Discussions about machine learning's impact on radiology might begin with image interpretation, but that's only the tip of the iceberg. When it comes to realizing the technology's full potential, it's like Bachman Turner Overdrive sang many years ago: You ain't seen nothing yet. The authors of a new analysis published in the Journal of the American College of Radiology wrote at length about the many applications of machine learning. "Machine learning has the potential to solve many challenges that currently exist in radiology beyond image interpretation," wrote lead author Paras Lakhani, MD, department of radiology at Thomas Jefferson University Hospital in Philadelphia, and colleagues. "One of the reasons there is great excitement in radiology today is the access to digital Big Data.
The pace of artificial intelligence technology adoption in healthcare varies considerably. Some medical establishments are undertaking small incremental changes; others centers have seen several years of innovation; and a proportion remain tied to the traditional healthcare model of the 1990s. This is the view of Dr. Ameet Bakhai, deputy director of research at the Royal Free London NHS Foundation Trust. Dr. Bakhai was expressing his views in advance of a major conference that is set to look at artificial intelligence in healthcare: Digital Healthcare Transformation Summit 2017, which takes place in London in December. A key theme is that although there are more advanced machines, from ultra-high-resolution imaging instruments to surgical robots, these tend to remain fully controlled by humans rather than with decisions made by artificial intelligence.
Before AI systems can be deployed in healthcare applications, they need to be'trained' through data that are generated from clinical activities, such as screening, diagnosis, treatment assignment and so on, so that they can learn similar groups of subjects, associations between subject features and outcomes of interest. These clinical data often exist in but not limited to the form of demographics, medical notes, electronic recordings from medical devices, physical examinations and clinical laboratory and images.12 For example, Jha and Topol urged radiologists to adopt AI technologies when analysing diagnostic images that contain vast data information.13 Li et al studied the uses of abnormal genetic expression in long non-coding RNAs to diagnose gastric cancer.14 Shin et al developed an electrodiagnosis support system for localising neural injury.15
Just imagine a day in your life, where you would no longer have to wait for weeks to visit your paediatrician, followed by an additional wait for your child's health test results and then still more waiting to get an accurate child health record. We are all aware that the constantly altering demographic drifts in child healthcare, the escalating child population and a steeping rise in various chronic illnesses that children these days are suffering from, have nothing but created an enormous demand for health care and social care services for children. Given the superiority of the 21st-century technology, the major question that arises is how can we modify the child health care system to better cope with the rising healthcare needs? The solution to this concern is nothing but efficiently digitalizing child health care to bring in more innovation. Electronic child healthcare is something that needs to be given immediate attention.
Singapore will introduce a new bill mandating all healthcare providers in the country to contribute to the national electronic health record system (NEHR). Launched in 2011, the system was developed to create a central database from which clinical summary records from different providers could be stored and shared to facilitate the delivery of healthcare. Government unveils a new scheme, investing up to S$150 million over five years, to use artificial intelligence to resolve challenges affecting society and sets up data science consortium to drive the sector. Touting the maxim "one patient, one health record", the database is owned by the Ministry of Health and managed by its agency Integrated Health Information Systems (IHIS). Data contribution currently is voluntary for private healthcare licensees and the ministry, over the years, has been encouraging all providers to participate.
London and Berlin-based, AI-powered health app maker Ada Health has raised $47 million (40 million euro) in a funding round led by global investment group Access Industries. June Fund, Cumberland VC, and entrepreneur William Tunstall-Pedoe also contributed along with existing investors. Ada Health officially launched its app back in April after a soft launch in late 2016 and six years of research and development. It's one of a handful of companies using artificial intelligence and natural language processing. It asks relevant, personalized questions and suggests possible causes for users' symptoms.
Picture the hospital of the future replete with a NASA-like command center featuring scores of information screens and a radiology department that leverages AI technology to help improve diagnostic accuracy and deep-learning technology to ensure that radiology images are clear. This is the world of data-driven medicine that, sees -- not in a crystal ball but in the real world. "Those technologies are here now, and they are gaining steam," said Charles Koontz, CEO of GE Healthcare Digital and chief digital officer of GE Healthcare in an interview at GE Digital's Minds Machines event in San Francisco last week. A 2016 McKinsey study supports the notion that the healthcare sector is embracing digital transformation. The field has seen "some core change," according to McKinsey, basing that assessment on a survey of 10 verticals.
Artificial intelligence could one day be used to help identify a person contemplating suicide. Around 800,000 people die a year from suicide, according to the World Health Organization. It's currently the second-leading cause of death in the U.S. among people between the ages of 15 and 24. But a new study is using brain scans and AI to show how someone experiencing suicidal thoughts thinks differently about life and death. The results were published in Nature Human Behavior.
Mention strong words such as "death" or "praise" to someone who has suicidal thoughts and chances are the neurons in their brains activate in a totally different pattern than those of a non-suicidal person. That's what researchers at University of Pittsburgh and Carnegie Mellon University discovered, and trained algorithms to distinguish, using data from fMRI brain scans. The scientists published the findings of their small-scale study Monday in the journal Nature Human Behaviour. They hope to study a larger group of people and use the data to develop simple tests that doctors can use to more readily identify people at risk of suicide. Suicide is the second-leading cause of death among young adults, according to the U.S. Centers for Disease Control and Prevention.
It has been a privilege to connect with the nation's health IT leaders at the CHIME 2017 CIO Forum in San Antonio this week. Listening to their stories and challenges, and seeing the healthcare landscape through their eyes, is vital to helping us create the best possible technology to unburden clinicians and advance patient care. The role of CIOs and CMIOs has been evolving in important ways in recent years. For much of the past decade, HealthIT leaders were hyper-focused on selecting, implementing and maintaining integrated, highly complex Electronic Health Record (EHR) systems across all facilities and care settings. For many CIOs, the EHR buy was the single largest financial investment in the history of their organization.