  Medicine, including fields in healthcare and life sciences, has seen a flurry
of quantum-related activities and experiments in the last few years (although
biology and quantum theory have arguably been entangled ever since
Schr\"odinger's cat). The initial focus was on biochemical and computational
biology problems; recently, however, clinical and medical quantum solutions
have drawn increasing interest. The rapid emergence of quantum computing in
health and medicine necessitates a mapping of the landscape. In this review,
clinical and medical proof-of-concept quantum computing applications are
outlined and put into perspective. These consist of over 40 experimental and
theoretical studies. The use case areas span genomics, clinical research and
discovery, diagnostics, and treatments and interventions. Quantum machine
learning (QML) in particular has rapidly evolved and shown to be competitive
with classical benchmarks in recent medical research. Near-term QML algorithms
have been trained with diverse clinical and real-world data sets. This includes
studies in generating new molecular entities as drug candidates, diagnosing
based on medical image classification, predicting patient persistence,
forecasting treatment effectiveness, and tailoring radiotherapy. The use cases
and algorithms are summarized and an outlook on medicine in the quantum era,
including technical and ethical challenges, is provided.
