Radiologist's Role in AI in Healthcare

When I was a #radiology resident, I often heard, “One view is no view.” When generating radiology reports for most exam types, one image is no image. Although GPT-4V is an impressive technology, it is important to exercise extreme caution when using it for medical imaging. OpenAI was likely aware that many individuals would test the capabilities of their #GPT4V model on medical images. To their credit, they stated (https://buff.ly/3rAHQvv), "[We] do not consider the current version of GPT-4V to be fit for performing any medical function or substituting professional medical advice, diagnosis, or treatment, or judgment." However, “The Dawn of LMMs” paper (https://buff.ly/46zzgvS) claimed “the effectiveness of GPT-4V in medical image understanding,” where they “conducted a detailed investigation into the application of GPT-4V in radiology report generation” with this being one of the “accurate examples.” Several posts (e.g., https://buff.ly/46M41xR) have identified some of the errors and shortcomings, and here’s my take as an MSK radiologist. As mentioned in the article (https://buff.ly/3MyIgt1) by Pranav Rajpurkar and Matthew Lungren MD MPH, a new generation of generalist medical AI models is coming. In this regard, Microsoft (https://buff.ly/3JmdaE5) and Google (https://buff.ly/3ZXw6zV) have presented some interesting developments. Radiology recently published an article showing, despite limitations, the potential benefit of large multimodal models (LMMs) (https://buff.ly/3POZN1w) with well-balanced commentary by Felipe Kitamura, MD, PhD and Eric Topol, MD (https://buff.ly/3Q7DuFO) that sparked some great discussions initiated by Rick Abramson, MD, MHCDS, FACR (https://buff.ly/3FmBweg). Clinical context and priors will be a part of the #LMMs. After all, that’s how we radiologists practice – every day. If this blog is any indication (https://buff.ly/48Tht4H), you can expect more LMMs to be developed that go beyond the limitations of relying solely on datasets like MIMIC-III (https://buff.ly/3QdlZnr). ⚠ Be aware that there may be some completely nonsensical papers, such as XrayGPT. #AI has the potential to revolutionize medical imaging despite challenges. We need to look beyond efficiency if we want greater clinical adoption of AI and also consider the radiologists' dilemma, as pointed out by Saurabh Jha in his excellent article (https://buff.ly/48CsWpv) - why I also agree with Nina Kottler, MD, MS when she says we should focus on optimizing the teams of radiologists + AI and prioritize education. It is an exciting time to be involved in research and development, but it is crucial for vendors and researchers in this field to have radiologists on their team to ensure that the technology is safe and effective. It's important to ask the right questions, be aware of the limitations, and avoid overhyping the potential of AI in healthcare. #GenerativeAI #GenAI #InteractiveAI #ChatGPT

Key Factors for Implementing AI-Enhanced Clinical Information Systems Successfully Based on industry reports and first-hand observations, it's evident that the integration of Artificial Intelligence (AI) into healthcare is well underway. However, the gap between a successful implementation and an unsuccessful one can be wide. Here are some key factors that maximize the probability of a successful implementation of an AI-enhanced clinical information system. Key Factors for Successful Implementation: 1. Organizational Leadership, Commitment, and Vision 👓 : Leadership buy-in is crucial. A clear organizational strategy for AI needs to be in place to guide the implementation process. 2. Improving Clinical Processes and Patient Care 👩⚕️ : The end goal should be better patient outcomes. Make sure the AI system aligns with this objective. 3. Involving Clinicians in Design and Modification 💻 : Those who will use the system should have input into its design. This ensures relevance and encourages adoption. 4. Maintaining or Improving Clinical Productivity 📈 : The new system should not disrupt workflow. Ideally, it should increase efficiency, perhaps by automating routine tasks. 5. Building Momentum and Support Among Clinicians 🌟 : Early wins can build momentum. Open communication and training are key for securing clinician support. A 🏥 Vignette: Radiology at Hospital X vs Hospital Y ✅ Hospital X: Dr. Smith, head of radiology, involved her team from the start. They pinpointed specific challenges that AI could address. The result: diagnostic accuracy improved, and image reading time dropped by 25%. The department's capacity increased, patient wait times fell, and the team's initial skepticism turned into strong support for the AI system. ⛔ Hospital Y: In contrast, Hospital Y’s administration relied on an external committee with no clinical experience. Dr. Johnson, a senior radiologist, felt sidelined. The system generated multiple false positives, creating bottlenecks and reducing efficiency. The morale dropped, and the project was ultimately abandoned. These contrasting stories underline the importance of each key factor in implementing AI-enhanced clinical systems. Hospital X succeeded due to its thoughtful approach, while Hospital Y serves as a cautionary tale of what can go wrong when these factors are ignored. #HealthcareAI #ClinicalInformatics #Leadership #PatientCare #ImplementationSuccess

Thank you for letting me share my thoughts as I finish the weekend outpatient #MRI list... 😬 😅 At our site the volume of #ARIA studies continues to increase. Subjective comparison with prior studies adds a great deal of time to the day, as does the process of adjudication as two dozen neuroradiologists and trainees are reading these with variable sensitivity and different approaches (e.g. we get SWI and GRE- which do you look at first? Priors are often added much later and addendums are requested. We are asked to compare studies that are standard MRI brain rather than the ARIA protocol- how do you handle this?) We also suffer from difficulties with the required reporting template- a problem not unique to us as I learned moderating the discussion of subject experts at the recent International Neuroimaging Symposium 2025 Ana M. Franceschi, MD PhD Tammie Benzinger. Through the American College of Radiology Neuro Commission Education and Dementia Committees we are tackling this issue. No challenge in radiology has changed my mind about #AI assistance before this. The need for consistent interpretation and reporting is vital here- for patient care and for radiologist sanity. We all want to do it perfectly, but how can we when we - the subspecialty trained "leaders" in the field- can't agree? PS- kudos to icometrix for the foresight to integrate the radiology report with the study output -so helpful. 👏 👏 And as CTO Dirk Smeets is a coauthor of the Radiological Society of North America (RSNA)-ACR Common Data Elements for ARIA, we know it will endure as part of the multidisciplinary, multifaceted collaboration in our efforts to standardize language in #radiology and all of medicine. https://lnkd.in/gwVZPsuW #Alzheimersdisease #ARIA #Patientcare #Neuroradiology #Radiologhy #Neurology #AI #CDE #Structuredreporting #MedicalEducation