New Journals of Note
- AI in Neuroscience
New in 2025: The only peer-reviewed research journal dedicated to the advancement of artificial intelligence applications in neuroscience. AI in Neuroscience is a multidisciplinary peer-reviewed research journal dedicated to exploring the relationship between artificial intelligence (AI) and neuroscience. Broad in scope, the journal aims to publish high-quality peer-reviewed content concerning the application of artificial intelligence (AI) methods to all areas of neuroscience, including clinical, cognitive and behavioral, computational, systems, and molecular and cellular neuroscience. By fostering collaboration and knowledge exchange, the journal seeks to advance understanding, develop new technologies, and address current challenges and opportunities.
New Articles of Note
- Systematic literature review on the application of explainable artificial intelligence in palliative care studiesThis review of 28 machine learning studies in palliative care found that while complex models offer high accuracy, most lack transparency and consistent use of explainable AI (XAI). Key issues include poor handling of missing data, limited explanation techniques, and a trade-off between performance and interpretability. Available online 8 April 2025.
- Can AI make scientific discoveries?AI technologies excel in fields like drug discovery, medicine, climate modeling, and archaeology through pattern recognition and hypothesis generation. While promising for aiding research, current AI lacks the competence and self-awareness needed for truly independent scientific discovery. Published online March 2025
- Patient consent for the secondary use of health data in artificial intelligence (AI) models: A scoping reviewThis study explores patient consent practices for the secondary use of health data in training AI models, emphasizing the importance of informed consent and social license to ensure ethical standards, data privacy, and public trust. Preprint. Published online June 2025.
- Comprehensive bibliometric analysis of advancements in artificial intelligence applications in medicine using Scopus databaseAI is revolutionizing healthcare by improving efficiency and quality, with a bibliometric analysis of 6900 articles from 2017 to 2024 revealing significant growth in AI research—particularly in deep learning and machine learning—led by the U.S. and China, impacting diagnostics, personalized medicine, and disease management. Published online March 1, 2025 (preprint).
- Bird’s Eye View of Artificial Intelligence in NeuroscienceThis review used bibliometric analysis to explore the growing use of artificial intelligence in neuroscience, highlighting the rise of convolutional neural networks and transformer-based models across various subdisciplines with a doubling time of 4 to 5 years for AI-related publications. Published online March 2025.
New Multimedia of Note
NEJM AI Grand Rounds presents Partners in Diagnosis: ChatGPT, a Mother’s Intuition, and a Doctor’s Expertise with Courtney Hofmann. (November 20, 2024)
In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Courtney Hofmann, a mother whose use of ChatGPT led to her son’s diagnosis of tethered cord syndrome after seeing 17 doctors over three years, and Dr. Holly Gilmer, the pediatric neurosurgeon who confirmed and treated the condition. The conversation explores how AI helped bridge diagnostic gaps, systemic health care challenges that led to missed diagnoses, and the evolving role of AI in patient advocacy and medical practice. The episode highlights the importance of combining AI insights with human medical expertise, while discussing both the potential and limitations of AI in health care.
New Books of Note
- Robot-Proof: Higher Education in the Age of Artificial Intelligence (Revised And Updated Edition) (MIT Press) byISBN: 9780262380935Publication Date: 2024A fresh look at a “robot-proof” education in the new age of generative AI.In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived.Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human.
AI Snake Oil by ISBN: 9780691249131Publication Date: 2024-09-24From two of TIME's 100 Most Influential People in AI, what you need to know about AI--and how to defend yourself against bogus AI claims and products. Confused about AI and worried about what it means for your future and the future of the world? You're not alone. AI is everywhere--and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn't, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil--products that don't work, and probably never will. While acknowledging the potential of some AI, such as ChatGPT, AI Snake Oil uncovers rampant misleading claims about the capabilities of AI and describes the serious harms AI is already causing in how it's being built, marketed, and used in areas such as education, medicine, hiring, banking, insurance, and criminal justice. The book explains the crucial differences between types of AI, why organizations are falling for AI snake oil, why AI can't fix social media, why AI isn't an existential risk, and why we should be far more worried about what people will do with AI than about anything AI will do on its own. The book also warns of the dangers of a world where AI continues to be controlled by largely unaccountable big tech companies
Older and Noteworthy
- AI and Neurology: review articleThe article is a review article that explores how artificial intelligence is transforming neurology through advanced diagnostics, prognostication, and therapy while emphasizing the need for neurologists to address ethical, safety, and generalizability challenges for its responsible integration.
- Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics communityThe National Academy of Medicine (NAM) is developing an AI code of conduct (AICC) to guide ethical and effective AI implementation in health and healthcare, with input from the biomedical informatics (BMI) community. The AICC Steering Committee encourages BMI professionals to contribute to refining and applying these guidelines to address ongoing challenges in the field. Published online February 2025.
- Introducing an Essential 7-Part Artificial Intelligence Review Series: A Guided Journey Into the Future of Pathology and MedicineA 7-part AI review series in Modern Pathology explores the transformative impact of AI and machine learning on healthcare, providing pathologists, clinicians, and researchers with insights into current applications, future trends, ethical challenges, and regulatory considerations in the field. Published online March 2025 (preprint).
- An interdisciplinary perspective on AI-supported decision making in medicineAI-supported medical diagnosis offers the potential to utilize the collaborative intelligence of context-sensitive humans and narrowly focused machines for patients’ benefit. The employment of recommender systems in medicine, however, raises important multi-disciplinary challenges that cannot be addressed in isolation. We discuss three disciplinary perspectives on the topic and their interplay. Ethical issues arise at the level of changing responsibility structures in healthcare. Behavioral issues relate to the actual impact that the recommender system has on physicians. Technical issues arise with respect to the training of an AI model that makes accurate recommendations. We argue that the interaction between physicians and AI recommenders including the concrete design of the interface in which this interaction occurs can only be considered at the intersection of all three disciplines. Published online December 2024
- Implications of Large Language Models for Clinical Practice: Ethical Analysis Through the Principlism FrameworkThe potential applications of large language models (LLMs)-a form of generative artificial intelligence (AI)-in medicine and health care are being increasingly explored by medical practitioners and health care researchers. Published online December 2024
- CPRS: a clinical protocol recommendation system based on LLMsAs fundamental documents in clinical trials, clinical trial protocols are intended to ensure that trials are conducted according to the objectives set by researchers. The advent of large models with superior semantic performance compared to traditional models provides fresh perspectives for research recommendations in clinical trial protocols. Published online December 2024
- Cognitive Bias in Large Language Models: Implications for Research and PracticeA study by Wang and Redelmeier shows that large language models (LLMs), like ChatGPT, are prone to cognitive biases, raising concerns for medical decision-making. Clinicians should critically engage with LLMs, and researchers should focus on strategies for AI-human collaboration. More research is needed to ensure AI complements, rather than replicates, human cognition. Published online November 27, 2024
- Cognitive Biases and Artificial IntelligenceGenerative AI models, increasingly used in medical applications, were tested for human-like cognitive biases in providing medical recommendations. The study found that these biases were generally larger than those seen in practicing clinicians and varied slightly depending on the characteristics of synthetic respondents. Additionally, the extent of bias differed between different generative AI models. The results suggest that generative AI can exhibit cognitive biases similar to humans, and the magnitude of these biases may exceed that of human clinicians. Published online November 27, 2024.
- Rubrics to Prompts: Assessing Medical Student Post-Encounter Notes with AIThis case study at UT Southwestern Medical Center describes the successful deployment of an AI-based grading system for medical students’ post-encounter Objective Structured Clinical Examination (OSCE) notes. The AI system, using a zero-shot GPT-4 framework, reduced human grading effort by 91% and cut turnaround time from weeks to days, achieving up to 89.7% agreement with human expert graders. The study also demonstrates that smaller open-source models, like Llama-2-7B, can be fine-tuned to perform similarly, offering scalability and operational advantages for medical education. Published online November 25, 2024
- Does AI-Powered Clinical Documentation Enhance Clinician Efficiency? A Longitudinal StudyThis study evaluated the impact of Nuance’s Dragon Ambient eXperience (DAX) Copilot, an AI-driven documentation tool, on primary care clinicians at Atrium Health. While no overall improvements in EHR use or financial metrics were found, exploratory analyses showed small reductions in documentation hours for high DAX usage and among low-volume or family medicine clinicians. The results suggest DAX did not significantly improve efficiency for clinicians as a whole, and further research is needed to explore its effects on specific subgroups. Published online November 22, 2024.
- Advancing health coaching: A comparative study of large language model and health coachesThe aim of this study was to compare the quality of responses to clients' sleep-related questions provided by health coaches and an LLM. LLaMA responses were preferred over health coach responses in about 60 % of cases. LLaMA had comparable performance with health coaches across five quality dimensions. LLM (GPT-4)-based evaluation was consistent with experts in accuracy and likelihood of harm. Response length correlated positively with accuracy and empathy, and negatively with readability. Published online October 19, 2024
- The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasonsIn March 2024, the European Union agreed upon the Artificial Intelligence Act (AIA), requiring medical AI-DSS to be ad-hoc explainable or to use post-hoc explainability methods. The ethical debate does not seem to settle on this requirement yet. This systematic review aims to outline and categorize the positions and arguments in the ethical debate. Published online October 1, 2024
- Artificial intelligence-enhanced patient evaluation: bridging art and scienceFindings: Generative AI tools are useful for providing scientific information consistent with the academic standards required of students in written assignments. Published online September 2024.
- MedExpQA: Multilingual benchmarking of Large Language Models for Medical Question AnsweringThe authors present MedExpQA, the first multilingual benchmark for Medical QA. As a new feature, the new benchmark also includes gold reference explanations to justify why the correct answer is correct and also to explain why the rest of the options are incorrect. The high-quality gold explanations have been written by medical doctors and they allow to test the LLMs when different types of gold knowledge is available. Published online September 2024.
- Medical students’ AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instrumentsArtificial intelligence (AI) is becoming increasingly important in healthcare. It is therefore crucial that today’s medical students have certain basic AI skills that enable them to use AI applications successfully. These basic skills are often referred to as “AI literacy”. Published online December 2024 (pre-print available in September 2024)
- How to harness AI’s potential in research — responsibly and ethicallyArtificial intelligence is propelling advances in all areas of science. But vigilance is needed, warn four researchers at the leading edge. Published online August 23, 2024
- Quest for AI literacyAs scientists avidly use, tinker and build with artificial intelligence tools, best practices begin to emerge. Published online August 8, 2024
- Smartwatch interventions in healthcare: A systematic review of the literatureThe review identified 13 interventions using smartwatches in research with middle-aged and older adults, targeting various health conditions, with most studies showing positive outcomes like improved symptom management and medication adherence. However, challenges such as device charging and data synchronization persist, and further high-quality research is needed to fully establish the clinical utility of smartwatches. Published online October 2024 (pre-print available in August 2024).
- The Chief Health AI Officer — An Emerging Role for an Emerging TechnologyNew forms of AI are transforming the health care industry. However, navigating the complex landscape of AI in health care presents challenges. In the past 2 years, some health systems have created a new role, the Chief Health AI Officer (CHAIO), to provide specialized leadership. The CHAIO’s role includes developing a comprehensive AI strategy aligned with the organization’s goals, identifying high-impact use cases, and ensuring effective implementation. Published online June 17, 2024
- Co-creating Consent for Data Use — AI-Powered Ethics for Biomedical AIAs nations design regulatory frameworks for medical AI, research and pilot projects are urgently needed to harness AI as a tool to enhance today’s regulatory and ethical oversight processes. Published online June 14, 2024
- Impact of digital health on the quadruple aims of healthcare: A correlational and longitudinal study (Digimat Study)The article's objectives were to: 1) measure the correlation between digital capability and health system outcomes mapped to the quadruple aim, and 2) measure the longitudinal impact of electronic medical record implementations upon health system outcomes. In press. Available online on June 21, 2024.
- The role of governance in the digital transformation of healthcare: Results of a survey in the WHO Europe RegionOptimal governance is among the key facilitators of the digital transformation of health systems intended to improve access to healthcare, quality, safety, and efficiency, and to attain universal health coverage. This paper highlights the findings of a survey assessing the status of governance of digital health in the WHO European Region. Preprint. To be published September 2024.
- Factors impacting the adoption of big data in healthcare: A systematic literature review"Big data" encompasses the vast volume, variety, and velocity of data from sources like sensors, social media, and online platforms. Its adoption in healthcare offers potential for better patient outcomes, improved efficiency, and data-driven decisions. Despite significant interest, there is a lack of empirical research on the factors affecting its adoption. This review systematically explores these factors in the existing literature. Preprint published online May 20, 2024. To be published July 2024.
- Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applicationsGenerative artificial intelligence (GAI) is revolutionizing healthcare with solutions for complex challenges, enhancing diagnosis, treatment, and care through new data and insights. However, its integration raises questions about applications, benefits, and challenges. Our study explores these aspects, offering an overview of GAI's applications and future prospects in healthcare.Received 6 April 2024, Revised 3 May 2024, Accepted 4 May 2024, Available online 8 May 2024.
- AlphaFold 3 predicts the structure and interactions of all of life’s moleculesIntroducing AlphaFold 3, a new AI model developed by Google DeepMind and Isomorphic Labs. By accurately predicting the structure of proteins, DNA, RNA, ligands and more, and how they interact, we hope it will transform our understanding of the biological world and drug discovery. Published online May 8, 2024
- Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applicationsGenerative artificial intelligence (GAI) is revolutionizing healthcare with solutions for complex challenges, enhancing diagnosis, treatment, and care through new data and insights. However, its integration raises questions about applications, benefits, and challenges. Our study explores these aspects, offering an overview of GAI's applications and future prospects in healthcare.Received 6 April 2024, Revised 3 May 2024, Accepted 4 May 2024, Available online 8 May 2024.
- How artificial intelligence could transform emergency care"Here, we describe the basics of AI, various categories of its functions (including machine learning and natural language processing) and review emerging and potential future use-cases for emergency care. For example, AI-assisted symptom checkers could help direct patients to the appropriate setting, models could assist in assigning triage levels, and ambient AI systems could document clinical encounters. AI could also help provide focused summaries of charts, summarize encounters for hand-offs, and create discharge instructions with an appropriate language and reading level." Preprint, to be published online July 1, 2024.
- Quality assessment of health science-related short videos on TikTok:This scoping review underscores that health science-related short videos on TikTok, assessed predominantly by physicians and medical students using the DISCERN tool, generally exhibit moderate to low quality, with implications for future interventions. Online preprint available April 2024, to be published in June 2024.
- A Case of Artificial Intelligence Chatbot HallucinationDespite the number of potential benefits of artificial intelligence (AI) use, examples from various fields of study have demonstrated that it is not an infallible technology. Our recent experience with AI chatbot tools is not to be overlooked by medical practitioners who use AI for practice guidance. By sharing this experience, we aim to remind clinicians to use AI in conjunction with human experience as well as highlight the assistance AI can provide with creative tasks. Published online April 18, 2024
- How I GPT It: Development of Custom Artificial Intelligence (AI) Chatbots for Surgical EducationHighlights include: Artificial intelligence chatbots allow users to engage with large-language models, OpenAI's new GPTs feature lets users easily create domain-specific chatbots, new GPTs can be built and iteratively developed using natural language, without code, rapid creation and spread of chatbots bring up issues about accuracy and safety, custom chatbots in surgical education show promise; the community should stay adept. Published online April 16, 2024
- Blueprint for trustworthy AI implementation guidance and assurance for healthcare coalition for health AI, v.1.0This summary consolidates insights from virtual meetings sponsored by the Gordon and Betty Moore Foundation, in partnership with the Partnership on AI (PAI) and the Coalition for Health AI (CHAI) Steering Committee. Contributors include experts from various sectors, such as healthcare and technology, representing institutions like University of California Berkeley, Google, Mayo Clinic, and government bodies including the FDA and NIH. Published online April 4, 2024
- Will Generative AI Tools Improve Access to Reliable Health Information?Could generative AI tools help extend access to reliable medical information and public health messaging—and, in turn, address health disparities? And what types of benefits should be demonstrated before AI technologies are introduced in the clinical setting? Published online April 4, 2024
- Artificial Intelligence & Medical Products: How CBER, CDER, CDRH, and OCP are Working TogetherU.S. Food and Drug Administration, white paper published online March 2024
- ‘A landmark moment’: scientists use AI to design antibodies from scratchNature, published online March 19, 2024
- Educational Interventions and their effects on healthcare professionals’ digital competence development: A systematic reviewInternational Journal of Medical Informatics, In press, journal preproof, available March 10, 2024
- Use of Artificial Intelligence Tools for Research by Medical Students: A Narrative ReviewCureus, published online, March 1, 2024
- Public health’s inflection point with generative AIMcKinsey & Company, published online February 28, 2024
- Future of Health: The Emerging Landscape of Augmented Intelligence in Health CareThe report provides an overview of current and future use cases, potential applications of AI, opportunities and risks physicians should be aware of, and underscores the AMA’s commitment to ensuring the physician voice is leading health care AI forward. February 26, 2024
- Using Artificial Intelligence to Improve Primary Care for Patients and CliniciansJAMA Internal Medicine. Published online February 12, 2024.
- Using Artificial Intelligence to Improve Primary Care for Patients and CliniciansJAMA Internal Medicine. Published online February 12, 2024.
- The Perils of Artificial Intelligence in a Clinical LandscapeJAMA Internal Medicine. Published online February 12, 2024.
- Google AI has better bedside manner than human doctors — and makes better diagnosesNature, Published online January 12, 2024
- Delivering on the Promise of AI to Improve Health OutcomesWhite House Brief, published online December 14, 2023.
Includes professional guidelines, policies, and considerations for ethical use of AI in medicine - Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media ForumJAMA Internal Medicine. Published online on April 28, 2023.
- CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimationWith huge patient dataset, AI accurately predicts treatment outcomes. Published online June 14, 2024
Special Issues & Collections
- Special Issue - IEEE CBMS 2022 - Mining healthcare: AI and machine learning for biomedicine
Attracting a worldwide audience, CBMS is the premier conference for computer-based medical systems, and one of the main conferences within the fields of medical informatics and biomedical informatics. CBMS allows the exchange of ideas and technologies between academic and industrial scientists. The proposed special issue aims to mainly attract authors who have submitted papers in CBMS 2022 regarding methods and applications of AI in healthcare; medical expert systems; data mining and knowledge discovery in healthcare; design and development of computer based medical systems using AI; text mining and natural language processing in medical documents; medical imaging and AI; deep learning applications in healthcare; multimodal medical data fusion; signal analysis and processing through AI; intelligent devices and systems for telemedicine. Last update September 3, 2024 - Special Issue - Artificial Intelligence in Medicine (AIME 2023)The 21 st International Conference on Artificial Intelligence in Medicine (AIME 2023) hosted researchers who presented original contributions regarding the development of theory, methods, systems, and applications of artificial intelligence in biomedicine, including the application of AI approaches in biomedical informatics and healthcare organisations. The papers presented and analysed novel AI theories or methodologies aimed at solving problems in this field. Last update August 26, 2024.
- Special Issue - Large language Models for MedicineLarge language models (LLMs) have a great potential to make a positive impact in the medical domain. To embrace the challenges and opportunities in designing, validating and deploying LLMs in a medical context, this special issue seeks submissions of scientific findings from both academia and healthcare industry that present fundamental theory, techniques, practical experiences of LLMs in medicine as well as roadmaps. Last updated July 1, 2024
- Special issue on Human-Centered Artificial Intelligence for One Health, special issue of Artificial Intelligence in MedicineFor this special issue, original and high-quality articles are sought that provide an opportunity to demonstrate the progress of HCAI methods and tools and their application to attain optimal health for the human and the whole ecological environment.
- Education & professionalism in Health Informatics, Special Issue of International Journal of Medical Informatics
New digital technologies such as big data, artificial intelligence or mobile technologies are powerful tools enabling healthcare systems world-wide to adapt to these changes & respond to new threats & opportunities. This issue seeks to highlight innovative approaches & methodologies used for capacity building, education & professionalism in digital health & health informatics. - ChatGPT’s impact on careers in science, Feature Collection in Nature
Generative AI systems, such as ChatGPT, are having a profound impact on how science is done. This ongoing collection explores the different ways these systems are impacting scientists’ working lives. - Special Issue: Medicine and Artificial Intelligence, MedComm – Future Medicine
MedComm – Future Medicine is a peer-reviewed，open access journal that publishes high quality research in medicine and biology on the basis of novelty, timeliness and significance to human health and diseases. It covers the latest advances in biomedical research and healthcare delivery that will address unmet medical needs and transform future medical therapies. These include demonstration of technological innovations (including screening, diagnosis, prevention.
Tools of Note
- OpenEvidenceA leading AI-powered medical information platform. OpenEvidence is "an artificial intelligence system to aggregate, synthesize, and visualize clinically relevant evidence in understandable, accessible formats that can be used to make more evidence-based decisions and improve patient outcomes".
AI Content Hubs
Continuously updated collections of publications and multimedia centered around AI in medicine.
- JAMA - AI in Clinical PracticeThe JAMA Network page on AI in Clinical Practice provides a comprehensive overview of how artificial intelligence is being integrated into healthcare to enhance patient care, streamline workflows, and improve diagnostic accuracy. It highlights various AI applications, discusses current research and advancements, and addresses both the potential benefits and challenges associated with AI in clinical settings.
- AI in Healthcare (NaturePortfolio)A continuously updated collection of articles from the Nature Reviews portfolio that explore opportunities across a range of medical specialties and discuss the related ethical considerations.
Guide Information
Last Updated: Apr 16, 2025 10:39 AM
URL: https://libguides.pcom.edu/ai_medicine
Subjects: Artificial Intelligence