A large amount of patient-related information is collected by healthcare operators in their everyday activities, which span over a wide spectrum of medical processes, such as wellness check-ups or examinations at healthcare hospitals or medical offices, just to name a few. For instance, when a patient undergoes a medical examination for the first time, the physician usually creates a patient file including his medical history, current treatments, medications, diagnosis, and other relevant information . Considering that disease diagnosis is crucial for health condition monitoring, it is natural to envisage that such large amount of data can be profitably used to guide data-driven disease classification tasks in the quest for early and accurate diagnoses, taking care of the complex interactions among clinical, biological, and pathological variables. Indeed, with the aim of identifying the best services and treatments for the patients, recent advances in medicine have proposed various models for personalized, predictive, and preventive medicine that make use of electronic health records (EHRs) and high-dimensional omics data . However, accessing and using EHRs and omics data can be rather challenging in practice, because they are heterogeneous and usually stored in different data formats.
WAKAYAMA – A female radiologic technologist at a medical university in Wakayama Prefecture was suspended from work for three months on Tuesday for persistently stalking four male doctors over a year-long period. Wakayama Medical University took the disciplinary action against the woman, 44, after she ambushed the doctors, who are in their 20s and 40s, on campus, sent emails professing her attraction to them and at times called them while they were on duty over a year from February 2017. The university had warned the woman twice after receiving complaints from the doctors, but it moved to suspend her because she continued stalking the men. The woman was quoted as telling the university that she had no intention to stalk the doctors.
The launch of the Department two years ago has been made possible by a generous initial gift from Dallas philanthropist, Ms. Lyda Hill. The scientific focus of the Department is on creating computational methods for integrative analysis and modeling of complex biomedical processes in high-dimensional and multi-modal data sets. The development of this program is driven under the premise that bioinformatics in its core is a pattern recognition problem whose solution builds on the combination of computational theory and algorithms that are shareable across all biomedical data types and research applications. Accordingly, the Department will be composed of a faculty that tackles fundamental questions in computer science while effectively translating the results into high-impact basic and clinical research. The Department is also home to the Bioinformatics Core Facility and the Bio-High Performance Computing group, which provide robust analytical and computational workflows to end users across campus.
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human–AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide. AI has the potential to reshape medicine and make healthcare more accurate, efficient and accessible; this Review discusses recent progress, opportunities and challenges toward achieving this goal.