DeepMind Faces: Google's AI department, otherwise known as DeepMined, the Google-owned AI research company, is the subject of a lawsuit. The lawsuit focuses on the company's use of the personal records of a whopping 1.6 million UK National Service patients, including confidential medical records. The #Google #AI department is getting a class-action lawsuit for gaining 1.6 million confidential medical records of #NHS patients. According to PCGamer, DeepMind received the documents to create a health application the company calls Streams. It was supposed to be an AI-based assistant to help healthcare workers and was previously used by the British National Health Service.
Figure 1 – Figure supplement 1: Learning curves on the random split-half validation used for model building. To facilitate comparisons, we evaluated predictions of age, fluid intelligence and neuroticism from a complete set of socio-demographic variables without brain imaging using the coefficient of determination R2 metric (y-axis) to compare results obtained from 100 to 3000 training samples (x-axis). The cross-validation (CV) distribution was obtained from 100 Monte Carlo splits. Across targets, performance started to plateau after around 1000 training samples with scores virtually identical to the final model used in subsequent analyses. These benchmarks suggest that inclusion of additional training samples would not have led to substantial improvements in performance.
The outbreak of the global pandemic has led to increased awareness and realization amongst the people about how technological advancements and innovations can be leveraged to help organizations and individuals to cope up with the drastic changes the world is witnessing across all levels. Consumers and their changing needs drive different industries. The way patients are consuming healthcare services is evolving, especially after the COVID-19, the consumer behavior has changed drastically. Pandemic, changing patients' needs and technological advancements are some of the few factors which are accelerating to change the dynamics of the healthcare industry. Patients are expecting new virtual and digital healthcare services from the healthcare institutions and to cater to those expectations, healthcare facilities have to change their work processes.
A class-action lawsuit has been launched against DeepMind, the Google-owned AI research company, over its use of the personal records of 1.6 million patients from the UK's National Health Service (thanks, AI News). The health data was provided by the Royal Free London NHS Foundation Trust in 2015.DeepMind is known for several achievements, not least kicking everyone's ass at Starcraft 2, but it was given the records in order to create a health app called Streams. This was supposed to be an AI-powered assistant to healthcare professionals and has been used by the UK NHS—but no more. This August it was announced that Streams is being decommissioned, and DeepMind's own 'health' section now returns a server error.The handing-over of patient records to one of the world's biggest technology companies was exposed by New Scientist in 2017, in a report showing that DeepMind had access to far more data than had been publicly announced. The UK Information Commission launched an investigation that ruled the Royal Free hospital hadn't done enough to protect patients' privacy: following which, DeepMind apologised. "Our investigation found a number of shortcomings in the way patient records were shared for this trial," Information Commissioner Elizabeth Denham said at the time. "Patients would not have reasonably expected their information to have been used in this way."The new suit has been launched by lead plaintiff Andrew Prismall, who was a patient at the Royal Free hospital, and includes approximately 1.6 million other affected patients on an 'opt-out' basis—that is, all parties will be included in the action unless they request otherwise."Given the very positive experience of the NHS that I have always had during my various treatments, I was greatly concerned to find that a tech giant had ended up with my confidential medical records," said Prismall in a statement."As a patient having any sort of medical treatment, the last thing you would expect is your private medical records to be in the hands of one of the world’s biggest technology companies. I hope that this case will help achieve a fair outcome and closure for all of the patients whose confidential records were obtained in this instance without their knowledge or consent."
In the Indian 2011 census, the elderly population age 60 and above accounted for 8.6% of the total population (103 million). This is projected to rise to 19.5% (319 million) by 2050. The proportion of the people aged 75 and above is expected to increase by 340% between 2011 and 2050. The demographic/ epidemiological shift will further overburden our health care systems. The need of the hour is to promote'Healthy ageing' as per WHO (decade commitment 2021-2030) to decrease the burden of chronic health conditions and improve quality of life of the older persons.
READY FOR ITS CLOSE-UP: Artificial intelligence has long been hyped as a game changer in health care: Remember this 2012 prediction that computers will replace 80 percent of doctors? But it's been much harder to get a sense of the real-world scale of the phenomenon. Is AI a perpetual technology of the future? Or is it starting to get a toehold? A recently released Food and Drug Administration database starts to get at that question.
Researchers at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Beth Israel Deaconess Medical Center have teamed up to improve electronic health records (EHRs) with artificial intelligence (AI) machine learning and published their findings in a recent study. The automation of patient health records gives hope to benefits to clinicians, patients, and stakeholders such as increase speed of data transfer, lower costs in maintaining paper records, increase efficiency, improve outcomes by avoiding or reducing clinical errors. However, electronic health records have yet to achieve many of these positive benefits and is a leading cause of burnout and stress among physicians according to the researchers. Clinicians are spending time on using the electronic health records instead of talking with patients. The worldwide electronic health records market was USD 26.8 billion in 2020 with North America having the highest revenue share of 45 percent according to Grand View Research.
Access to big datasets from e-health records and individual participant data (IPD) meta-analysis is signalling a new advent of external validation studies for clinical prediction models. In this article, the authors illustrate novel opportunities for external validation in big, combined datasets, while drawing attention to methodological challenges and reporting issues. #### Summary points A popular type of clinical research is the development of statistical models that predict disease presence and outcome occurrence in individuals,123 thereby informing clinical diagnosis and prognosis. Such models are referred to here as diagnostic and prognostic prediction models, but they have many other names including risk models, risk scores, and clinical prediction rules. They are typically developed by use of a multivariable regression framework, which provides an equation to estimate an individual’s risk based on values of multiple predictors (such as age and smoking, or biomarkers and genetic information). …
While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.
Healthcare and Life Sciences continues to experience an explosion in the use of Artificial Intelligence to deliver precision medicine, enhance quality of care, improve operational efficiencies, and drive breakthroughs in biomedical research to treat disease. Healthcare Providers, in particular, are looking to the areas of Smart Hospitals and Medical Imaging in this regard. There is an array of interconnected medical devices, sensors and applications that create the Internet of Medical Things (IoMT) which produce significant amounts of data needed to feed these AI models. In this webinar, OMDIA, Hewlett Packard Enterprise and NVIDIA experts will focus on how AI will act as a catalyst to enable the new wave of capabilities and the investments being made now to take advantage of machine learning and state-of-the-art computing. If you have already registered, click here to access.