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The accelerating power of machine learning in diagnosing disease and in sorting and classifying health data will empower physicians and speed-up decision making in the clinic.
Leveraging the expertise of physicians to identify medically meaningful features in ‘counterfactual’ images produced via generative machine learning facilitates the auditing of the inference process of medical-image classifiers, as shown for dermatology images.
A framework for integrating continuous therapeutic monitoring and the development of AI for clinical care may improve patient and health-system outcomes by tightening feedback loops between patient health, clinical interactions and the development of AI models.
The development of machine-learning systems for safer, robust and fairer outcomes should leverage fine-tuning, generalization, explainability and metrics of uncertainty.
Graph neural networks and transformers taking advantage of contextual information and large unannotated multimodal datasets are redefining what is possible in computational medicine.
Weakly supervised deep-learning models for the analysis of whole-slide images from tumour biopsies perform better at prognostic tasks if the models incorporate context from the local microenvironment.
Deep-learning models trained with images of the external part of the eyes, rather than fundus images of the retina, can also be used to detect severe diabetic conditions, such as diabetic retinopathy.
An efficient protocol for the preparation of DNA libraries for the analysis of methylation patterns in cell-free DNA in plasma enhances the sensitivity of bisulfite sequencing for the early detection of lung cancer.
The proliferation of synthetic data in artificial intelligence for medicine and healthcare raises concerns about the vulnerabilities of the software and the challenges of current policy.
Neuropathologies can be classified, on the basis of post-mortem histopathology and by using machine learning, into six transdiagnostic clusters associated with clinical phenotypes.
A deep-learning model for cancer detection trained on a large number of scanned pathology slides and associated diagnosis labels enables model development without the need for pixel-level annotations.
Accurate and explainable detection, via deep learning, of acute intracranial haemorrhage from computed tomography images of the head is achievable with small amounts of data for model training.
Clinical implementations of machine learning that are accurate, robust and interpretable will eventually gain the trust of healthcare providers and patients.
A deep-learning algorithm enables the real-time video-based recognition of polyps during colonoscopy, with sensitivities and specificities surpassing 90%.
A holographic approach relying on small-molecule chromogens enables a rapid and inexpensive test for the accurate classification of aggressive lymphoma at the point of care.
A microfluidic device for assaying neutrophil motility in blood samples from sepsis patients and a machine-learning algorithm trained with the motility data enable a faster and accurate sepsis diagnosis.
Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.
Stimulated Raman spectroscopy combined with machine learning generates histological images for the rapid diagnosis and classification of brain tumours.