Background While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities. Methods We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API. Results Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%). Interpretation U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity.

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Hybrid printed electronics (HPE) combines advantages of conventional electronics manufacturing technologies such as surface mount technology with those of printed electronics in order to realize more complex electronic systems. To realize the final product, such HPE subassemblies have to be connected to higher-level assemblies. So far interconnection techniques for the so-called level 3 interconnection between printed subsystems and standard electronics have not yet been considered adequately in scientific research. In this paper, alternative detachable level 3 interconnection technologies, i.e. spring loaded contact pins, zero insertion force (ZIF) as well as Non-ZIF connectors are investigated systematically regarding their electrical behaviour against the background of printed electronics. Screen-printed silver filled polymer thick film paste is used to realize printed conductor patterns on flexible polymer substrates, which are connected later on with the alternatives mentioned above. Transition resistance is measured using the four wire method as produced, after repeated mating cycles as well as after accelerated aging tests. The various electrical contacts are subjected to thermal stress in temperature cycling testing and during aging at a constant high temperature. As a result it can be stated, that the sping-loaded contact pins used in this investigation show superior behavior compared with the selected connectors in terms of resistance increase after mating cycles. Concerning long-term behavior after thermal cyling and high temperature storage, all investigated alternaives reveal convincing results.