DeepMind Blog
Apr 30
Enabling a new model for healthcare with AI co-clinician
Researching the path to AI-augmented care and development of an AI co-clinician.
"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research
arXiv:2311. 18424v3 Announce Type: replace-cross Abstract: Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together.
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arXiv Machine Learning
Jun 4
Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis
arXiv:2512. 03296v2 Announce Type: replace-cross Abstract: Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams.
arXiv AI
Aug 5
Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era
arXiv:2408. 02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era.
arXiv AI
Jun 2
Algorithmic Authority and the Clinical Standard of Care
arXiv:2606. 00044v1 Announce Type: cross Abstract: The integration of artificial intelligence into clinical medicine creates a fundamental tension between algorithmic probabilistic reasoning and the experiential intuition of expert physicians; applying Lawrence Lessig's \enquote{Code is Law} framework, I argue that the architecture of clinical AI systems already functions as de facto medical regulation, reshaping liability and the standard of care.
arXiv AI
Jul 1
Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
arXiv:2606. 31616v1 Announce Type: new Abstract: Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many Machine Learning (ML) models remain opaque.
arXiv AI
Jun 2
AI From the Margins (AIM): Rethinking Participatory AI Design Through the Lived Experience of Minoritized Communities
arXiv:2606. 01171v1 Announce Type: cross Abstract: Artificial intelligence (AI) can reproduce and amplify the structural inequities faced by minoritized communities.
arXiv AI
Jun 2
OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence
arXiv:2603. 14771v3 Announce Type: replace Abstract: Large Language Model (LLM)-based Collective Intelligence (CI) presents a promising approach to overcoming the data wall and continuously boosting the capabilities of LLM agents.
arXiv Machine Learning
Jun 18
Beyond Algorithms: Conceptual Innovation in Medical Imaging AI
arXiv:2606. 19270v1 Announce Type: cross Abstract: Artificial intelligence has driven rapid progress in medical imaging research, producing increasingly sophisticated algorithms and steady improvements on benchmark tasks.
arXiv AI
Jun 19
AI4SE and SE4AI Exploration: A Decade Looking Back and Forward
arXiv:2606. 19630v1 Announce Type: new Abstract: The March 2020 INCOSE INSIGHT special issue on AI and Systems Engineering (SE) became the most downloaded issue in the publication's history and launched a research community that now draws over 250 registrants to its annual workshop.
arXiv AI
Aug 7
Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics
arXiv:2608. 05656v1 Announce Type: cross Abstract: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies.
arXiv AI
Jun 11
From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Health
arXiv:2606. 11214v1 Announce Type: cross Abstract: Algorithmic fairness is essential for responsible ML-driven public health research, yet its practical implementation remains limited.
arXiv AI
Aug 11
Ethical Framework for Responsible Foundational Models in Medical Imaging
arXiv:2406. 11868v2 Announce Type: replace-cross Abstract: The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning.