AI holds immense potential for promoting the well-being of each patient within the context of personalised medicine. Here are several ways AI can be successfully applied to achieve this:

Predictive Analytics

AI can analyse vast amounts of patient data, including genetic information, medical history, lifestyle data, and real-time health monitoring data, to predict the risk of developing specific diseases or health complications. This enables early intervention and preventive measures tailored to individual patients based on patterns detected by AI to identify patients at risk or predict patient response to treatment.

When it comes to treating patients, AI algorithms can analyse patient data to create personalised treatment plans. This includes selecting the most appropriate medications, dosages, and treatment schedules based on the patient’s genetics, disease progression, and responses to previous therapies.

To support clinical decision-making on patient treatments, AI-driven clinical decision-support systems provide healthcare professionals with real-time recommendations and insights based on a patient’s specific health data. This assists in making more informed treatment decisions and reduces medical errors. It also ensures that all possible diagnoses and potential treatments have been considered. 

The responsible clinician can also utilise AI-powered wearable devices and sensors that regularly or continuously monitor patients’ vital signs, activity levels, and other health metrics. This data can be analysed to detect early signs of health deterioration or non-compliance with treatment plans, enabling timely interventions and appropriate management of patients’ conditions.

Using such devices can empower patients and give them more control over their treatment. AI-powered chatbots and virtual assistants can also engage with patients to provide education, reminders, and support for medication adherence, lifestyle changes, and overall well-being.

Within the context of clinical research and drug discovery, AI can accelerate drug discovery by analysing molecular data, identifying potential drug candidates, and predicting their efficacy and safety. This leads to the development of more targeted and effective therapies for individual patients but will also restrict licensed indications to specific patient characteristics and biomarkers. 

Natural Language Processing (NLP) algorithms can extract valuable information from unstructured clinical notes and patient records, making it easier to create comprehensive patient profiles and understand their unique needs, as well as make real-world evidence studies based on chart reviews much easier and shorter to conduct.

From the perspective of budget management and healthcare service provision, AI can optimise resource allocation in healthcare systems by predicting patient admissions, identifying bottlenecks in care delivery, and ensuring that patients receive the right level of care at the right time whilst reducing their time at the healthcare facility. It will also be valuable for identifying cost-saving strategies for healthcare providers.

AI can play a role in ensuring ethical and equitable healthcare by helping identify biases in treatment recommendations and access disparities, thereby promoting the well-being of all patients regardless of background. It can also be used to aid Research Ethics decision-makers when considering clinical studies for ethics approvals, as it can be trained on previous clinical trial considerations from an ethical perspective.