Emerging applications of machine learning and AI for predictive modeling in precision medicine

According to npj Digital Medicine, the editorial by Zorraquin, J., Kvedar, J.C., Bange, M. et al.[1]"summarizes 31 papers showing that precision medicine requires integrating..."[1]The authors write that "laboratory-based video analysis is precise but expensive, limiting access to specialized care."[1] They add that new paradigms focus on small, mobile hardware suitable for any environment, including low-resource settings.[1]The editorial reports that smartphone-based graph convolutional networks now assess Parkinsonian gait asymmetry at home[1]. It also says open-source models transform raw video into interpretable Autism Spectrum Disorder metrics[1].The authors also cite deep learning for real-time ultrasound lesion detection, noninvasive blood count estimation from ocular videos, and cardiovascular risk prediction from echocardiography[1].In cardiology, the editorial says deep learning detects subclinical heart failure from ECGs[1]. It adds that transformer-based models further improve outcome predictions from routine records with reduced bias.[1]Nature's page for the related article collection says these technologies "mark a shift towards personalized, predictive, and participatory healthcare."[2]The same collection page warns that "ethical considerations, data privacy, and equitable access remain key challenges."[2]