Trends & AI Innovations
Trends & AI Innovations
This year’s RSNA showcased innovations which are bound to transform radiology redefining diagnostic precision, workflow efficiency, and cross-specialty collaboration. From multi-modal diagnostic acceleration to AI-powered workflow orchestration, the event underscored how cutting-edge technologies are breaking down silos, enhancing decision-making, and addressing workforce challenges. As AI adoption continues to expand, its role in multi-modality integration, structured reporting, and predictive analytics is shaping a future where radiology is more connected, efficient, and patient-centric than ever before.
I had the pleasure of attending the 2024 Radiological Society of North America annual meeting. Once again around 40k attendees, between healthcare professionals and industry representatives, shared the halls of the McCormick Center. It is an exciting event that offers the opportunity to reconnect, learn about current products and gain a glimpse to what lies ahead. This year’s theme ‘Building Intelligent Connections’ stressed the transformative role that intelligent devices have been playing in healthcare.
AI is igniting a revolution in healthcare spanning from enhancing diagnostic accuracy and impacting workflow efficiency, to reducing time for MRI acquisitions, to reinventing imaging informatics, leveraging multimodal data integration and addressing radiologist’s workforce challenges. While the financial implications of AI adoption emphasize the importance of return on investment from patient, provider and enterprise perspective, the industry is still in the ascending stage of the adoption curve. However, various studies and products are showcasing dramatic potentials, which, given the increasing costs and diminishing availability of resources, will undoubtably lead to the recognition of the value that can be garnered by adopting the newer technologies.
RSNA: Key Observations and Trends
There were many technological advancements in AI that pervaded all aspects of medical devices and software solutions. These included
- Human-AI Synergy: The widening adoption and impact of AI are seeing as combination of human and machine intelligence can lead to connections to augment the radiologist capabilities while predicting patient risks and implement preventive measures, leading to a new era of personalized medicine.
- Multi-Modal Solutions: The trend toward multi-condition, multi-modality AI solutions represents a significant shift in the medical imaging AI landscape. This includes diagnostic acceleration through AI in medical imaging, particularly in triage and point-of-care scenarios, enabling faster decision-making and improved patient outcomes across various imaging modalities and clinical conditions.
- Reporting: Workflow optimization continues to deliver significant improvements in efficiency and quality, enhancing the radiologist’s ability to generate accurate and comprehensive reports.
- Orchestration: Platform market represents a critical component in the scaling of AI adoption in medical imaging, with continued evolution toward more comprehensive and sophisticated solutions.
The Widening Adoption and Impact of AI
Amongst the many plenary sessions, two of them stand out as the presenters emphasized the benefits of combining human and AI capabilities to create connections that can augmented the radiologist capabilities as well as predicting patient risks and suggesting preventive measures. These aspects are harbingers of a new era of personalized medicine.
President Address
RSNA President Curtis P. Langlotz, MD, PhD, emphasized the combination of human and machine intelligence, advocating for forming intelligent connections to lead radiology into an exciting future. During the presentation, Dr. Langlotz emphasized how AI can amplify human expertise, streamline workflows, and foster deeper interpersonal connections within healthcare. Acknowledging early concerns about AI, Dr. Langlotz pointed out that growing familiarity has shifted perceptions, underscoring that machine intelligence is complementary, not superior, to human intelligence. He advocated for universal electronic image exchange, accessible de-identified data for AI research, and diverse, interdisciplinary teams to design equitable and transparent AI models. Concluding on a hopeful note, he argued that by automating routine tasks, AI can alleviate burnout, enabling radiologists to focus on meaningful patient care and strengthening the human connections that are central to healthcare.
AI’s Transformation of Medicine
Eric Topol, a cardiologist and executive vice-president of the Scripps Research Translational Institute, discussed how AI provides the power to predict patient risks and implement preventive measures. Dr. Topol discussed the transformative potential of artificial intelligence (AI) in medicine. He emphasized the shift from unimodal AI, which analyzes a single data type, to multimodal AI capable of integrating diverse data sources—such as electronic health records, genomics, and environmental factors—to create comprehensive patient models. This evolution enables personalized medical forecasting, early disease detection, and tailored preventive strategies. Dr. Topol highlighted studies where AI predicted breast cancer years before manifestation and assessed pancreatic cancer risk using electronic health records. He underscored the importance of transparency and trust in AI’s integration into clinical practice, noting that radiologists’ expertise is crucial in leveraging AI’s full potential to usher in a new era of individualized medicine
Transformative Trends: AI Reshaping Medical Imaging
Multi-Modal Diagnostic Integration & Acceleration: Breaking Down Silos in Healthcare
The evolution of AI in medical imaging has taken a quantum leap, transitioning from single-modality applications to comprehensive, multi-modal diagnostic solutions. In this section, we review the role of AI enabling multimodal integration, its impact on patient care, and complete with a detailed review on companies that are bringing multi-modal solutions to the market.
We further characterize these companies according to their impact in: Automated Triage Prioritization, Cross-Modality Delivery, Real-Time Clinical Decision Support, Structured Reporting Enhancement, and Predictive Analytics and Risk Stratification
Enabling Multi-Modality Integration
AI is the linchpin that enables multi-modal integration, addressing critical challenges like:
- Data Fusion: AI algorithms analyze and integrate heterogeneous datasets to provide a unified diagnostic report.
- Real-Time Insights: AI-powered platforms offer instant correlation of findings from different modalities, allowing clinicians to act faster in time-sensitive cases.
- Standardized Reporting: AI compiles structured reports that harmonize findings across modalities, reducing variability and improving communication among care teams.
Impact to Patient Care
The integration of multi-modal diagnostics with AI is transforming healthcare by:
- Enhancing Diagnostic Accuracy: Combining modalities reduces false negatives and provides a fuller picture of disease states.
- Streamlining Workflows: AI eliminates redundancies by linking multiple data sources into a single, actionable platform.
- Improving Outcomes in Complex Cases: Particularly in oncology, cardiology, and neurology, multi-modal AI solutions are enabling earlier detection, better staging, and more effective treatment plans.
Companies Delivering AI Innovation
The paradigm of diagnostic imaging is shifting from siloed, single-modality approaches to integrated, multi-modal solutions. This shift, fueled by advancements in AI, is enabling clinicians to synthesize data from multiple sources—including CT scans, MRIs, PET scans, ultrasounds, and even clinical records—into a cohesive diagnostic narrative. The result is a holistic, patient-centered approach to diagnosis and care.
Below we examine key innovations in multi-modal integration enabled by AI, with respect to (1) automated triage prioritization, cross modality analysis, highlighting contributions from emerging companies in the field:
1. Automated Triage Prioritization
AI algorithms automatically prioritize cases based on the severity of findings, enabling faster response times for critical conditions. Below are companies’ solutions that focus on Triage Prioritization. (Note that some of these companies appear under different categories).
Solution: AI-powered tool for chest X-rays and CT scans to detect critical conditions such as pneumothorax, pulmonary embolism, and fractures.
Result: Reduced time to diagnosis for emergencies by flagging high-priority cases.
Solution: AI models for chest X-rays and CTs, focusing on tuberculosis and lung abnormalities.
Result: Improved diagnostic turnaround times, particularly in underserved areas.
Solution: Acute triage solutions for neurovascular conditions (e.g., strokes) using CT and MRI imaging.
Result: Real-time flagging of large vessel occlusions significantly reduced delays in treatment.
Solution: AI system for prioritizing detection of lung nodules in thoracic CT and mammography cases with suspected malignancies.
Result: Enhanced workflow efficiency by focusing radiologists on urgent cases.
2. Cross-Modality Delivery
AI systems integrate data from various modalities to offer a holistic view of patient conditions, improving diagnostic accuracy. Below are companies’ solutions that focus on multi-modal platform integration.
Solution: Multi-modal platform integrating X-rays and MRI for musculoskeletal injuries.
Result: Enhanced detection rates for fractures and soft tissue damage.
Solution: Combines MRI and ultrasound imaging for liver disease assessment, integrating metabolic and structural insights.
Result: Improved diagnosis and personalized treatment strategies for liver conditions.
Solution: Lung cancer detection using CT and PET data, combining anatomical and metabolic insights.
Result: Improved early-stage cancer identification rates.
Solution: Combines mammography and ultrasound imaging for breast cancer diagnostics.
Result: Increased detection rates of early-stage cancers, with fewer false positives.
Solution: Combines mammography and ultrasound imaging for breast cancer diagnostics.
Result: Increased detection rates of early-stage cancers, with fewer false positives.
3. Real-Time Clinical Decision Support
AI provides immediate insights from multi-modal data, aiding clinicians in faster, evidence-based decisions. Below are companies’ solutions that focus on Real-Time clinical decision support.
Solution: Neurovascular care platform integrating CT and MRI to identify strokes and aneurysms in real-time.
Result: Significantly reduced door-to-treatment times for neurovascular emergencies.
Solution: AI-powered platform for cardiovascular and stroke diagnostics, integrating imaging and clinical records.
Result: Improved coordination between clinicians and faster decision-making in critical cases.
Solution: Real-time analysis for neurovascular and cardiopulmonary emergencies using multi-modal imaging.
Result: Faster identification of critical conditions and optimized resource allocation.
Solution: AI systems supporting breast and lung cancer diagnostics by correlating mammography with other imaging modalities.
Result: Enhanced decision-making during tumor board reviews.
Solution: AI tool for multi-modal lung cancer diagnostics, integrating chest X-rays and CTs.
Result: Faster risk stratification and case prioritization.
4. Structured Reporting Enhancement
AI automates the generation of standardized, comprehensive reports from multi-modal imaging data, reducing variability. Below are companies’ solutions that focus on enhancing structured reporting.
Solution: AI-powered structured reporting for mammography and ultrasound, streamlining breast cancer diagnosis.
Result: Improved report standardization and reduced reporting times.
Solution: AI-enhanced reporting for chest X-rays and CT scans, focusing on clearer nodule visualization.
Result: Increased accuracy and confidence in lung cancer diagnoses.
Solution: Neurological AI platform generating structured reports from brain MRI and CT data for MS and Alzheimer’s disease.
Result: Consistent reporting across clinical sites and faster diagnostic insights.
Solution: MRI brain volumetry and lesion quantification for dementia and MS patients.
Result: Automated generation of high-quality reports for neurologists.
Solution: Automated reports integrating PET and MRI for oncology and neurology diagnostics.
Result: Faster delivery of comprehensive imaging assessments.
5. Predictive Analytics and Risk Stratification
AI analyzes multi-modal data to predict disease progression and stratify patient risk, enabling personalized care. Below are companies’ solutions that focus on predictive analytics and risk stratification.
Solution: AI platform combining MRI and ultrasound for liver disease risk prediction.
Result: Early identification of high-risk patients for personalized treatment.
Solution: Predictive AI for glioblastoma using MRI data to assess tumor response to treatment.
Result: Enhanced monitoring of therapy efficacy.
Solution: Multi-modal platform predicting cancer progression using CT, PET, and MRI data.
Result: Faster, more accurate prognosis for oncology patients.
Solution: Cardiac risk prediction using MRI and CT imaging combined with clinical data.
Result: Improved risk stratification for heart disease.
Solution: Predictive models for lung cancer progression using CT and chest X-rays.
Result: Early detection of changes in patient status.
Looking Ahead
AI-driven multi-modal diagnostic integration is transforming medical imaging into a more connected, precise, and patient-centered ecosystem. By combining data from CT, MRI, PET, ultrasound, X-ray, and clinical records, AI enables faster diagnoses, improved treatment planning, and enhanced patient outcomes.
Innovators like Qure.ai, Annalise.ai, Lunit, RapidAI, Aidoc, Viz.ai, Perspectum, iCAD, Avicenna.ai, and Coreline Soft are leading this shift, offering solutions that streamline workflows, reduce diagnostic uncertainty, and improve cross-specialty collaboration. With AI automating triage, integrating modalities, standardizing reporting, and predicting disease progression, healthcare is becoming more efficient, proactive, and personalized.
As AI adoption grows, seamless integration into hospital infrastructure, regulatory alignment, and expanded interoperability will drive the next wave of innovation. The future of medical imaging is clear: AI-powered multi-modal solutions will redefine diagnostics, accelerate precision medicine, and elevate global healthcare standards.
