{"id":368884,"date":"2023-11-12T14:00:00","date_gmt":"2023-11-12T13:00:00","guid":{"rendered":"https:\/\/medizinonline.com\/?p=368884"},"modified":"2023-11-17T15:39:05","modified_gmt":"2023-11-17T14:39:05","slug":"more-than-a-glance-into-the-crystal-ball","status":"publish","type":"post","link":"https:\/\/medizinonline.com\/en\/more-than-a-glance-into-the-crystal-ball\/","title":{"rendered":"More than a glance into the crystal ball"},"content":{"rendered":"\n

Personalized medicine refers to diagnostics and treatment tailored precisely to the individual patient, which also makes use of digital technologies and artificial intelligence (AI). This is a forward-looking trend, especially in dermatology. Digital and AI-supported solutions are becoming increasingly important and are valuable technical aids, but experts agree that they will not replace dermatologists in the future.<\/strong><\/p>\n\n\n\n

In a report published this year in Journal of the European Academy of Dermatology and Venereology <\/em>Prof. Roderick Hay, professor emeritus and former chairman of the International Foundation for Dermatology<\/em> when asked what the biggest challenge in dermatology will be in the next 10 years: “Reconciling a range of AI-based decision-making techniques with the requirements of a humane and patient-centered healthcare system” [1]. When asked what the next breakthrough will be in his specialist field of cutaneous infections, Prof. Hay mentioned the widespread introduction of antigen and molecular-based point-of-care tests, which will largely replace conventional laboratory tests and should enable rapid and reliable diagnosis in all settings. <\/p>\n

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AI as the right hand of dermatologists <\/h3>\n\n

In an article published last year in the Journal of Clinical Medicine<\/em> on current developments and future trends in the field of artificial intelligence (AI) in dermatology, the conclusion was that AI cannot replace dermatologists, but makes everyday practice easier by optimizing patient care [2]. According to the authors [2], this requires a willingness to engage with AI technologies and acquire the relevant expertise. One area of AI application in which a very high level has already been reached is digital image recognition and analysis. However, the authors emphasize that this is only one aspect of clinical diagnostics and treatment and that daily practice is about combining technical achievements with the qualities of the medical professional [2,3]. Compared to humans, AI models require much more data to be trained. Among other things, because they learn statistical associations instead of causal relationships. However, if this is made possible, AI models can deliver extremely precise and reliable outputs [4]. For example, a 360\u00b0 scanner can be used to create a map of all skin changes and patients can be photographed automatically and standardized from head to toe and from all sides in just a few minutes (“body map”) [2]. Special software can precisely compare the images from the different examination times and detect any kind of change using artificial intelligence and present them to the dermatologist for assessment [5\u20137]. <\/p>\n\n

In addition to 3D full-body scanners for diagnosis and follow-up, AI apps that can detect common skin diseases with a high degree of accuracy or smartwatches that can collect various biomarkers are among the more recent AI-supported developments that are driving forward precision medicine and personalized medical care [8]. There is also great potential in increasing the efficiency of administrative processes and the storage and transmission of patient data.<\/p>\n\n

Literature:<\/p>\n\n

    \n
  1. Hay RJ: Pioneers in Dermatology and Venereology: An Interview with Professor Roderick James Hay. JEADV 2023; 37: 18-20. <\/li>\n\n\n\n
  2. Li Z, et al: Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. J Clin Med 2022 Nov 18; 11(22): 6826.www.mdpi.com\/2077-0383\/11\/22\/6826#,<\/a>(last accessed 10\/19\/2023) <\/li>\n\n\n\n
  3. Batbaatar E, et al: Determinants of Patient Satisfaction: A Systematic Review. Perspect. Public Health 2017; 137: 89-101. <\/li>\n\n\n\n
  4. “How AI is improving dermatology”, https:\/\/hub.hslu.ch\/informatik\/wie-ki-die-dermatologie-verbessert,<\/a>(last accessed 19.10.2023) <\/li>\n\n\n\n
  5. Mu\u00f1oz-L\u00f3pez C, et al: Performance of a Deep Neural Network in Teledermatology: A Single-Centre Prospective Diagnostic Study. JEADV 2021; 35: 546-553. <\/li>\n\n\n\n
  6. Coates SJ, Kvedar J, Granstein RD: Teledermatology: From Historical Perspective to Emerging Techniques of the Modern Era: Part I: History, Rationale, and Current Practice. JAAD 2015; 72: 563-566.<\/li>\n\n\n\n
  7. Sinclair R, Meah N, Arasu A: Skin Checks in Primary Care. Aust J Gen Pract 2019; 48: 614-619.<\/li>\n\n\n\n
  8. “Ten theses on digital dermatology”, BVDD, 14.02.2023. <\/li>\n<\/ol>\n\n

    <\/p>\n\n

    DERMATOLOGY PRACTICE 2023; 33(5): 43<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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