AI healthcare ethics report: safety, bias and oversight
Source-linked evidence on clinical AI safety, diagnostic bias, patient rights and medical-device oversight, updated daily. Coverage counts are signals of attention—not measures of importance, harm or consensus.
Prepared by the ethics.ai evidence desk · automatically refreshed · editorial scope reviewed against the methodology and corrections policy
Tracks clinical decision support, diagnostic tools, patient safety, medical-device regulation and health-data rights. It does not provide medical advice or rate products.
Questions to take into the evidence
Where is clinical performance being independently evaluated?
Which patient groups may face unequal outcomes?
How are regulators defining evidence and accountability?
Selena Gomez and her legal team have released a fiery statement after the “Only Murders in the Building” star was accused of defrauding investors in her mental health startup, Wondermind, in a federal lawsuit filed Thursday. “The allegations that Selena Gomez engaged in any way whatsoever in any purported ‘fraud’ or other wrongdoing are completely […]
The Food and Drug Administration (FDA, the Agency, or we) is issuing a final order reclassifying in situ hybridization (ISH) test systems indicated for use with a corresponding approved oncology therapeutic product (product codes NYQ, MVD, OWE, and PNK), all postamendments class III (premarket approval) devices, into class II (special controls), subject to premarket notification. FDA is also establishing a new device classification regulation, along with the special controls that are necessary t
Background: Patient-facing digital health tools such as mobile health apps, wearables, and digital therapeutics have expanded rapidly and show promise for improving chronic disease management. Despite increasing evidence of effectiveness, health systems and payers continue to face challenges integrating these tools into routine care. Objective: This study examined the decision-making processes of health system and payer leaders regarding the adoption and sustainability of patient-facing digital
Background: Early risk stratification in emergency medical services (EMS) is essential for patients presenting with acute cardiopulmonary symptoms, yet prehospital decision-making at the dispatch stage is often based on limited structured information. Free-text dispatch narratives may contain additional clinical signals, but their role in early risk assessment remains insufficiently characterized. Objective: This study aims to develop and temporally validate a natural language processing–assiste
Background: The exponential expansion of biomedical literature has created an urgent need for efficient methods to recognize and extract population, intervention, comparison, and outcome (PICO) elements—the foundational elements of evidence-based medicine. Objective: This study systematically evaluated 2 complementary approaches for automating PICO recognition and extraction in medical literature: prompt engineering optimization and parameter-efficient fine-tuning (PEFT) of large language models
Background: Although mobile health (mHealth) interventions serve as potential solutions for addressing mental health problems, evidence on whether mHealth interventions targeting physical activity can reduce psychological distress among generally healthy workers is limited. Objective: This study aimed to investigate the effectiveness of a stand-alone smartphone app, which passively monitors physical activity and psychological distress, in reducing psychological distress among workers. Methods: T
This report is assembled automatically from source metadata and keyword classifications. It summarizes what the tracked source fleet published; it does not independently validate every linked claim. Source-fleet growth can inflate historical comparisons. Cite the individual evidence record and original publisher for substantive claims.
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