Say Goodbye to the 'Paper on Screen', Rethinking Presentation of and Interaction with Medical Information.

Traditionally, Electronic Medical Records (EMR) have been designed to mimic paper records. Organizing and presenting medical information along the lines that evolved for non-digital records over the decades, reduced change management for medical users, but failed to make use of the potential of organizing digital data. We proposed a method to create clinical dashboards to increase the usability of information in the medical records. Official clinical guidelines were studied by a working group, including dashboard target users. Necessary clinical concepts contained in the medical records were identified according to the clinical context and finally, dedicated technical tools with standard terminologies were used to represent categories of information. We used this method to generate and implement a dashboard for sepsis. The dashboard was found to be appropriate and easy to use by the target users.

Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives.

INTRODUCTION: Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed. AREAS COVERED: AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device. The draft EU Regulation on AI proposes rules that will apply across industry sectors, while for devices the Medical Device Regulation also applies. In the CORE-MD project (Coordinating Research and Evidence for Medical Devices), we have surveyed definitions and summarize initiatives made by professional consensus groups, regulators, and standardization bodies. EXPERT OPINION: The level of clinical evidence required should be determined according to each application and to legal and methodological factors that contribute to risk, including accountability, transparency, and interpretability. EU guidance for MDSW based on international recommendations does not yet describe the clinical evidence needed for medical AI software. Regulators, notified bodies, manufacturers, clinicians and patients would all benefit from common standards for the clinical evaluation of high-risk AI applications and transparency of their evidence and performance.

Regulatory Aspects of the Use of Artificial Intelligence Medical Software.

The rapidly evolving scenario of Artificial intelligence (AI) in medicine comes with new regulatory challenges, including certification, ownership, and control of data sharing, privacy protection, and accountability. The Medical Physicists (MPs) are traditionally responsible for ensuring the safety and quality of technology implementation in diagnostic and therapeutic settings. As such, they are also expected to contribute to the introduction of AI medical devices in routine clinical practice. Specifically, the MPs will play a stakeholder role for AI tools procurement, acceptance testing, commissioning, and quality assurance to confirm the claimed performances in relation to the medical device's intended use. Moreover, MPs who act as co-creators of such AI tools, will play a pivotal role in product requirements definition, data collection and annotation, clinical evaluation, support for regulatory pathways and marketing through scientific congresses and scientific publications. As AI software differs from the traditional (hardware) medical device that the MP is used to introduce in clinical settings, there is a need to acquire new competencies in the field of AI and its regulatory aspects. The purpose of this paper is to provide MPs with practical guidelines on regulatory aspects of AI medical devices, in the European and in the US landscape.

Identification of four adenosine kinase isoforms in tobacco By-2 cells and their putative role in the cell cycle-regulated cytokinin metabolism.

Adenosine kinase (ADK), a key enzyme in the regulation of the intracellular level of adenosine is also speculated to be responsible for the conversion of cytokinin ribosides to their respective nucleotides. To elucidate the role of ADK in the cytokinin metabolism of tobacco BY-2 cells (Nicotiana tabacum cv. "Bright Yellow-2"; TBY-2), we have identified and characterized the full-length cDNAs encoding four ADK isoforms of N. tabacum and determined their catalytic properties. The four TBY-2 ADK isoforms (designated 1S, 2S, 1T, and 2T) display a high affinity for both adenosine (Km 1.88-7.30 microm) and three distinct types of cytokinin ribosides: isopentenyladenosine; zeatin riboside; and dihydrozeatin riboside (Km 0.30-8.71 microm). The Vmax/Km values suggest that ADK2S exhibits in vitro an overall higher efficiency in the metabolism of cytokinin ribosides than the other three isoforms. The expression pattern of NtADK genes is modulated significantly during the cell cycle. We suggest that the increased transcript accumulation of NtADK coupled to an increased ADK activity just prior to mitosis is associated with a very active cytokinin metabolism at that phase of the cell cycle of synchronized TBY-2 cells.