AI in Healthcare

This document, developed by experts at the LHA Trust Funds, informs patients that ambient listening technology may be used during their clinical visit to assist healthcare providers with real‑time clinical documentation and decision‑making. It explains that the technology passively captures conversations, treats all processed data as protected health information, and does not store recordings as part of the medical record. The form outlines how the information will be used, clarifies privacy safeguards, and provides an acknowledgment section for patients to confirm they understand and consent to the use of this technology.

This article provides a yearly overview of how health information technology (Health IT) impacts patient safety. It highlights advancements, ongoing challenges, and real‑world trends in the use of electronic health records, digital communication tools, and data systems.

Explore how artificial intelligence influences diagnostic accuracy—both positively and negatively with this article, as it reviews ways AI can support clinicians by identifying patterns, reducing cognitive load, and catching missed findings. At the same time, it examines new risks introduced by algorithmic behavior, such as overreliance, data quality issues, bias, and errors that could lead to delayed or incorrect diagnoses.

This primer provides a foundational explanation of clinical decision support systems (CDSS) and their role in improving patient safety. The resource is designed to help teams implement and manage CDSS effectively to enhance safety and care quality.

This article discusses how clinicians can clearly explain the role of AI‑assisted tools in patient care, emphasizing transparency, informed consent, and reassurance that technology supports—not replaces—clinical judgment. It underscores the need for transparent communication to maintain patient trust as AI becomes more prevalent in healthcare.

A review of 53 studies to assess how AI tools influence clinical safety. It finds that AI—especially machine‑learning and natural‑language–based systems—can strengthen error detection, patient stratification, and drug safety, though inconsistent reporting and limited real‑world validation remain challenges.

Outlines how AI and machine learning technologies are increasingly used in medical devices to generate clinical insights, explains their potential to improve patient care, and describes the FDA’s evolving approach to regulating these adaptive systems across their lifecycle to ensure safety and effectiveness.

Outlines how AI tools can help clinicians by improving diagnosis, treatment planning, and workflow efficiency, while also highlighting challenges such as data quality, transparency, bias, and regulatory gaps.

Highlights the rapid growth of AI in clinical settings and emphasizes that while AI can enhance decision‑making and patient experience, it also carries risks such as inaccurate outputs, bias, and privacy concerns. 

A report showing that a majority of U.S. adults feel uneasy about clinicians using AI for tasks like diagnosing and recommending treatments, reflecting widespread skepticism about whether AI would improve health outcomes.

Examines how AI has been used across eight major harm domains—such as adverse drug events, infections, diagnostic errors, and patient decompensation—and finds that AI offers significant opportunities for predicting, preventing, and detecting safety events, especially when leveraging novel data sources like wearables, sensors, and computer vision.

This article warns that misused AI can harm human health by enabling manipulation, empowering lethal autonomous weapons, and displacing human labor, and it highlights the potential existential risks posed by self‑improving artificial general intelligence—calling for strong regulation and even a moratorium on high‑risk AI development.

Examines how AI is being used to support clinical risk management, finding that AI can help prevent a wide range of incident types by predicting risks, detecting safety issues earlier, and improving analysis of complex clinical data—while also highlighting the need for standardized methods and careful implementation.

AI‑powered virtual patients are reshaping medical training by offering realistic, interactive simulations that help learners practice clinical reasoning and communication skills in a safe environment. This article highlights both the educational benefits of these tools and the challenges involved in implementing them effectively.

Emerging artificial intelligence technologies are reshaping the skills, competencies, and training approaches needed for future physicians. It discusses the growing need for clinicians to understand AI-driven tools, interpret algorithmic outputs, and collaborate effectively with intelligent systems. The piece emphasizes that medical education must evolve to prepare learners for a healthcare environment increasingly influenced by automation, data‑driven decision support, and digitally augmented clinical practice.

An article that highlights key concerns—including compliance with laws like HIPAA and GDPR, risks of data breaches, limited data availability, and fragmented data storage—and discusses the need for strong safeguards such as robust privacy protections, secure data‑handling practices, and improved data governance to ensure AI can be safely and ethically integrated into healthcare.

This video explains why risk assessments are becoming essential as healthcare organizations adopt AI tools. It highlights how AI increases privacy and security vulnerabilities, emphasizing the need for stronger data‑protection practices, clearer governance, and proactive evaluation of potential risks before implementing AI systems.

This video explores how Kaiser integrates AI into health information management to identify clinical and operational risks. It discusses how AI supports more accurate risk assessment, improves decision‑making, and enhances patient safety while underscoring the importance of responsible oversight and data quality.

Examine how bias in machine‑learning systems can undermine clinical safety and limit the real‑world value of AI in healthcare. It highlights short‑, medium‑, and long‑term risks—ranging from flawed training data and opaque algorithms to unpredictable system behavior—and offers guidance to help clinicians and developers identify and mitigate safety concerns as AI tools advance toward clinical use.

Examines how machine‑learning models can unintentionally reinforce existing racial, gender, and socioeconomic disparities. Using case studies involving ICU mortality and psychiatric readmission predictions, the authors show that variations in clinical notes and unequal model performance across groups can lead to biased outcomes.

Examines whether highly accurate AI programs—such as those capable of detecting cancer cells more effectively than human pathologists—should be used to augment or replace human expertise. The authors highlight key concerns including the “black‑box” problem and automation bias, arguing that while these issues may be less significant from a patient’s perspective, proper evaluation of AI-generated results requires specialized expertise.

Explains how harmful societal inequalities—such as gender and racial disparities in healthcare—can embed bias directly into the datasets used to train AI systems. These biases can then be replicated or amplified by machine learning algorithms, influencing clinical decisions and potentially worsening inequities.

It highlights how biased data and unequal representation can lead to harmful disparities in AI performance and stresses that equitable AI requires deliberate design, robust oversight, and meaningful inclusion—not just check‑the‑box gestures.

An article that outlines ethical guidance for nurses as AI becomes increasingly integrated into patient care. It emphasizes that AI should support—not replace—nursing judgment, stressing the importance of maintaining compassion, protecting human relationships, and ensuring patients’ understanding and trust. 

An article that explores how facial recognition tools introduce significant concerns around privacy, informed consent, data accuracy, and the quality of algorithmic analysis.

Explore both the promise and the ethical challenges of integrating AI into clinical practice. This article highlights AI’s potential to enhance diagnosis, decision‑making, and personalized medicine while also underscoring concerns related to patient safety, privacy, bias, and the risk of undermining patient preferences.

Highlights that although artificial intelligence holds enormous promise for improving healthcare, it also introduces significant ethical concerns. Experts warn that issues such as data privacy, data ownership, the handling of massive sensitive datasets, and the potential for biased or low‑quality training data demand urgent attention. 

The guidance outlines key ethical challenges, six core principles for responsible AI use, and recommendations to ensure AI technologies are governed in ways that protect patients, support health workers, and promote equitable health outcomes

The guidance outlines benefits, such as enhanced diagnostic and educational support, as well as risks including biased outputs, misinformation, automation bias, and cybersecurity vulnerabilities. It provides detailed recommendations for governments, developers, and health institutions to ensure safe, equitable use of LMMs.

The AMA Journal of Ethics article “Making Policy on Augmented Intelligence in Health Care” outlines the American Medical Association’s efforts to shape responsible AI adoption by emphasizing that AI should enhance—not replace—clinical judgment. 

Information that explains that effective medical AI requires a strong ethical foundation, attention to equity and patient autonomy, and careful, long‑term development to build trust among clinicians, regulators, and patients.

Outlines key principles for developing and deploying AI tools safely and ethically in clinical settings. The article emphasizes using population‑representative data, addressing bias, ensuring transparency, prioritizing augmented intelligence, strengthening training and governance, and balancing innovation with patient safety.

An article that outlines AMA policies promoting ethical, transparent, and responsible deployment of AI tools, emphasizing oversight, data privacy, and reducing physician burden. The article also reflects growing physician interest in AI’s potential benefits, alongside ongoing concerns about bias, privacy, and liability in clinical use.

Describes how AI is used to support—not replace—physicians, outlining policies that ensure ethical, transparent, and responsible AI use in health care. It highlights physician enthusiasm for AI’s potential benefits alongside concerns about privacy, oversight, and liability.

Explains the fundamentals of machine learning and how it is being used to support diagnosis, disease management, and medical discovery. It highlights how algorithms analyze patterns in electronic health records to predict risks, identify diseases earlier, and uncover new patient subgroups.

This article emphasize that although AI offers major opportunities to improve efficiency and patient outcomes, its adoption must be balanced with safeguards that address ethical, technical, and operational risks

Experts emphasize that healthcare organizations must establish strong AI governance structures, protect patient data, monitor for biased outputs, integrate “human‑in‑the‑loop” oversight, and invest in staff training to ensure AI systems are used fairly, safely, and effectively.

Describes how augmented intelligence (AI designed to support—not replace—clinicians) can help achieve the “quadruple aim” of improving patient experience, population health, provider work life, and cost efficiency.

This article explains that while AI could greatly improve global healthcare, its rapid growth brings uncertainty, making it essential to embrace the technology responsibly, ethically, and with clear frameworks to manage risks.

This article explains that although machine learning is rapidly expanding in medicine, many AI studies suffer from serious methodological flaws, unreliable data, and lack of validation—creating a growing credibility crisis in medical AI research.

This video explores how AI can improve healthcare through better diagnostics and efficiency, while also warning about risks such as bias, privacy concerns, and the potential for unsafe or unreliable algorithmic decisions.

This video provides an overview of how AI tools can create compliance and documentation challenges in healthcare, explaining the risks they pose for billing, audits, and third‑party payer scrutiny.

This article introduces MINIMAR, a framework defining the minimum information needed to transparently and responsibly report medical AI models—covering data, model design, evaluation, intended use, and potential biases to improve clarity, generalizability, and trust in healthcare AI.

Information that explains that traditional malpractice and product liability laws struggle to account for harms caused by opaque, “black‑box” AI tools. As clinicians increasingly rely on AI recommendations they cannot fully interpret, the article highlights growing uncertainty about who is legally responsible and calls for updated frameworks to address these gaps

Explores how artificial intelligence is transforming healthcare by improving administrative efficiency, supporting clinical decision‑making, and offering significant financial savings for providers and payers. It highlights strong enthusiasm for AI—reflected in high adoption rates and patient optimism—while emphasizing that its rapid expansion also raises important concerns around privacy, security, ethics, and regulatory oversight. 

This article warns that AI’s rapid expansion brings major public health risks, with experts comparing unregulated AI to threats on the scale of pandemics and nuclear events. It highlights how current regulatory efforts are fragmented and stresses the urgent need for stronger legal and policy frameworks to ensure accountability and prevent harmful impacts on public health.

Outlines the key challenges that arise as AI becomes embedded in clinical practice, including patient harm from algorithmic errors, threats to privacy from large‑scale data use, and the dangers of biased or unrepresentative training data. It also highlights potential remedies. 

Discusses how AI’s growing role in healthcare introduces significant new risks, including system malfunctions, heightened privacy and confidentiality challenges, and concerns about the repurposing of patient data. 

An argument  that as AI becomes widely integrated into clinical decision‑making, traditional fault‑based medical liability systems will struggle to fairly assign blame for errors. 

An article that examines how artificial intelligence complicates traditional negligence law by inserting opaque, statistically driven systems between human decision‑makers and the outcomes of their choices.

Emphasizes that as AI adoption grows, healthcare organizations must proactively strengthen data protections and address bias risks to avoid legal exposure.

Ethical and practical considerations of using IBM Watson as a clinical decision‑support tool. Through a leukemia case, it highlights Watson’s potential to rapidly analyze vast medical data and uncover overlooked diagnoses while emphasizing its limitations, the importance of clinician oversight, and the need for transparency when involving AI in patient care decisions. 

How AI’s growing role in care is creating unresolved questions about who is accountable when AI‑influenced decisions lead to harm. Because the law hasn’t caught up with the technology, hospitals and clinicians must navigate uncertainty around responsibility and closely follow emerging legal guidance.

The authors explain that while LLMs may help clinicians gather information or explore diagnostic possibilities, their tendencies to generate inaccurate or non‑transparent outputs raise unique medicolegal concerns. 

This video explains the key risks of adopting AI in healthcare, including privacy concerns, data security vulnerabilities, algorithmic bias, and the potential for errors when clinicians rely too heavily on AI-driven recommendations.

This video highlights both the promise and the difficulties of using AI in medicine—showing how AI can enhance diagnosis and efficiency while also presenting challenges such as bias, data privacy issues, and the need for careful oversight.

This video explores how AI is transforming healthcare by improving diagnostics, speeding clinical workflows, and supporting more personalized patient care, while also addressing the risks—such as data privacy concerns, algorithmic bias, and the potential for errors or over‑reliance on automated systems.

This video breaks down how liability works when healthcare organizations use digital tools and AI‑powered systems. It explains where legal responsibility may fall—whether on clinicians, developers, or health systems—and highlights the importance of transparency, proper oversight, and safe implementation to reduce risk.

This video highlights how AI can improve medical decision‑making and efficiency while also pointing out key challenges such as data quality, bias, privacy concerns, and the need for careful oversight.

Explains how GPT‑4 could support clinicians and patients by answering medical questions and assisting with tasks, while also warning that the model can generate incorrect or misleading responses and therefore must be used cautiously as a supplement—not a replacement—for clinical judgment.

This video outlines the key risks associated with using AI in healthcare and explains how organizations can manage those risks through proper oversight, safety practices, and responsible implementation.

This video explains how Mount Sinai evaluates the risks of AI in clinical care, focusing on patient safety, data quality, bias, and the need for careful oversight before integrating AI tools into healthcare workflows.

Although AI tools are rapidly entering clinical settings and hold promise for improving care, many systems are not yet reliably validated, creating safety concerns related to accuracy, regulation, and potential patient harm.

This article examines the ethical and practical risks of deploying AI triage tools in emergency medicine, highlighting concerns about over‑ and under‑triage, clinician bias, and how to responsibly test and implement such tools in real‑world settings.

This review outlines how AI can improve diagnosis, personalize treatment, and enhance efficiency in health care, while also highlighting major risks such as bias, privacy concerns, and transparency challenges.

This article explains that while AI offers major opportunities for improving healthcare, many hospitals lack the management systems, quality controls, and infrastructure needed to implement it safely and effectively.

A Medscape‑reported article describes how physicians increasingly use AI tools in clinical care but worry that doing so could expose them to malpractice risk, even though no AI‑related malpractice suits have yet occurred.

An article that explains how artificial intelligence is increasingly shaping health care by improving diagnostic accuracy, speeding clinical decision‑making, and expanding access through tools like telemedicine. At the same time, it cautions that AI introduces serious challenges, including data privacy risks, cybersecurity threats, and concerns about biased or unreliable algorithms.

The piece underscores that despite optimism about AI’s role in diagnostics and precision medicine, significant challenges remain—particularly around data quality, adaptability, and true comprehension of complex medical scenarios.