identify problems quickly. The segmentation, registration, and diagnostics analysis from AI can provide a boost over traditional methods. Machine-learning (ML) applied to clinical datasets has the potential to identify and predict disease regretfully missed by human analysis alone.
Early cancer diagnosis can drastically improve the prognosis. In November 2021, Google announced an AI algorithm that had improved the screening of breast cancer in mammograms. AI is showing significant promise in detecting cancers in medical imaging studies.
- Predictive Analysis for Individualized Care
AI techniques can use historical health records and current health data to make prognostic predictions on individual patients or at the population level. This allows cancelable care options to be analyzed based on predictors that can only be resolved after a treatment option has been presented. For example, disadvantaged patients might laugh at the idea of offering them investigational surgical options after receiving physical therapy or opioid (pathway) treatment options; however, they might recommend considering care alternatives if a higher-risk injury appeared on imaging after follow-up.
- Coordination of Care
AI will reduce some of the physical and cognitive burdens placed on health systems and will pave the way for the necessary change in patient care. Through synthetic intelligence, integrated care research programs will be designed to better coordinate overall patient care and make entities accountable to patients for the overall patient experience at the point of patient engagement.
- Robotics and Mechanized Support
AI applications can work effectively in a medical context. For example, adaptive robotics have been developed to improve the balance, mobility, and independence of patients. Hospitals are implementing robotic automation to help with crucial treatment and surgical staff turnover and finally emerge from the pandemic with a summarized care shift of institutional staff well being. As hospitals move from the predominant person providing care model, AI/machine learning and robotics will help to support and augment real-time healthcare delivery.
Benefits of Artificial Intelligence in Healthcare
- Improved Patient Outcomes
Artificial Intelligence leads to earlier diagnoses, personalized treatments, and proactive health interventions. - Reduced Costs
Increased automation of processes, predictive analytics, and people being able to address health situations earlier will mean less unnecessary hospital visits and expensive treatments. - Increased Efficiency
AI will alleviate administrative workloads for clinicians so they can focus more on providing patient care. - Access to Remote Care
Telemedicine and AI-powered virtual assistants will help bring necessary healthcare services to members of underserved communities and areas. - Improving Research
AI can also help researchers to conduct fraud-free clinical trials, drug and device design/discovery, patient monitoring, and epidemiology.
Challenges and Ethics
- Data Privacy and Security
Healthcare data is never trivial. Protecting patient data is of utmost importance because AI systems can potentially collect and analyze very large amounts of personal data. - AI Algorithms/Systems Bias
AI systems that are trained on non-representative datasets will produce biased results (Unequal). It is important to address fairness a priori to utilizing AI. - Integration with Previously Established Systems
Existing systems in healthcare are typically legacy systems, and new AI systems may not operate or integrate with them without additional investments, costs, and/or structural changes. - Regulation and Accountability
If AI causes a problem, then who is responsible? The new regulations will be needed to give clarity on accountability moving forward. - Patient Trust
Patients may be reluctant to accept AI-related diagnoses or treatment recommendations without trust. Moreover, patients need more transparency on the systems’ competence and a clear understanding of previous interactions with similar patients.
Social Examples of AI in Healthcare
IBM Watson Health: Helps with oncology research and patient treatment recommendations.
Google DeepMind: Diagnoses certain eye diseases from retinal scans.