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Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper "Machine Learning that Matters", which highlighted such structural issues in the ML community at large, and offered a series of clearly defined "Impact Challenges" to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare-the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access., Competing Interests: L.A.C. is the Editor-in Chief of PLOS Digital Health., (Copyright: © 2024 Balagopalan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

By making their own exploration decisions, robotic spacecraft can conduct traditional science investigations more efficiently and even achieve otherwise impossible observations, such as responding to a short-lived plume at a comet millions of miles from Earth., (Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

This work presents a method with which to automate simple aspects of geologic image analysis during space exploration. Automated image analysis on board the spacecraft can make operations more efficient by generating compressed maps of long traverses for summary downlink. It can also enable immediate automatic responses to science targets of opportunity, improving the quality of targeted measurements collected with each command cycle. In addition, automated analyses on Earth can process large image catalogs, such as the growing database of Mars surface images, permitting more timely and quantitative summaries that inform tactical mission operations. We present TextureCam, a new instrument that incorporates real-time image analysis to produce texture-sensitive classifications of geologic surfaces in mesoscale scenes. A series of tests at the Cima Volcanic Field in the Mojave Desert, California, demonstrated mesoscale surficial mapping at two distinct sites of geologic interest.

The first observations of extraterrestrial environments will most likely be in the form of digital images. Given an image of a rock that contains layered structures, is it possible to determine whether the layers were created by life (biogenic)? While conclusive judgments about biogenicity are unlikely to be made solely on the basis of image features, an initial assessment of the importance of a given sample can inform decisions about follow-up searches for other types of possible biosignatures (e.g., isotopic or chemical analysis). In this study, we evaluated several quantitative measures that capture the degree of complexity in visible structures, in terms of compressibility (to detect order) and the entropy (spread) of their intensity distributions. Computing complexity inside a sliding analysis window yields a map of each of these features that indicates how they vary spatially across the sample. We conducted experiments on both biogenic and abiogenic terrestrial stromatolites and on laminated structures found on Mars. The degree to which each feature separated biogenic from abiogenic samples (separability) was assessed quantitatively. None of the techniques provided a consistent, statistically significant distinction between all biogenic and abiogenic samples. However, the PNG compression ratio provided the strongest distinction (2.80 in standard deviation units) and could inform future techniques. Increasing the analysis window size or the magnification level, or both, improved the separability of the samples. Finally, data from all four Mars samples plotted well outside the biogenic field suggested by the PNG analyses, although we caution against a direct comparison of terrestrial stromatolites and martian non-stromatolites.