The following are key points to remember from a state-of-the-art article describing how artificial intelligence (AI) applications can improve patient evaluation by combining clinical expertise with technologic advancements:

1. There have been exponential increases in AI technologies in medicine, but incorporation into the traditional framework of history taking and physical exam is limited.
2. Face-to-face patient-provider interactions are important to patients, and this can be maximized through the use of automatic dialogue summarization and intelligent clinical note-taking.
3. Accurate inspection of the patient can be AI-enhanced by the use of frailty and gait analytics that are embedded into existing infrastructure.
4. Physical exam can be accentuated through the use of AI-guided digital health tools, such as smartphone-adapted computer vision models for fundoscopic exam, digital stethoscopes, point-of-care ultrasonography, implantable sensors, and wearable devices.
5. AI-assisted digital health-enabled remote monitoring can inform of clinical changes and provide immediate feedback for both the patient and clinician. These include amplified remote monitoring of fluid status in patients with heart failure, rhythm disturbances, and blood pressure.
6. Expected future use of common data models and large language models for medical record summarization, diagnostic reasoning, and evaluation of clinical trial eligibility is promising.
7. Personalizing patient encounters with use of phenotypes and summarization of large amounts of data through AI applications could be used to improve interpretation of health data, aid decision-making, and provide effective care transition among clinicians.
8. Continuous signals and medical interventions require critical assessment of clinical workflows and continuous feedback systems to prevent AI model performance degradation.
9. Partnerships in health care, academia, and industry are essential for clinical deployment, but must protect patient privacy; federated learning allows secure model training, prompting regulatory adaptations of data privacy laws.
10. The ability to provide equitable AI use includes promoting AI-related literacy, education, critical analysis of AI-generated information, and continuous evaluation of models.
11. To ensure high-performance and high-value AI tools in health care, it is crucial to define their effectiveness, study their real-world impact, integrate them efficiently into clinical environments, use continual learning to remove outdated algorithms, and evaluate them through pragmatic clinical trials considering patient outcomes and safety.

Clinical Topics: Cardiovascular Care Team

Keywords: Artificial Intelligence, Digital Technology, Physical Examination

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