Digitales Auskultationssystem zur Differentialdiagnose von Lungenerkrankungen mittels Machine Learning (DigitaLung); Teilvorhaben: Patientenstudie, Datensammlung und Datenanalyse

Abstract

_Background_ Auscultation is one of the key medical skills in physical examination. Themain problem with auscultation is the lack of objectivity of the findings and great dependence on the experience of the examiner. Auscultation using machine learning and neural networks promises great potential for solving these problems in clinical practice.

_Methods_ A selective search for studies in PubMed was carried out, which revealed the possibilities of machine learning in medical diagnostics.

_Results_ In all the studies identified, significant differences were shown between the respective test groups in favour of artificial intelligence (AI). In addition to the positive study results, the limitations of AI could also be analysed and critically scrutinised.

_Conclusion_ Medical research in the field of artificial intelligence is still in its infancy. The prospects and limitations of AI must be further investigated and require close attention in the collaboration between clinicians, scientists and AI experts. Publicly funded projects such as DigitaLung (a digital auscultation system for the differential diagnosis of lung diseases using machine learning), which aims to improve lung auscultation using AI, will help to unlock the diagnostic benefits of AI for patient care and could improve care in the future.