The future of cardiology: Harnessing digital innovation in emergency medicine

• Advanced biomarkers, coupled with dynamic risk models and other digital tools, offer the potential to improve risk stratification, reduce misdiagnosis, and avoid unnecessary hospital admission
• While artificial intelligence (AI) and machine learning hold immense potential to aid clinicians and reduce medical errors, overcoming cultural and systemic barriers is critical to realizing the full benefits of AI
• The focus of AI must remain on enhancing patient care, with technology serving to augment the patient experience by improving  communication through remote monitoring and shared decision-making

The future of cardiology can no longer be considered without the integration of digital solutions. For both cardiovascular and emergency medicine, digital tools are reshaping clinical practice, offering unprecedented opportunities to improve patient care and streamline workflows. In the 12-episode podcast series Cardio Insights, key opinion leaders and subject matter experts explore the transformative potential of these tools, alongside the challenges and cultural shifts required for their adoption. Below, we distill the central themes from these conversations, highlighting actionable insights and future directions for the field.

Emergency departments (EDs) and cardiology clinics face immense pressure to triage patients accurately while managing limited resources. Clinicians on the podcast emphasized that overcrowding and time constraints can lead to overtesting in low-risk patients and under-testing in high-risk cases. Prof. Christopher Baugh of Harvard Medical School, underscores this “blurry decision environment,” with Prof. Martin Than of University of Otago, noting that a certain proportion of ED chest pain cases are misdiagnosed, highlighting the urgent need for tools that refine risk stratification.

Many healthcare professionals, like  Prof. Cynthia Papendick of University of Adelaide Medical School, and Prof. Lori B. Daniels of University of California San Diego, recognize that traditional clinical decision-making pathways are especially challenging in the ED, where physicians must manage the fixed-time measurement of biomarkers in parallel with other priorities. As Prof. Richard Body of Manchester NHS Foundation Trust shares, this is an area where digital health solutions hold a lot of potential, as they enable clinicians to dynamically assess a patient’s risk by moving away from static thresholds that are time-sensitive.

Artificial intelligence (AI) and machine learning emerged as game-changers, particularly for interpreting complex data. Prof. Wolf Hautz of Bern University Hospital, described AI’s potential to reduce diagnostic errors by mitigating cognitive biases: “These tools should broaden clinicians’ perspectives, not just confirm their initial hypotheses.” One example comes from AI-driven ECG analysis, which can detect subtle signs of occlusive coronary artery disease that might be missed and directly excluded by human eyes. The ability of AI solutions to ingest a wide variety of clinical parameters dynamically is also promising. Prof. Hans-Peter Brunner-La Rocca of Maastricht University, and Prof. Evangelos Giannitsis of University of Heidelberg, highlight models that can predict heart disease risk based on several variables in real-time, enabling risk stratification with a unique level of precision.

Beyond clinical decision support, ambient AI technologies generate enthusiasm among several experts, including Prof. C. Michael Gibson of Harvard Medical School. He  shared his optimism toward autonomous note-taking tools, which he sees as a unique opportunity for clinicians to focus more on direct patient interaction: “Imagine a system that transcribes consultations, summarizes key points, and even suggests differential diagnoses—all while maintaining eye contact with the patient.” An opportunity, in a sense, to reposition the patient-doctor relationship at the center of health systems.

Coming from a variety of angles, these clinicians are reaching a consensus: AI has the potential to interpret complex data, reduce diagnostic errors by mitigating cognitive biases, and free up clinician time, even if this potential is not yet a thriving reality.

While the science behind digital tools advances rapidly, adoption lags partially due to cultural and systemic hurdles. Prof. Gibson likens this disconnect to “culture eating strategy for breakfast,” noting that electronic health records (EHRs) often prioritize billing over clinical utility. The adoption of new digital solutions within clinical practice can face skepticism from healthcare professionals who are already exposed to a wide variety of tools that can cause alarm fatigue.

This situation calls for seamless integration of digital solutions as part of medical workflows and current IT environments to flatten the learning curve for clinicians and make the adoption of new technologies smoother. Clinical algorithms should auto-populate patient data and sync with hospital IT systems to avoid manual entry,  a position that echoes Prof. Gibson’s point on ambient listening technologies, mentioned earlier. As Prof. Than frames it: “The perfect tool should make the right decision the easiest choice.”

One thing that the experts on the podcast unanimously agree on is that technology must enhance, not replace, the human element of care. Prof. Gibson advocates for “emotion-based medicine,” where AI aids in patient education and follow-up: “A doctor’s visit shouldn’t end at discharge; continuous, tailored communication is key.” He emphasized shared decision-making, citing potential tools that might weigh patients’ individual preferences (e.g., regarding the risk of a stroke vs. the risk of bleeding) to personalize treatment plans.

Along these lines, remote monitoring and wearables also garnered attention, with Prof. Brunner-La Rocca discussing initiatives that use a “physician avatar” to guide self-care in heart failure patients. Prof. Body envisioned dynamic risk models updated via wearable data to enable proactive interventions, bridging real-world data to daily clinical practice. For these physicians, the future of cardiology and digital innovation in healthcare is moving beyond clinical decision support to serve patients in a continuous, personalized, and more engaging manner across the healthcare journey.

Despite the enthusiasm shared by all guests on the podcast, many challenges remain. Prof. Hautz cautioned against “AI hype,” stressing the need for rigorous validation across diverse populations, and  Prof. Brunner-La Rocca and Prof. Body called for prospective trials comparing AI-driven pathways to standard care, and moving beyond simple observational studies.

Subject matter experts from the industry emphasized multifaceted strategies to bridge gaps between innovation and real-world application in order to drive evidence generation efforts for digital solutions forward. Dr. Matthew S. Prime highlighted the industry’s role in co-developing tools with clinicians and health systems to ensure usability and relevance: “We want to collaborate with the sites that have the academic rigor, space for change, and where you can test products in the actual, real clinical environment. Because what makes sense on a laptop does not necessarily make sense in an emergency department.”

As Sara Zimbardo points out, the task of evidence generation is further complicated by the varying nature of clinical practice across geographies and health systems. Ultimately, the broader regulatory and policy environment needs to align incentives between manufacturers, health systems, and payers. To provide sustainable access to these solutions, there is a need for clearer guidance on best practices, both in terms of implementation and validation, a vision that Dr. Paul Neveux and Dr. Afua Van Haasteren share.

The podcast paints a hopeful vision: digital tools, when thoughtfully designed and implemented, can alleviate clinician burnout, reduce errors, and empower patients. However, success hinges on aligning stakeholders—clinicians, hospitals, developers, and policymakers—around shared goals and incentives. As Prof. Than summarizes, “The future [of cardiology] isn’t about replacing clinicians with AI; it’s about creating systems where both humans and machines thrive.”

By prioritizing patient-centered design, robust evidence generation, and cultural adaptability, the medical community has begun to harness digital innovation to build a more efficient, equitable, and compassionate healthcare system.

For more on the podcast, check out our Cardio Insights