In this study, the convolutional neural network (CNN) and multipart interactive training were used to create a state-of-the-art classifier for the early detection of cardiac pathologies. The classification was performed using three sets of data samples; the first was recorded using a digital stethoscope and the others were recorded with mobile smart devices. These data were part of the competition on the Kaggle platform and used on the CNN in the form of audio samples as well as in spectrogram format, and experiments were conducted using both methods. Moreover, this method was used not only to distinguish healthy from unhealthy heart rhythm but also to attempt reaching some initial diagnosis. It was possible to identify a set of problems with the dataset, make corrections, and share them with the scientific community. The experiments were conducted using the so-called multi-part interactive training and an additional ResNet pre-trained network, resulting in more than 93% precision. This allows anyone to undertake prophylactical diagnosis using a smartphone alone. It also has a great educational potential for young doctors and students. Greater improvement is possible by supplying more data to the described method.

Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.

Hyperspectral imaging (HSI) is a swiftly developing intraoperative and diagnostic technique in several clinical specialties. By monitoring oxygenation and biochemical markers, it helps with tissue viability, burn depth measurement, wound healing, and tumor detection. HSI facilitates real-time, harmless diagnosis throughout surgeries or outpatient settings, and allows for the detection of tumor boundaries with over 90% accuracy, according to clinical studies. Originally developed for remote sensing and aerospace applications, HSI has rapidly evolved and found increasing relevance across diverse sectors, including agriculture, environmental monitoring, food safety, pharmaceuticals, defense, and especially medical diagnostics. This review explores the origins, development, and expanding applications of HSI, with a particular emphasis on its role in healthcare. It discusses the operational principles and unique features of hyperspectral systems, such as their ability to produce spectral data cubes, perform non-destructive analysis, and integrate with emerging technologies like artificial intelligence and drone-based platforms. By comparing hyperspectral imaging to traditional and multispectral techniques, the review highlights its superior spectral resolution and versatility. Key challenges, including data volume, sensor calibration, and real-time processing, are also addressed. Finally, emerging trends such as miniaturization, integration with the Internet of Things, and sustainable system designs are examined, offering insights into the future directions and interdisciplinary potentials of HSI in both scientific research and practical applications.

People with speech, hearing, or mental impairment require special communication assistance, especially for medical purposes. Automatic solutions for speech recognition and voice synthesis from text are poor fits for communication in the medical domain because they are dependent on error-prone statistical models. Systems dependent on manual text input are insufficient. Recently introduced systems for automatic sign language recognition are dependent on statistical models as well as on image and gesture quality. Such systems remain in early development and are based mostly on minimal hand gestures unsuitable for medical purposes. Furthermore, solutions that rely on the Internet cannot be used after disasters that require humanitarian aid. We propose a high-speed, intuitive, Internet-free, voice-free, and text-free tool suited for emergency medical communication. Our solution is a pictogram-based application that provides easy communication for individuals who have speech or hearing impairment or mental health issues that impair communication, as well as foreigners who do not speak the local language. It provides support and clarification in communication by using intuitive icons and interactive symbols that are easy to use on a mobile device. Such pictogram-based communication can be quite effective and ultimately make people’s lives happier, easier, and safer.

Background: Between 2014 and 2024, Poland underwent a significant digital transformation in its healthcare sector, evolving from isolated initiatives to a cohesive national eHealth ecosystem. This review examines the development, clinical significance, and research trends in telemedicine in Poland, providing comparative insights from 1995 to 2015 and assessing the impact of the COVID-19 pandemic. Methods: A narrative review was conducted using the PubMed, Scopus, EMBASE, and Web of Science databases to identify peer-reviewed articles published between January 2014 and December 2024. A total of 1012 records were identified, and 212 articles were included after applying predefined inclusion criteria. These articles were categorized by medical specialty, study type, COVID-19 relevance, and clinical versus nonclinical focus. Gray literature and policy reports were examined only to provide a context for the findings. Results: Ninety-six publications were included in the clinical studies. The most common specialties are cardiology, psychiatry, geriatrics, general practice, and rehabilitation. In earlier years, survey-based and observational designs were predominant, whereas later years saw an increase in interventional trials and studies enabled by Artificial Intelligence (AI). The COVID-19 pandemic has had a significant impact on research activity, accelerating the adoption of digital technologies in previously underrepresented fields, such as pulmonology and palliative care, as well as in the routine use of modern Internet communication technologies for daily patient–doctor interactions. Discussion: Advancements in digital health (including eHealth and telemedicine) in Poland have been driven by policy reforms, technological advancements, and epidemiological events, such as COVID-19. Various fields have evolved from feasibility studies to clinical trials, and emerging specialties have focused on user experience and implementation. However, the adoption of AI and its interoperability remains underdeveloped, primarily because of regulatory and reimbursement challenges. Conclusions: Poland has made significant strides in institutionalizing digital health; however, ongoing innovation necessitates regulatory alignment, strategic funding, and enhanced collaboration between academia and industry. As the country aligns with the European Union (EU) initiatives, such as the European Health Data Space, it has the potential to lead to regional integration in digital health.