Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis -- a biologically-inspired three-layer parameter architecture -- and a therapeutic framework connecting diagnosis to treatment.
Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and institutional constraints. In addition, models trained in centralized settings frequently fail to generalize across clinical sites because of heterogeneity in imaging protocols and continuously evolving data distributions arising from differences in scanners, acquisition parameters, and patient populations. Federated learning offers a promising solution by enabling collaborative model training without sharing raw data. However, incorporating differential privacy into federated learning, while essential for privacy guarantees, often leads to degraded accuracy, unstable convergence, and reduced generalization. In this work, we propose an adaptive differentially private federated learning (ADP-FL) framework for medical image segmentation that dynamically adjusts privacy mechanisms to better balance the privacy-utility trade-off. The proposed approach stabilizes training, significantly improves Dice scores and segmentation boundary quality, and maintains rigorous privacy guarantees. We evaluated ADP-FL across diverse imaging modalities and segmentation tasks, including skin lesion segmentation in dermoscopic images, kidney tumor segmentation in 3D CT scans, and brain tumor segmentation in multi-parametric MRI. Compared with conventional federated learning and standard differentially private federated learning, ADP-FL consistently achieves higher accuracy, improved boundary delineation, faster convergence, and greater training stability, with performance approaching that of non-private federated learning under the same privacy budgets. These results demonstrate the practical viability of ADP-FL for high-performance, privacy-preserving medical image segmentation in real-world federated settings.
Vision-language foundation models (VLMs) have shown great potential in feature transfer and generalization across a wide spectrum of medical-related downstream tasks. However, fine-tuning these models is resource-intensive due to their large number of parameters. Prompt tuning has emerged as a viable solution to mitigate memory usage and reduce training time while maintaining competitive performance. Nevertheless, the challenge is that existing prompt tuning methods cannot precisely distinguish different kinds of medical concepts, which miss essentially specific disease-related features across various medical imaging modalities in medical image classification tasks. We find that Large Language Models (LLMs), trained on extensive text corpora, are particularly adept at providing this specialized medical knowledge. Motivated by this, we propose incorporating LLMs into the prompt tuning process. Specifically, we introduce the CILMP, Conditional Intervention of Large Language Models for Prompt Tuning, a method that bridges LLMs and VLMs to facilitate the transfer of medical knowledge into VLM prompts. CILMP extracts disease-specific representations from LLMs, intervenes within a low-rank linear subspace, and utilizes them to create disease-specific prompts. Additionally, a conditional mechanism is incorporated to condition the intervention process on each individual medical image, generating instance-adaptive prompts and thus enhancing adaptability. Extensive experiments across diverse medical image datasets demonstrate that CILMP consistently outperforms state-of-the-art prompt tuning methods, demonstrating its effectiveness. Code is available at https://github.com/usr922/cilmp.
Classifier-free guidance (CFG) is a key technique for improving conditional generation in diffusion models, enabling more accurate control while enhancing sample quality. It is natural to extend this technique to video diffusion, which generates video conditioned on a variable number of context frames, collectively referred to as history. However, we find two key challenges to guiding with variable-length history: architectures that only support fixed-size conditioning, and the empirical observation that CFG-style history dropout performs poorly. To address this, we propose the Diffusion Forcing Transformer (DFoT), a video diffusion architecture and theoretically grounded training objective that jointly enable conditioning on a flexible number of history frames. We then introduce History Guidance, a family of guidance methods uniquely enabled by DFoT. We show that its simplest form, vanilla history guidance, already significantly improves video generation quality and temporal consistency. A more advanced method, history guidance across time and frequency further enhances motion dynamics, enables compositional generalization to out-of-distribution history, and can stably roll out extremely long videos. Project website: https://boyuan.space/history-guidance
Seyed Abdosaleh Jafari, Behin Araminia, Hanie Tavasoli
et al.
Research on human dignity is crucial for understanding the ethical foundations of human rights. Neglecting to address certain pitfalls in this area of research can lead to adverse effects, including the perpetuation of discrimination, the misrepresentation of dignity across different schools of thought, and the weakening of ethical standards in human rights discourse. The present study aims to identify such challenges by analytically examining outstanding research in this field. Our surveys have identified challenges and pitfalls that were categorized into two groups: challenges in the field of materials, and challenges in the field of methods. In terms of materials, researchers may fail to adequately consider the historical and cultural contexts that shape these views, while in terms of methods, they may overlook the diverse perspectives that contribute to a comprehensive understanding of dignity. Consequently, it is imperative for researchers to remain vigilant and avoid these pitfalls to ensure that their work upholds the true essence of human dignity and effectively advocates for the rights of all individuals, especially those from marginalized backgrounds.
History of medicine. Medical expeditions, Medical philosophy. Medical ethics
Over 50% of those engaging in high altitude activities are women. Nonetheless, women have historically been neglected in scientific literature regarding backcountry environments. Acute mountain sickness (AMS), a condition triggered by hypoxia, is common at altitudes >2500 m above sea level, and prior studies suggest that women are particularly vulnerable to it due to hormonal changes associated with menstruation. Therefore, this study assessed the association of menstruation and AMS incidence during a highaltitude expedition to Mount Kilimanjaro. This retrospective study involved a review of records from the Equal Playing Field expedition, in which 48 female athletes trekked to the summit of Mount Kilimanjaro via the Shira route (5895 m) to set a world record for the highest altitude soccer match. One emergency medicine physician with altitude experience served as Chief Medical Officer along with two wilderness emergency medical technicians. The medical team conducted ‘rounds’ in the mountain camp every evening, determining if anyone had any AMS or other nonAMS symptoms. AMS was defined by the Lake Louise Consensus Scale and the physician’s clinical judgement. Moreover, formal physiological assessments of every participant were conducted throughout the expedition and detailed records were kept. The incidence of AMS was recorded and compared between women who were and were not menstruating during the expedition as well as those women taking and not taking acetazolamide. Relative risk (RR) was used to compare AMS incidence by menstruation status, prophylaxis status, and menstruation and prophylaxis status combined. All analyses were conducted in R Studio V.1.4.1717 with an alpha level of 0.05. Two women were excluded from analyses as one was postmenopausal and one had prior highaltitude exposure, which may have decreased her odds of developing AMS compared to those without exposure. Of the 46 women, 13 (28.3%) developed AMS. Among the 17 menstruating, 6 developed AMS (35.3%) vs 7 of the 29 not menstruating (24.1%), RR 1.5, 95% CI 0.6 to 3.6 (table 1). For the 37 (80.4%) women that took prophylactic acetazolamide therapy, 11 developed AMS (29.7%) vs 2 of the 9 (22.2%) that did not take acetazolamide, RR 1.3, 95% CI 0.4 to 5.0. Of women taking acetazolamide, AMS incidence was 38.5% in those menstruating vs 25.0% of those not menstruating, RR 1.6, 95% CI 0.6 to 4.1. There was no significant difference in AMS incidence between women who were and were not menstruating or any effect of prophylaxis on this relationship. While the sample size is small, this provides evidence against menstruation having a large effect on the development of AMS, mostly contradicting prior literature. Although participants menstruating versus not menstruating were not matched in terms of age, medical history or medication use, we still believe this is an important preliminary study on this topic. It has been suggested that the hormones involved in menstruation may account for women’s susceptibility to developing AMS. Richalet et al observed that womens’ physiological responses to high altitude were related to menstrual cycle phase, with the luteal/midluteal phase being protective against AMS. This is supported by Takase et al, who similarly found that women in the luteal phase at high altitude had an enhanced ventilatory response in hypoxic conditions than those in the follicular phase. Contrastingly, Riboni et al found no difference in AMS incidence, symptom scores or oxygen saturation between women at midfollicular versus midluteal phase in a hyperbaric chamber simulating 15000 ft of altitude. Future research should focus on investigating specific hormonal influences on AMS incidence and performing a larger, similar study with more rigorous controls. Such research may add to the paucity of information on the topic and address discrimination against female athletes in extreme conditions. Megan Elizabeth Paul , Thomas D Wagner, Connor A Tukel, Dana R Levin Medical Education, Icahn School of Medicine at Mount Sinai, New York, New York, USA Medical Education, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA Aerospace Medicine, The University of Texas Medical Branch at Galveston, Galveston, Texas, USA Emergency Medicine, University of Colorado— Anschutz Medical Campus, Aurora, Colorado, USA
Abstract Medical historians and educators have long lamented that the integration of the study of the history of medicine into the educational curricula of medical schools and clinic-based teaching has been protractedly troubled. Employing the development of the history of medicine program at the University of Calgary as a case study, this article emphasizes the importance of integrating medical history with teaching schedules to further students’ insights into changing health care settings, the social contingency of disease concepts, and socio-economic dependences of medical decision-making. History of medicine programs can furnish plentiful opportunities for research training through summer projects, insight courses, and field practica. This article explores the first fifty years of the History of Medicine and Health Care Program in Calgary and considers the impact of interdisciplinary cooperation as well as the role of interprofessional undergraduate and clinical medical education. Through this exploration, I argue that medical history should be a central part of study curricula, that a historical understanding can provide a robust background for physicians in a fast-changing world in the clinic, and that through their disciplinary expertise, medical historians play a fruitful role in scholarly and teaching exchanges with medical students and clinicians in the modern medical humanities.
This research examines the expansion and characteristics of the Korean Army’s chain of medical evacuation in 1948–1953. The most important goal of the chain of medical evacuation was to conserve fighting strength, which cannot be achieved only by sending the sick and wounded to the rear for treatment. It was more important to maintain as many mission-capable wounded soldiers on the frontline. Therefore, triage for conserving strength was the priority in the evacuation process, and military doctors conducting triage played a significant role. Focusing on military doctors, this article studies the instability of the Korean Army’s medical evacuation chain.Although Korea was liberated from Japanese colonial rule in August 1945, Korea had no army or army medical services. With the support of KMAG, the Korean Army was able to build a nationwide evacuation chain during the Korean War. However, the expansion of the medical evacuation chain resulted in instability. At the heart of the instability was manpower, rather than organization and transportation. Koreans had almost no experience with the military medical services before 1948, and during the Korean War, most doctors, who had been conscripted after the outbreak of the war, were not trained as military doctors. Therefore, the Korean Army had no other choice but to conduct medical evacuations using mobilized civilian doctors who were not sufficiently trained as military doctors. The escalating war revealed the problems of civilian doctors in military uniforms. Unlike the goal of the chain of medical evacuation, they easily evacuated patients and were reluctant to release patients to return to their duties. Korean Army doctors who were not sufficiently trained as military doctors struggled between the goals of military medical services and those of medical care. Consequently, the military doctors and the instability of the medical evacuation chain during the Korean War reflect the fundamental tension between war and medicine.
Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In this work, we investigate whether additional information available during the training process can be used to create an understandable and powerful model. We propose an innovative solution called Proto-Caps that leverages the benefits of capsule networks, prototype learning and the use of privileged information. Evaluating the proposed solution on the LIDC-IDRI dataset shows that it combines increased interpretability with above state-of-the-art prediction performance. Compared to the explainable baseline model, our method achieves more than 6 % higher accuracy in predicting both malignancy (93.0 %) and mean characteristic features of lung nodules. Simultaneously, the model provides case-based reasoning with prototype representations that allow visual validation of radiologist-defined attributes.
Heather M. Whitney, Natalie Baughan, Kyle J. Myers
et al.
Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify longitudinal representativeness of the demographic characteristics of the primary imaging dataset compared to the United States general population (US Census) and COVID-19 positive case counts from the Centers for Disease Control and Prevention (CDC). Approach: The Jensen Shannon distance (JSD) was used to longitudinally measure the similarity of the distribution of (1) all unique patients in the MIDRC data to the 2020 US Census and (2) all unique COVID-19 positive patients in the MIDRC data to the case counts reported by the CDC. The distributions were evaluated in the demographic categories of age at index, sex, race, ethnicity, and the intersection of race and ethnicity. Results: Representativeness the MIDRC data by ethnicity and the intersection of race and ethnicity was impacted by the percentage of CDC case counts for which data in these categories is not reported. The distributions by sex and race have retained their level of representativeness over time. Conclusion: The representativeness of the open medical imaging datasets in the curated public data commons at MIDRC has evolved over time as both the number of contributing institutions and overall number of subjects has grown. The use of metrics such as the JSD support measurement of representativeness, one step needed for fair and generalizable AI algorithm development.
Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although biology and quantum theory have arguably been entangled ever since Schrödinger's cat). The initial focus was on biochemical and computational biology problems; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and algorithms are summarized and an outlook on medicine in the quantum era, including technical and ethical challenges, is provided.
Extensive adoption of electronic health records (EHRs) offers opportunities for their use in various downstream clinical analyses. To accomplish this purpose, enriching an EHR cohort with external knowledge (e.g., standardized medical ontology and wealthy semantics) could help us reveal more comprehensive insights via a spectrum of informative relations among medical codes. Nevertheless, harnessing those beneficial interconnections was scarcely exercised, especially in the medication recommendation task. This study proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed) to recommend medicines by inducing knowledge from myriad medical-related external sources upon the EHR cohort and rendering interconnected medical codes as medical knowledge graphs (KGs). On top of relation-aware graph representation learning to obtain an adequate embedding over such KGs, we leverage hierarchical sequence learning to discover and fuse temporal dynamics of clinical (i.e., diagnosis and procedures) and medicine streams across patients' historical admissions to foster personalized recommendations. Eventually, we employ attentive prescribing that accounts for three essential patient representations, i.e., a summary of joint historical medical records, clinical progression, and the current clinical state of patients. We validated the effectiveness of our KindMed on the augmented real-world EHR cohorts, achieving improved recommendation performances against a handful of graph-driven baselines.
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2\% of the parameters. Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.
Omran Mohamed Omran, M. Sherif, Ekram Sadek Saied
et al.
Background: Even in the era of drug-eluting stents, underexpansion of coronary stents remains a prominent cause of in-stent thrombosis and restenosis in patients having percutaneous coronary interventions (PCI). The aim of this work was to evaluate the value of using stent boost (SB) to detect stent under expansion (UE) by comparing this method to the gold-standard method of measurement by intravascular ultrasound (IVUS). Methods: This prospective observational cross-sectional research enrolled 21 cases with chronic coronary artery disease who had elective PCI with IVUS and SB. Every patient was exposed to full history taking, full clinical examination and echocardiography. Pre-stenting IVUS was done to assess lesion characteristics, vessel measurements specifically distal reference lumen diameter and area (distal RLD, RLA) and to assess the size of the needed stent. SB image was obtained helped by the deflated balloon of the immediately deployed stent. IVUS was introduced post-stenting to obviate any hidden complication as well as to assess stent measurements of minimal stent diameter and area (MSD, MSA), hence, identify the group of patients with stent UE for which subsequent high pressure balloon dilatation was done. Post-procedure off-line processing of SB and QCA images to evaluate the presence of UE by both modalities. Results: SB showed good agreement to IVUS regarding MSD which became optimal agreement when done for Xience Xpedition stent (as the commonly used stent in our study). SB was able to detect optimal expansion compared to IVUS with 100% sensitivity and 33.33% specificity (p =0.005, AUC=0.808) at cut-off value criteria of MSD/distal RLD of 70%. The specificity increased to 66.67% when the cut-off value criteria of MSD/distal RLD was 76%. There was less agreement between QCA and IVUS. Conclusions: Stent boost showed good agreement to IVUS regarding MSD which became optimal agreement when done for Xience Xpedition stent (as the commonly used stent in our study). SB was able to detect optimal expansion compared to IVUS with 100% sensitivity and Expedition 66.67% specificity (p =0.005, AUC=0.808) at MSD/distal RLD of 76% as a cut-off value criteria.
Resumo O artigo apresenta o modo como as imagens relativas aos conjuntos de química expressam as representações sobre a predominância do gênero masculino nas brincadeiras que simulam aspectos técnicos, gestuais, de laboratório, criando padrões de conduta que direcionam para a vocação profissional: o ser cientista. Foram privilegiadas as documentações produzidas pelas empresas Gilbert (1920) e Chemcraft (1922), presentes no acervo da Chemical Heritage Foundation (EUA). Discute-se o que Joan Scott chama de organização social da diferença sexual, enquanto são analisadas as ilustrações que privilegiam a dominância do gênero masculino, nas formas de “ser criança”, brincando de cientista.
This paper examines the social life of masks in colonial Korea with a focus on their use in hygienic practices. It argues that masks first appeared in the disease control scene in late 1919 when the Governor-General of Korea belatedly introduced preventative measures against the Spanish Influenza pandemic. Since then, the central and regional hygiene authorities had begun to encourage colonial Koreans to wear masks whenever respiratory disease epidemics transpired. Simultaneously, Korean doctors and news reporters framed mask-wearing as something needed for family hygiene, particularly for trans-seasonal child health care, and advised colonial Korean women to manage and wear masks. This paper also reveals that the primary type of masks used in colonial society was black-colored Japanese respirators. Its design was the main point of contention in the debates on the effectiveness of masks against disease infection. Finally, it also highlights that the wide support of using masks by medical doctors and authorities was not based on scientific evidence but on empirical rules they developed through the pandemic and epidemics. The mask-usage practice would be challenged only when South Korean doctors reframed it as a “Japanese custom not grounded on science” at the height of postcolonial nationalism and the raised concern about the artifact’s usefulness during the Hong Kong Influenza pandemic of 1968.
During the explanation of the origin of ‘prescription,’ an interesting phenomena in the accumulation and diffusion of medical knowledge in the Song Period is that many prescriptions contain narratives with bizarre elements, such as those given by God through dreams, received from ‘strange people,’ or from animals appearing in these dreams. This study features an anecdote called ‘zhiguai (志怪) Medical Cases,’ which contains bizarre elements in the dissemination process of prescription, narrative of the treatment experience, and specific content of prescription, called a ‘zhiguai prescription.’ In previous research, such prescriptions were often called a ‘God-delivered prescription.’ However, a ‘zhiguai prescription’ appears adequate because it includes a number of factors beyond the ‘God-delivered prescription.’ This study examines the background of the intensive emergence of massive zhiguai medical cases in the Song Period, reviews the characteristics and significance of the zhiguai prescriptions in the context of postwar medical history, and finally investigates the influence of the bizarre narrative by tracing the dissemination of related prescriptions. This study found that the zhiguai prescription experiences were different from the so-called ‘academic’ that was formed in the Song Period, and it was ‘another’ method of medical knowledge dissemination based on their narratives. The emergence of many zhiguai medical cases in the Song Period, especially in the Southern Song period, is related to the activities of the literati official. The literati officials of the Song Period frequently witnessed strange or anomalous phenomena in their daily life. They relied on them to relieve the powerlessness of reality and left records. In addition, unlike the authors of the zhiguai genre of the previous era, they maintained an attitude faithful to the facts when recording them. The massive appearance of the zhiguai medical cases in the Song Period was the result of the combination of the intention of the literati official who valued medicine their medical knowledge to spread the awareness, their reliance on the strange or anomalous phenomena, and their attitude that emphasized a realistic narrative. The significance of the zhiguai prescription of the Song Period can be found in the supplementation and diffusion of existing medical knowledge. In previous research, these were collectively described as ‘public experienced methods’; however, various characteristics were found by analyzing the nineteen cases of zhiguai medical cases in Yijianzhi by comparing them with the related contents of the herbal medicine and prescription books of the time. In the use of herbal medicines for specific diseases, there are cases that are unusual or meaningful when compared with existing herbal medicine or prescription books, and thus, this became a decisive basis for the expansion of herbal knowledge in the later period. Moreover, new treatment methods that were not often seen in medical books at the time were introduced, and they have been continuously transmitted to the medical and herbal medicine books since then. Additionally, this study also found cases that were focused on promoting medical knowledge that was not well-known, and the knowledge that must be known, although they were recorded in the existing medical and herbal medicine books. The record of the zhiguai medical cases evidently had its meanings in supplementing and disseminating existing medical knowledge. Prescriptions in the record of the zhiguai medical cases of the Song Period were subsequently recorded in various medical and herbal medicine books, and they handed down until the Ming and Qing period. Later, when a zhiguai prescription was described in a medical book, its bizarre narrative was not omitted, leaving a trace in the name of the prescription. It can be seen that this bizarre narrative served as a decisive opportunity for the prescription to be transmitted later, considering that existing medical books mentioned the related narratives in Yijianzhi as the source for these subsequent transmissions. When discussing the characteristics of the Song Period in Chinese medical history, many studies state that a strong academic medical trend was centered on the pulse and internal medicine, referring to the development of printing technology, the literati official’s interest in medicine, and the compilation of medical books. The contents and dissemination of the zhiguai medical cases of the Southern Song confirm ‘another’ tradition of medical knowledge transmission that relied on the bizarre phenomena and its narratives in Chinese medical history. Its transmission to the Ming and Qing period signifies the continuation of this tradition into later times. The fact that the zhiguai medical cases were later recorded in medical books in the Ming and Qing period clearly shows the dynamism of how knowledge of the ‘case’ affects the knowledge expansion of medicine, thereby revealing the power of ‘another’ tradition called the ‘zhiguai’ narratives.
Chronic diseases can greatly benefit from bioelectronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired low-power implantable processing systems that can be interfaced with biological systems. These circuits, therefore, represent a promising additional tool in the tool-set of bioelectronic medicine. In this paper, we describe the main features of neuromorphic circuits that are ideally suited for continuously monitoring the physiological parameters of the body and interact with them in real-time. We propose examples of computational primitives that can be used for real-time pattern generation and present a neuromorphic implementation of neural oscillators for the generation of sequence activation patterns. We demonstrate the features of such systems with an implementation of a three-phase network that models the dynamics of the respiratory Central Pattern Generator (CPG) and the heart chambers rhythm, and that could be used to build an adaptive pacemaker.
The management of the coronavirus pandemic required huge worldwide vaccination efforts. In this endeavour, healthcare workers faced the twofold challenge of reaching remote areas, and persuading people to take the vaccine. As it happens, this is nothing new in the history of medicine. Health workers may indeed continue to take inspiration from Francisco Xavier Balmis, a Spanish physician of the 19th century who realised the importance of Jenner's vaccine against smallpox, and led a successful expedition to administer the vaccines in the Spanish colonial possessions of the Western hemisphere and Asia. This article presents a biographical sketch of Balmis, focusing on his expedition.