The ultimate goal of any healthcare system is to ensure that people remain healthy and thrive outside of clinical settings. In the past, fitness trackers were the pinnacle of AI in wellness-related equipment. However, these technologies have now started to enter clinical care settings. AI is increasingly assisting healthcare professionals in improving health outcomes by enabling precise, faster, and accurate diagnoses, and delivering personalized treatments—all while maintaining a strong focus on patient safety. The integration of AI into healthcare in the recent past holds enormous potential. For example, AI embedded into modalities like MRI, CT, or X-Ray provides real-time feedback to technicians, allowing precise patient positioning to avoid repeat scanning.
Screening, Triaging, and Diagnosis
A number of computational AI algorithms enable the reconstruction of high-quality images with faster scans that use lower dosages, thus minimizing radiation exposure for patients. While diagnostics are central to effective care, challenges persist due to the growth in the use of medical imaging and workforce shortages, potentially leading to incorrect or delayed treatment, thereby causing harm to patients. Intelligent algorithms integrated into the radiology workflow can automatically determine and deliver the right case to the most appropriate available radiologists based on their area of expertise, availability, and current workload. This ensures an efficient balance of caseloads, accelerating reading times and corresponding faster diagnoses and treatments. Higher-risk cases, based on abnormalities, can be prioritized in the review order to ensure the most urgent cases are not missed. AI-enabled solutions can lighten the cognitive burden by analyzing vast amounts of medical data, imaging, and patient histories to provide a summary for healthcare professionals to act upon. Sophisticated computer vision machine learning models can detect patterns and anomalies that may be missed by the human eye, leading to more accurate diagnoses. AI-driven imaging tools can identify early signs of diseases such as cancer, significantly improving early detection and treatment outcomes.
In the future, AI can create personalized treatments by analyzing historical data and current patient information, suggesting care plans for patients while considering allergies and other nuances associated with each case. Predictive analytics play a crucial role in identifying high-risk patients, predicting adverse events, and suggesting proactive interventions. This capability allows healthcare providers to address potential issues before they become critical, thereby improving patient outcomes and safety.
A rapidly growing body of research has demonstrated how AI can have a wide range of useful applications in healthcare, such as the interpretation of chest X-rays, spotting cancer in mammograms, identifying brain tumors in MR images, and detecting arrhythmias in ECGs. AI has also been used to inform cancer treatment recommendations based on a patient’s genetic profile and to predict the likelihood of complications in stroke treatment. With these applications comes the promise of earlier detection of disease, more precise diagnosis, and more personalized treatment—supporting healthcare professionals and patients across the continuum of care.
Discharge and Post-Discharge Surveillance
It is a well-established fact that patients can recuperate faster at home in the company of loved ones. Moreover, patients whose immune systems are already impacted by illness run a higher risk of contracting infections present in the hospital. Ideally, doctors may want to discharge patients as quickly as possible to ensure a speedy recovery and prevent exposure to pathogens in hospitals. This could also ensure the availability of beds for patients next in line. However, determining the right moment to discharge a patient while balancing recovery, safety, re-admission risk, and other considerations is a “tight-rope walking” exercise for treating doctors. Here again, AI-based discharge readiness scores can aid physicians in the discharge process from ICU to ward and from ward to home.
Surveillance of patients post-discharge and risk stratification powered by AI can provide assurance both to doctors and patients, alleviating some of the risks associated with the discharge process.
As a next frontier, AI will help connect previously disconnected and disparate patient data to provide novel insights that support healthcare providers in their decision-making. In cancer care, for example, AI can help integrate information across different clinical domains such as radiology, pathology, EHR systems, and genomics—providing a clear, intuitive view of the patient’s disease state. This can assist multidisciplinary tumor boards in making timely, informed treatment decisions, to give every patient the best chance of a positive treatment outcome. In the future, the intelligent integration of data could give further insight into a patient’s prognosis, supporting the selection of the best care pathway for that particular patient based on an analysis of treatment outcomes for similar patients. As another example of AI supporting precision care, AI can analyze vital signs in acute and post-acute care to help care teams identify patients at risk of deterioration, allowing for timely intervention.
The future of AI in healthcare is promising, with emerging technologies and advancements poised to further reduce errors and enhance patient safety. Advanced predictive analytics, improved AI algorithms, and increased integration of AI in clinical workflows hold the potential to significantly transform healthcare delivery. As these technologies and tools evolve, the continued focus on mitigating diagnostic errors through AI will be crucial in achieving safer and more effective patient care.
To make the most of AI’s impact in healthcare, it is also important to overcome existing challenges around data management, lack of interoperability, and maintaining data standards. These challenges can make it difficult to compile the necessary high-quality data for training AI models, particularly if those models rely on multimodal data from different sources. To overcome these challenges, robust and interconnected platform infrastructures are needed for collecting, combining, and analyzing data at scale. As healthcare becomes increasingly distributed, extending from the hospital to the home, such infrastructures need to cover the entire continuum of care to connect patient data across settings and over time. For example, adopting a robust platform can enable healthcare providers to collect, compile, and analyze data from multiple sources, including medical records, imaging and monitoring data, as well as personal devices and technologies.
Interoperability and standardized data sharing between different hospitals and health systems are key to exploiting the full potential of data and AI. Data should be available in formats that can be shared effortlessly, transparently, and securely. The use of open data standards and semantic interoperability, through methods such as a unified Information Language System, can allow healthcare providers to connect and integrate data in a meaningful way. Finally, adherence to regional legislation and collaboration should enable the secure exchange and access to properly annotated data for AI research and clinical practice while safeguarding patient privacy.
