Journal of Medical Artificial Intelligence (JMAI) is now a member of Committee on Publication Ethics (COPE)

Starting from July 2021, AME Publishing Company is formally recognized a publisher member of Committee on Publication Ethics (COPE) and over 60 AME-published journals become a member of Committee on Publication Ethics (COPE), as part of AME’s commitment to best practice in scholarly publishing. Journal of Medical Artificial Intelligence (JMAI) is among the list of COPE membership.

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Implementation of transparent peer review at Journal of Medical Artificial Intelligence (JMAI)

With a commitment to openness and accountability, and to increase the level of transparency throughout our peer review process, our editorial office has decided to implement a transparent peer review process as an option for all manuscripts submitted to JMAI from March 19, 2020.

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Application of the CARE Guideline as reporting standard in Journal of Medical Artificial Intelligence

Journal of Medical Artificial Intelligence welcomes case reports which are supposed to report new observations of diseases, clinical findings or novel/unique treatment outcomes relevant to practitioners in the fields of medicine.

For completeness and transparency for published case reports, Journal of Medical Artificial Intelligence will apply the CARE guidelines (1) as reporting standards for case reports starting from October 14, 2019.

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Editor In Chief: Jia Chang

Director of Tencent Internet Plus Medical Center, Director of the National New-Generation Open Medical Imaging Artificial Intelligence Platform. Shenzhen, China
Mr. Chang Leads Miying to become a member of the "National Artificial Intelligence Standardization Group" under the delegation of the Ministry of Science and Technology.

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Psychogenic non-epileptic seizures, recent advances and commentary on, Vasta et al., the application of artificial intelligence to understand the biological bases of the disorder

As many as 33 per 100,000 people experience episodes of paroxysmal impairment associated with a range of manifestations that can be motor, sensory, and/or mental and closely mimic and frequently mistaken for epileptic seizures (1). These episodes are termed psychogenic non-epileptic seizures (PNES). The prevalence of PNES episodes is much higher in epilepsy practices, reaching as high as 30% (2). The diagnosis of PNES remains a process of excluding epilepsy and thus leads to an average time from onset of these paroxysms to diagnosis of close to seven years.

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Promises and limitations of deep learning for medical image segmentation

It is not a secret that recent advances in deep learning (1) methods have achieved a scientific and engineering milestone in many different fields such as natural language processing, computer vision, speech recognition, object detection, and segmentation, to name a few. Different applications of deep learning to medical imaging started to appear first in workshops, conferences and then in journals. According to a recent survey (2), the number of papers grew rapidly in 2015 and 2016. Nowadays, deep learning methods are pervasive throughout the entire medical imaging community, with Convolutional Neural Networks (CNNs) being the most used model for tasks such as dense prediction (or segmentation), detection and classification. In the same survey, which analyzed more than 300 contributions in the field, the authors found that computed tomography (CT) was the third most used imaging modality.

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Journal of Medical Artificial Intelligence (JMAI)

Dear friends and colleagues,
Welcome to Journal of Medical Artificial Intelligence (JMAI). With the mission to promote specific areas of medical artificial intelligence (AI), this journal is dedicated to showcase the latest issues in the field.
The study and development of AI has experienced 70 years of development. Although AI developed slowly in the past decades, innovative technologies have propelled advances in AI in recent years. The invention of applications, software and algorithms that helps manipulate a large amount of data produces statistically- and evidence-based results, and their application into the medical field helps improve the accuracy in diagnosis, decision making and medical therapy.

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JMAI Annual Report 2019

Greetings from the editorial office of JMAI. First of all, we would like to sincerely thank you for your support and assistance in improving the journal as our editorial board members, authors, reviewers and readers. We appreciate your great work, commitment and dedication in the past year, which have been integral to JMAI's progress and success.

Here we would like to share the annual report with you, we would love to hear your thoughts on how we can improve our journal. We are looking forward to your innovations and contributions in the year to come.

We wish you a very Happy 2020, filled with success, happiness and good health.

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