Image de-identification is essential for the public sharing of medical images, particularly in the widely used Digital Imaging and Communications in Medicine (DICOM) format as required by various regulations and standards, including Health Insurance Portability and Accountability Act (HIPAA) privacy rules, the DICOM PS3.15 standard, and best practices recommended by the Cancer Imaging Archive (TCIA). The Medical Image De-Identification Benchmark (MIDI-B) Challenge at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024) was organized to evaluate rule-based DICOM image de-identification algorithms with a large dataset of clinical DICOM images. In this report, we explore the critical challenges of de-identifying DICOM images, emphasize the importance of removing personally identifiable information (PII) to protect patient privacy while ensuring the continued utility of medical data for research, diagnostics, and treatment, and provide a comprehensive overview of the standards and regulations that govern this process. Additionally, we detail the de-identification methods we applied - such as pixel masking, date shifting, date hashing, text recognition, text replacement, and text removal - to process datasets during the test phase in strict compliance with these standards. According to the final leaderboard of the MIDI-B challenge, the latest version of our solution algorithm correctly executed 99.92% of the required actions and ranked 2nd out of 10 teams that completed the challenge (from a total of 22 registered teams). Finally, we conducted a thorough analysis of the resulting statistics and discussed the limitations of current approaches and potential avenues for future improvement.

De-identification is important in protecting patients' privacy for healthcare text analytics. The MASK framework is one of the best on the de-identification shared task organised by n2c2/i2b2 challenges. This work enhances the MASK framework by integrating ClinicalBERT, a deep learning model specifically fine-tuned on clinical texts, alongside traditional de-identification methods like dictionary lookup and rule-based approaches. The system effectively identifies and either redacts or replaces sensitive identifiable entities within clinical documents, while also allowing users to customise the masked documents according to their specific needs. The integration of ClinicalBERT significantly improves the performance of entity recognition, achieving 0.9732 F1-score, especially for common entities such as names, dates, and locations. A risk assessment feature has also been developed, which analyses the uniqueness of context within documents to classify them into risk levels, guiding further de-identification efforts. While the system demonstrates strong overall performance, this work highlights areas for future improvement, including handling more complex entity occurrences and enhancing the system's adaptability to different clinical settings.

In this contributed article, Ittai Dayan, co-founder and CEO of Rhino Health, believes that while traditional machine learning has huge potential for medical researchers, its major shortcoming is the vast amount of centralized data collection that’s required, and the privacy issues this creates. Federated learning has been suggested as a potential solution to this problem. This is a novel ML technique that is able to access data held across numerous decentralized servers (such as data held by individual hospitals), with the data never leaving these servers and remaining completely anonymous.

Remote patient monitoring is now a key application in medical spaces where cameras and AI are revolutionizing the delivery of care. This article will thus discuss how the two technologies work together to make life easier for patients and caregivers. The adoption of artificial intelligence is on the rise across all sectors. Though current AI cannot compete with the cognitive ability of the human brain, it has already started to dominate when it comes to performing mundane as well as intelligent tasks – and the medical field is not an exception to this. It has been captivating to see new and emerging applications and use cases where AI works in harmony with other technologies to enhance human experiences.

BOSTON, MA / ACCESSWIRE / August 2, 2022 / Rhino Health, a distributed computer platform leveraging the privacy-preserving concept of federated machine learning, announced that it has joined the Alliance for Artificial Intelligence in Healthcare (AAIH). Founded in 2018, the AAIH is the top global advocacy organization dedicated to the responsible adoption and application of AI/ML technologies in healthcare. It contains over 40 organizations dedicated to this mission, with stakeholders from industry, government, academia, and finance. "For AI to have strong, equitable, replicable, and continuously improving performance in healthcare, access to data is key," said Ittai Dayan, MD, co-founder and CEO of Rhino Health. "Federated data management, computation, and learning provides healthcare access without risking patient privacy. We look forward to contributing our knowledge of distributed computation and federated learning to this esteemed group of organizations to collaboratively advance AI in healthcare."

AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more. By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients--AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it's difficult to verify that researchers are only using the data they need and deleting it after they're done. Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner's system.

AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more. By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients -- AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it's difficult to verify that researchers are only using the data they need and deleting it after they're done. Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner's system.

Let's start with the one minute version: I was part of the EF12 London cohort in 2019, where I met my co-founder. A privacy-preserving medical-data marketplace and AI platform built around federated deep learning. The purpose of the platform would have been to allow data scientists to train deep learning models on highly sensitive healthcare data without that data ever leaving the hospitals. At the same time, thanks to a novel data monetization strategy and marketplace component, hospitals would have been empowered to make money from the data they are generating. We received pre-seed funding, valued at $1 million. Then the race for demo day began with frantic product building and non-stop business development.

In a new perspective piece "Transparency and reproducibility in artificial intelligence" published this week in the journal Nature, an international group of scientists including CUNY Graduate School of Public Health and Health Policy (CUNY SPH) Associate Professor Levi Waldron raised concerns about the lack of transparency in publication of artificial intelligence algorithms for health applications. The authors raise concerns about a recent publication in which a group including Google Health reported using artificial intelligence to diagnose breast cancer from mammogram images more accurately than expert human radiologists. The authors contend that restrictive data access procedures, lack of published computer code, and unreported model parameters make it impractically difficult for any other researchers to confirm or extend this work. The piece also highlights tensions over what are appropriate measures to protect patient privacy while allowing the broader research community to contribute methodology and to correct potential errors that could set back progress to the detriment of other patients. "This back-and-forth is one high-profile example of the current state of struggles over who controls data that has played out for decades in the biomedical sciences and other fields," says Professor Waldron.

Since March, several celebrities, including Tom Hanks, Rita Wilson, Idris Elba, sports figures and members of Congress have announced that they were infected with COVID-19. Hanks and Elba framed their Instagram posts with public health messages to urge others to self-quarantine and heed public health warnings about social isolation. Utah Jazz player Rudy Gobert included a dose of remorse for having challenged the severity of the risk when he contracted the virus after grabbing mics from the press pool, infecting several teammates. The point in repeating these stories is that in each case, a well-known person chose to reveal his or her health status. In recent years, many celebrities have taken this route, from Angelina Jolie with breast cancer treatment to Justin Bieber with Lyme disease.