A new artificial intelligence model helps physicians and nurses work together at Stanford Hospital to boost patient care.

Assessment in medical education has evolved through a sequence of eras each centering on distinct views and values. These eras include measurement (e.g., knowledge exams, objective structured clinical examinations), then judgments (e.g., workplace-based ...

From AI to machine learning to training data: A brief glossary of artificial intelligence terms.

ChatGPT’s responses to queries about handling a difficult doctor-patient conversation shows one physician what we can learn from such tools.

A new study shows AI's capabilities at analyzing medical text and offering diagnoses — and forces a rethink of medical education.

Stanford Medicine doctors and researchers are modifying existing chatbots to perform well in a frontier of AI-enhanced medicine: the doctor-patient interaction.

Stanford Medicine study shows that large language models can lend a hand to clinicians in responding to patient email messages.

Stanford Medicine integrates AI-powered listening technology that takes notes for health care providers, allowing them to spend more time with patients and less time on administrative tasks.

Stanford HAI faculty fellow says there’s a better way to make health data useful for patients and clinicians.

A new study led by Stanford Medicine indicates that an AI app can help Type 2 diabetic patients manage their blood glucose levels.

This qualitative study examines issues identified by representatives from different health sector organizations on the development of artificial intelligence and sharing of health data.

Stanford Medicine researchers are helping patients use AI image-generation software as part of a unique study that aims to quantify how creating art aids patients in their recovery.

The POPDx model eliminates the need for large patient datasets, giving it the potential to help patients with uncommon diseases.

Supervised methods for training medical image models aren’t scalable. A new review highlights the potential of self-supervised learning.

Researchers were able to devise an app that can determine which skin lesions are caused by mpox with an accuracy of 90%.

While cardiac sphericity was the focus of Stanford Medicine-led research, the possibility of data science expanding the reach of biomedical science was its true core, researchers say.

Stanford experts examine the safety and accuracy of GPT-4 in serving curbside consultation needs of doctors.

Natural language processing assists pediatricians in drawing the fine line between a teen’s right to privacy and a parent’s right to know.

Hospitals are increasingly adopting AI tools for patient care. They need to be thinking about liability.

This Special Communication presents a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity.

Leaders from health care, industry and government convened virtually to find ways to ensure artificial intelligence improves care for caregivers as well as patients.

As the use of artificial intelligence has spread rapidly throughout the US health care system, concerns have been raised about racial and ethnic biases built into the algorithms that often guide clinical decision making.

Health care providers must reckon with inherent race-based biases in medicine, which can reinforce false stereotypes in algorithms and lead to improper treatment recommendations or late diagnoses.

Decision-support tools for helping physicians follow clinical guidelines are increasingly using artificial intelligence, highlighting the need to remove bias from underlying algorithms.

Stanford health care AI scholar discusses implications of the ability of AI to predict the race or ethnicity of patients, based solely on medical images such as X-rays and ultrasounds.

Stanford Medicine researcher Jonathan Chen discusses the promise and danger of using AI, such as ChatGPT, in medicine.

The ethical impact of AI algorithms in healthcare should be assessed at each phase, from data creation to model deployment, so that their use narrows rather than widens inequalities.

Dean Lloyd Minor on the need to not only learn to employ AI effectively but also to invest in efforts to guide its safe and responsible use

Biomedical ethicist encourages programmers to consider the ethical implications of using AI in health care settings.

New artificial intelligence tools have the potential to revolutionize health care. But Stanford researchers argue that disparities could worsen without intervention now.

Scholars detail the current state of large language models in healthcare and advocate for better evaluation frameworks.

The applications for AI in medicine are being explored deeply at Stanford Medicine and elsewhere. Putting guardrails in place now is crucial.

This new class of models may lead to more affordable, easily adaptable health AI.

Researchers at Stanford Medicine are putting together a guide for principled implementation of artificial intelligence in health care.

The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes.

To realize the benefits of AI in detecting diseases such as skin cancer, doctors need to trust in the decisions rendered by AI. That requires better understanding of its internal reasoning.

Stanford Medicine researchers devise a new artificial intelligence model, SyntheMol, which creates recipes for chemists to synthesize the drugs in the lab.

A new study finds that large language models used widely for medical assessments cannot back up claims.

Doctors worked with a prototype AI assistant and adapted their diagnoses based on AI’s input, which led to better clinical decisions.

Stanford Medicine researchers have developed a powerful new artificial intelligence model that can distinguish between male and female brains.

Wearing a non-invasive, electronic cap that reads the wearer’s EEGs, humans can now command robots to perform a range of everyday tasks.

Scholars develop a new model to surface high-risk messages and dramatically reduce the time it takes to reach a patient in crisis, from 10 hours to 10 minutes.

Generative models dominated the year, as calls for policy and transparency heated up.

A bioengineer is developing computer hardware and software that emulates the way the human brains and nervous system really work.

Digital pathology uses AI to increase the information gleaned from tissue samples and enhance clinical care, teaching and collaboration.

Amid the frenzy over the potential of artificial intelligence to revolutionize medicine, Stanford Medicine is building on experience.

Two of Brian Suffoletto's close friends died in an alcohol-related car accident when he was in college. It helped focus his path in medicine.

Testing drugs “in a dish” using heart and blood vessel cells can speed drug development and reduce health care disparities, cardiologist says.

Four prominent Stanford Medicine neuroscientists weigh in on the chances AI could best the human brain’s intellectual capacity.

Stanford Medicine researchers and leaders discuss the need for medical and health professionals to shape the creation of large language models.

A Stanford researcher questions the need for generalizable models and proposes instead sharing recipes for creating useful local models.

A clinical trial shows an AI algorithm can read echocardiograms accurately and work well with healthcare practitioners.

Stanford Medicine researchers studied how AI can enhance evaluation of cardiac tests in the clinic and found it improved accuracy.

Stanford Medicine researchers are using AI to mine discussion on Reddit to better understand sentiments about statins.

This study aims to assess parents’ awareness of technoference and its harms, the acceptability of AI tools for mitigating technoference, and how each of these constructs vary across sociodemographic factors.