these rights would come responsibilities: to support and defend the constitution, to stay informed of issues affecting one’s community, to participate in the democratic process, to respect and obey the laws, to respect the rights, beliefs and opinions of others, to participate in the community, to pay income and other taxes, to serve on jury when called, and to defend the country should the need arise. In other words, if recognized as a person, or, more specifically, as a person capable of obtaining American citizenship, Sophia could have the same rights as any other American, lining up at the polls to vote, or even potentially becoming president. Read More

“Siri, Should Robots Give Care?”

By Gali Katznelson

Having finally watched the movie Her, I may very well be committing the “Hollywood Scenarios” deadly sin by embarking on this post. This is one of the seven deadly sins of people who sensationalize artificial intelligence (AI), proposed by Rodney Brooks, former director of the Computer Science and Artificial Intelligence Laboratory at MIT. Alas, without spoiling the movie Her (you should watch it), it’s easy for me to conceptualize a world in which machines can be trained to mimic a caring relationship and provide emotional support. This is because, in some ways, it’s already happening.

There are the familiar voice assistants, such as Apple’s Siri, to which people may be turning for health support. A study published in JAMA Internal Medicine in 2016 found that that the responses of smartphone assistants such as Apple’s Siri or Samsung’s S Voice to mental and physical health concerns were often inadequate. Telling Siri about sexual abuse elicited the response, “I don’t know what you mean by ‘I was raped.’” Telling Samsung’s S Voice you wanted to commit suicide led to the perhaps not-so-sensitive response, “Don’t you dare hurt yourself.” This technology proved far from perfect in providing salient guidance. However, since this study came out over a year ago, programmers behind Siri and S Voice have remedied these issues by providing more appropriate responses, such as counseling hotline information.

An AI specifically trained to provide helpful responses to mental health issues is Tess, “a psychological AI that administers highly personalized psychotherapy, psycho-education, and health-related reminders, on-demand, when and where the mental health professional isn’t.” X2AI, the company behind Tess, is in the process of finalizing an official Board of Ethics, and for good reason. The ethical considerations of an artificially intelligent therapist are rampant, from privacy and security issues to the potential for delivering misguided information that could cost lives. Read More