The rapid development of Artificial Intelligence (AI) has opened up exciting new possibilities in medicine. There are a multitude of similar projects underway that employ AI in the early detection of macular degeneration, acute kidney failure, skin cancer, sepsis, and Alzheimer’s disease, among others.
In healthcare, AI has been used in a myriad of ways, yet it is still far from reaching its potential. Already, AI has been used to quickly obtain useful information from patient populations to assess - in real-time - risks for the general population (truer than ever now in the COVID-19 age) and to carry out highly repetitive tasks such as analysis of tests, X-rays, or CT Scans. Clinically, it has proven to lower the risk of diagnostic mistakes, promoting a precision medicine approach and implementing the best course treatments based on a rapid assessment of clinical condition. For health systems, it can help organize clinical charts while reducing needless administrative work, and analyze the performance of specific healthcare institutions.
If AI reaches its potential, it would truly even the playing field for patients. Diagnostic tests could be turned around faster, saving precious time in treatment. Physicians can conserve more bandwidth for patient care and less for administrative tasks. Patients in rural areas can receive the same level of care that were previously reserved for big city academic medical institutions.
In order to do so, however, it needs to be trained on very large data sets. Healthcare institutions have huge patient data sets available, from the first presentation to diagnosis, treatment, and final outcome. Scientists who are going to develop AI-based tools need those data to achieve the full potential of AI for healthcare. Etta Pisano, chief research officer at the American College of Radiology, says that “to fulfill the promise of AI in healthcare, medical data will need to be treated as precious to our health as drinking water”. However, one of the limitations for the advancement of AI-based tools has been the lack of consensus on an ethical framework for sharing clinical data.
In March 2020, a framework for the ethics of sharing clinical imaging data for AI was published by Dr. David Larson and his colleagues from Stanford University. They argue that after clinical data are acquired for the primary purpose of diagnosing and treating the patient, any secondary use of the data should be regarded as a form of public good. Rather than debate whether patients or provider organizations “own” the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.
The implications are that the patients do not need to consent to secondary use of their clinical data and that data should not be sold, but rather made available for the development and implementation of knowledge and tools that fulfill societal benefit.
We know that in the evolving landscape of health information technologies, AI can transform medicine by improving diagnosis, treatment, and the delivery of patient care. Currently, this transformation is stalled without the proper sharing of healthcare data. Dialogue and joint efforts are needed from developers, providers, and policymakers to navigate the ethical questions surrounding AI and medical data-sharing, and to thoughtfully translate ethical consideration into regulatory and legal requirements. Dr. Larson’s recently published framework is a first promising step in that direction, and it should be on all stakeholders to engage and improve future care for patients.
Segmed is positioned at the intersection of multiple stakeholders, and we have explored the ethical quandaries of data sharing from different viewpoints. We find that the ultimate goal across all stakeholders is to provide equitable healthcare for all and improve the standard of quality across the entire population by taking advantage of developing technologies.
Healthcare providers are the core stakeholders in healthier society, and have a responsibility to utilize their data for development. Our role within that framework is to ethically and transparently facilitate that process so providers can rest assured knowing that their data is being used to affect positive change. With increased collaboration, our families can finally get the cutting edge care they deserve.
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