Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis.

Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of the rapidly developing field of AI-based medical technologies and extract the latest research trend information from the field. We classified 119,553 publications obtained from SCI constructed with the keywords "conventional," "machine-learning," or "deep-learning" and grouped them into 36 clusters, which demonstrated the academic landscape of AI applications. We also confirmed that one or two close clusters included the key articles on AI-based medical image analysis, suggesting that clusters specific to the technology were appropriately formed. Significant research progress could be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Then we tracked recent research trends by re-analyzing "young" clusters based on the average publication year of the constituent papers of each cluster. The latest topics in AI-based medical technologies include electrocardiograms and electroencephalograms (ECG/EEG), human activity recognition, natural language processing of clinical records, and drug discovery. We could detect rapid increase in research activity of AI-based ECG/EEG a few years prior to the issuance of the draft guidance by US-FDA. Our study showed that a citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of rapidly developing AI-based medical technologies.

Misconceptions in the health technology industry that are delaying the translation of artificial intelligence technology into relevant clinical applications.

There is great optimism that artificial intelligence (AI), as it disrupts the medical world, will provide considerable improvements in all areas of health care, from diagnosis to treatment. In addition, there is considerable evidence that AI algorithms have surpassed human performance in various tasks, such as analyzing medical images, as well as correlating symptoms and biomarkers with the diagnosis and prognosis of diseases. However, the mismatch between the performance of AI-based software and its clinical usefulness is still a major obstacle to its widespread acceptance and use by the medical community. In this article, three fundamental concepts observed in the health technology industry are highlighted as possible causative factors for this gap and might serve as a starting point for further evaluation of the structure of AI companies and of the status quo.

Há uma grande expectativa de que a inteligência artificial (IA), ao transformar a medicina, determine melhoras relevantes em todas as áreas da assistência médica, desde o diagnóstico até o tratamento. Simultaneamente, há evidências de que algoritmos baseados em IA já ultrapassaram o desempenho do ser humano em diversas atividades, como, por exemplo, na análise de imagens médicas ou na associação entre sintomas e biomarcadores com o diagnóstico e prognóstico de doenças. No entanto, a defasagem entre o potencial de desempenho das ferramentas ou aplicativos médicos que utilizam IA e sua relevância clínica prejudica bastante a utilização em larga escala desses programas de computadores. Neste artigo, três conceitos básicos da indústria de tecnologia da saúde são sugeridos como possíveis fatores causais para essa dissincronia entre desempenho e utilidade. Tal discussão pode servir como ponto de partida para uma avaliação mais profunda sobre a estrutura e status quo da indústria médica tecnológica atual.

Misconceptions in the health technology industry that are delaying the translation of artificial intelligence technology into relevant clinical applications / Conceitos enviesados na indústria de tecnologia da saúde que retardam a tradução da inteligência artificial em ferramentas clínicas relevantes

Abstract There is great optimism that artificial intelligence (AI), as it disrupts the medical world, will provide considerable improvements in all areas of health care, from diagnosis to treatment. In addition, there is considerable evidence that AI algorithms have surpassed human performance in various tasks, such as analyzing medical images, as well as correlating symptoms and biomarkers with the diagnosis and prognosis of diseases. However, the mismatch between the performance of AI-based software and its clinical usefulness is still a major obstacle to its widespread acceptance and use by the medical community. In this article, three fundamental concepts observed in the health technology industry are highlighted as possible causative factors for this gap and might serve as a starting point for further evaluation of the structure of AI companies and of the status quo.

Resumo Há uma grande expectativa de que a inteligência artificial (IA), ao transformar a medicina, determine melhoras relevantes em todas as áreas da assistência médica, desde o diagnóstico até o tratamento. Simultaneamente, há evidências de que algoritmos baseados em IA já ultrapassaram o desempenho do ser humano em diversas atividades, como, por exemplo, na análise de imagens médicas ou na associação entre sintomas e biomarcadores com o diagnóstico e prognóstico de doenças. No entanto, a defasagem entre o potencial de desempenho das ferramentas ou aplicativos médicos que utilizam IA e sua relevância clínica prejudica bastante a utilização em larga escala desses programas de computadores. Neste artigo, três conceitos básicos da indústria de tecnologia da saúde são sugeridos como possíveis fatores causais para essa dissincronia entre desempenho e utilidade. Tal discussão pode servir como ponto de partida para uma avaliação mais profunda sobre a estrutura e status quo da indústria médica tecnológica atual.