Zusammenfassung
Die Digitalisierung verändert das gesamte Gesundheitssystem dramatisch. Schlagworte wie künstliche Intelligenz, elektronische Patientenakte (ePA), elektronisches Rezept (eRp), Telemedizin, Wearables, Augmented Reality oder digitale Gesundheitsanwendungen (DiGA) stehen für die bereits stattfindende digitale Transformation. Digital wird real! Der folgende Überblick skizziert den Stand der Forschung und Entwicklung, aktuelle Planungen und bereits laufende Nutzungen digitaler Tools in der Onkologie in der ersten Hälfte des Jahres 2024. Die Möglichkeiten der Nutzung von künstlicher Intelligenz und der Einsatz von DiGAs in der Onkologie werden in dieser Übersicht entsprechend ihrem Entwicklungsstand etwas ausführlicher dargestellt, da sie in der Onkologie bereits einen erkennbaren Nutzen zeigen.
Abstract
Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.
Literatur
Cellina M et al (2022) Artificial intelligence in lung cancer imaging: unfolding the future. Diagnostics 12(11):2644. https://doi.org/10.3390/diagnostics12112644
Esteva A, Kuprel B, Novoa R et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118. https://doi.org/10.1038/nature21056
Chen S et al (2023) Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types. Br J Cancer 129(1):46–53. https://doi.org/10.1038/s41416-023-02262-6
Bulten W, Kartasalo K, Chen P‑HC et al (2022) Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med 28:154–163. https://doi.org/10.1038/s41591-021-01620-2
Nam JG et al (2023) AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology. https://doi.org/10.1148/radiol.221894
Chaunzwa TL et al (2021) Deep learning classification of lung cancer histology using CT images. Sci Rep 11:5471. https://doi.org/10.1038/s41598-021-84630-x
Baek S, He Y, Allen BG et al (2019) Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep 9:17286. https://doi.org/10.1038/s41598-019-53461-2
Robles-Medranda C et al (2023) Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model. Endoscopy 55(8):719–727. https://doi.org/10.1055/a-2034-3803
Yamada M, Saito Y, Imaoka H et al (2019) Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep 9:14465. https://doi.org/10.1038/s41598-019-50567-5
Saldanha OL, Loeffler CML, Niehues JM et al (2023) Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. Npj Precis Onc 7:35. https://doi.org/10.1038/s41698-023-00365-0
Pao JJ, Biggs M, Duncan D et al (2023) Predicting EGFR mutational status from pathology images using a real-world dataset. Sci Rep 13:4404. https://doi.org/10.1038/s41598-023-31284-6
Jeppesen M et al (2023) Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking. Nat Commun 14(1):8283. https://doi.org/10.1038/s41467-023-43681-6
Cai Y et al (2023) Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. Front Oncol 12:1054231. https://doi.org/10.3389/fonc.2022.1054231
Jumper J et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589
Baek M et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373:871–876
Lin Z et al (2023) Evolutionary-scale prediction of atomic level protein structure with a language model. Science 379:1123–1130
Manz CR et al (2023) Long-term effect of machine learning–triggered behavioral nudges on serious illness conversations and end-of-life outcomes among patients with cancer. JAMA Oncol 9(3):414–418. https://doi.org/10.1001/jamaoncol.2022.6303
Placido D et al (2023) A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med 29(5):1113–1122. https://doi.org/10.1038/s41591-023-02332-5
Garriga R et al (2022) Machine learning model to predict mental health crises from electronic health records. Nat Med 28(6):1240–1248. https://doi.org/10.1038/s41591-022-01811-5
Ayers JW et al (2023) Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med 183(6):589–596. https://doi.org/10.1001/jamainternmed.2023.1838
Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies. https://www.thelancet.com/journals/landig/article/PIIS2589-7500(22)00252-7/fulltext
Govindaraj R et al (2023) Assessing patient-reported outcomes in routine cancer clinical care using electronic administration and telehealth technologies: realist synthesis of potential mechanisms for improving health outcomes. J Med Internet Res 25:e48483. https://doi.org/10.2196/48483
Klier K et al (2023) Corrected QT interval (QTc) diagnostic app for the oncological routine: development study. JMIR Cardio 7:e48096. https://doi.org/10.2196/48096
Harbeck N et al (2023) Significantly longer time to deterioration of quality of life due to CANKADO PRO-React eHealth support in HR+ HER2-metastatic breast cancer patients receiving palbociclib and endocrine therapy: primary outcome analysis of the multicenter randomized AGO‑B WSG PreCycle trial. Ann Oncol 34(8):660–669. https://doi.org/10.1016/j.annonc.2023.05.003
Schunn FA et al (2023) Oncologic treatment support via a dedicated mobile app: a prospective feasibility evaluation (OPTIMISE-1). Strahlenther Onkol. https://doi.org/10.1007/s00066-023-02166-7
Changing cancer mindsets: a randomized controlled feasibility and efficacy trial. https://pubmed.ncbi.nlm.nih.gov/37529924/
Kaidar-Person O et al (2023) Evaluating the ability of an artificial-intelligence cloud-based platform designed to provide information prior to locoregional therapy for breast cancer in improving patient’s satisfaction with therapy: the CINDERELLA trial. PLoS ONE 18(8):e289365. https://doi.org/10.1371/journal.pone.0289365
Adam R et al (2021) Can-pain—a digital intervention to optimise cancer pain control in the community: development and feasibility testing. Support Care Cancer 29(2):759–769. https://doi.org/10.1007/s00520-020-05510-0
Digital therapeutic to improve cancer-related well-being: a pilot randomized controlled trial. https://pubmed.ncbi.nlm.nih.gov/37321673/
Trained artificial intelligence (AI) for predicting treatment termination based on patient observations in advanced breast cancer. https://aacrjournals.org/cancerres/article/83/5_Supplement/P6-03-08/718143/Abstract-P6-03-08-Trained-Artificial-Intelligence
One million cancer treatment months (OMCAT). https://clinicaltrials.gov/study/NCT04531995
Optimization of a technology-supported physical activity promotion intervention for breast cancer survivors: results from Fit2Thrive. https://pubmed.ncbi.nlm.nih.gov/34812521/
Saesen R et al (2023) Defining the role of real-world data in cancer clinical research: the position of the European organisation for research and treatment of cancer. Eur J Cancer 186:52–61. https://doi.org/10.1016/j.ejca.2023.03.013
Spahrkäs SS et al (2020) Beating cancer-related fatigue with the Untire mobile app: results from a waiting-list randomized controlled trial. Psychooncology. https://doi.org/10.1002/pon.5492
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Interessenkonflikt
F. Overkamp: CEO OncoConsult Overkamp GmbH I Berlin; CEO onkowissen.de GmbH I Würzburg.
Für diesen Beitrag wurden vom Autor keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.
Additional information
Redaktion
I. Gockel, Leipzig
Hinweis des Verlags
Der Verlag bleibt in Hinblick auf geografische Zuordnungen und Gebietsbezeichnungen in veröffentlichten Karten und Institutsadressen neutral.
QR-Code scannen & Beitrag online lesen
Rights and permissions
About this article
Cite this article
Overkamp, F. Ein Blick in die Nachbardisziplin: eHealth in der Onkologie. Chirurgie 95, 451–458 (2024). https://doi.org/10.1007/s00104-024-02089-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00104-024-02089-8
Schlüsselwörter
- Künstliche Intelligenz
- Digitale Transformation
- Digitale Tools
- Digitale Gesundheitsanwendungen
- Telemedizin