• Künstliche Intelligenz in der Medizin

  • Machine Learning Healthcare Applications: 2018 and Beyond - Daniel Faggella


    Abstract In the broad sweep of AI’s current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years.

    Artificial Intelligence and Machine Learning for Healthcare - sigmoidal


    Abstract Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare.

    Zukunftsmedizin: Wie das Silicon Valley Krankheiten besiegen und unser Leben verlängern will - Thomas Schulz


    Abstract Lange Zeit konnten wir von solchen Durchbrüchen in der Medizin nur träumen. Doch bereits in den nächsten Jahren werden viele dieser Träume Wirklichkeit werden, denn im Silicon Valley wird gerade die Medizin neu erfunden. Mithilfe von Algorithmen, künstlicher Intelligenz und Unmengen an Daten entwickeln Start-ups und Konzerne wie Google, Microsoft, Apple und Co. bahnbrechende Therapien und verblüffende neue Diagnosemöglichkeiten. Thomas Schulz, langjähriger Silicon-Valley-Korrespondent des SPIEGEL, hat Einblicke in die geheimen Forschungslabore erhalten. In seinem Buch zeigt er, worauf Patienten hoffen dürfen, und erklärt, welche Chancen und Risiken die Zukunftsmedizin für jeden von uns birgt.

    Dermatologist-level classification of skin cancer with deep neural networks - Esteva et al.

    Wissenschaftlicher Artikel

    Abstract Andre Esteva et al. used 129,450 clinical images of skin disease to train a deep convolutional neural network to classify skin lesions. The result is an algorithm that can classify lesions from photographic images similar to those taken with a mobile phone. The accuracy of the system in detecting malignant melanomas and carcinomas matched that of trained dermatologists. The authors suggest that the technique could be used outside the clinic as a visual screen for cancer.

    Opportunities and obstacles for deep learning in biology and medicine - Ching et al.

    Wissenschaftlicher Artikel

    Abstract Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

    Your next doctor’s appointment might be with an AI - Douglas Heaven


    Abstract A new wave of chatbots are replacing physicians and providing frontline medical advice—but are they as good as the real thing?