Background: Digital health tools, such as mobile apps and wearable devices, have been widely adopted to support self-management of health behaviors. However, user engagement remains inconsistent, particularly among populations with varying BMI. While digital…
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Accurate detection of lung cancer from Computed Tomography (CT) scans is vital for improving patient survival but remains challenging for deep learning models, which struggle with scale variations of pulmonary nodules and the complex…
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Lung cancer remains the top cause of cancer death, demanding consistent decisions. This clinically oriented review synthesizes computer-aided diagnosis across classical imaging, machine learning, and deep learning, emphasizing bedside-proven advances: multimodal CT/PET–clinical fusion; small-data…
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What You Should Know:
– Qure.ai, a TIME100 most influential company and global leader in digital healthcare, today announced its collaboration with Microsoft. Qure.ai will onboard its end-to-end lung cancer detection, measurement and management suite of…
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Coreline Soft AI powers French national lung cancer pilot and more briefs | MobiHealthNews
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Chris Wood, CEO of Reveal Dx
With lung cancer being the leading cause of cancer-related deaths worldwide, advancements in AI-powered healthcare technology are offering newfound hope. Over the span of the next few decades, AI…
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Efficient and accurate identification of genetic alterations of non-small cell lung cancer is a critical diagnostic process for targeted therapies. Utilizing advanced modern deep learning is a potential solution that can accurately predict genetic…
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Recent advances in unbiased metagenomic next-generation sequencing (mNGS) enable simultaneous examination of microbial and host genetic material. We developed a multimodal machine learning-based diagnostic approach to differentiate lung cancer and pulmonary infections by analyzing…
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Generative artificial intelligence is revolutionizing digital twin development, enabling virtual patient representations that predict health trajectories, with large language models (LLMs) showcasing untapped clinical forecasting potential. We developed the Digital Twin—Generative Pretrained Transformer (DT-GPT),…
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Background: Minimally invasive thoracic surgery has improved lung cancer outcomes but requires enhanced postoperative care. Traditionally, the episodic care model has limited timely and multidimensional monitoring of patients. Recent technological advances in multimodal digital…
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