Application of artificial intelligence to uplift patient experiences and clinical decision making

Introduction

Artificial Intelligence (AI) is the new buzz around the world with some spectacular applications from personalization of online content, summarization and financial decision support to the generation of text, personas, images and videos (generative or “GenAI”). While it is easy to get disoriented amidst all this hype, there are some very real and impactful applications of AI to the high-stakes domain of healthcare around the horizon. Here we summarize some exciting applications in the foreseeable future.

1. Personalizing Treatment Plans

Generative AI facilitates the development of personalized medicine by:

• Customized Health Promotion: Analysis of both active and background individual digital user data helps generate valuable insights and opportunities of behavior modification with the aim of promoting healthy lifestyles custom-fit to the individual’s environment and goals.

• Bespoke Treatment Plans: Harnessing individual patient data to tailor personalized treatment plans based on unique genetic, physiological and biochemical profiles.

• Drug Discovery & Optimization: Discovery of new drugs and therapies targeted to a user’s genetic and biophysical profile and in-silico testing of molecular structures and predicting their efficacy.

• Digital Twins: All these affordances enable the creation of high and low-fidelity “digital twins” of human users. Just as digital twin technology has revolutionized manufacturing sector by predicting failure modes and forecasting failures well before they occur, human digital twins can recommend early interventions and behavior change leading to healthier lifestyles and disease outcomes.

2. Enhancing Diagnostic Accuracy and Clinical Decision Making

Generative AI models can analyze vast amounts of medical data, including medical images, patient records and genetic and biochemical information to assist in diagnosing conditions with greater precision. For instance:

• Image Analysis: AI algorithms can detect patterns in medical images (e.g. Mammograms, Chest X-rays, MRIs, CT scans) that may be too subtle for the human eye, improving early detection of diseases such as cancer. GenAI has already shown superior accuracy in predicting breast cancer on mammography images as compared to individual radiology physicians.

• Predictive Analytics: By generating predictive models, AI can forecast disease progression and outcomes, aiding clinicians in making informed and just-in-time decisions.

3. Process Mining and Optimization

Routine and repetitive administrative tasks in healthcare can be automated and optimized through AI:

• Automated Documentation: Generating and organizing patient records, clinical notes, and reports, reducing the administrative burden on healthcare professionals.

• Resource Optimization: AI can facilitate forecasting of patient flow and optimization of scheduling to improve resource allocation and reduce wait times. AI can also help optimize inventory and medical supply chains as compared to traditional models which showed some spectacular failures when faced with unpredictable catastrophes like COVID-19 pandemic.

• Enhancing Clinical Human Resource Utilization: Medical human resource is expensive. Over-burdening existing clinicians with repetitive and routine tasks enhance clinical errors and increase the risk of burn-out. Many of these routine tasks can be sourced to AI assistants.

4. Enhancing Patient Engagement and Education

Generative AI tools can improve patient engagement and education by:

• Creating Personalized Content: Generating educational materials tailored to individual patient needs can lead to improved understanding and compliance.

• Virtual Nurses and Assistants: Providing interactive and intelligent support through chatbots and virtual health assistants, addressing patient queries and offering guidance.

5. Supporting Clinical Decision-Making

AI models can assist clinicians by:

• Decision Support Systems: Generating insights and recommendations based on multi-source data analysis, supporting evidence-based, personalized decision-making.

• Risk Assessment: Identifying potential risks and suggesting preventive measures or alternative treatments based on patient data.

• Digital Twins for Clinical Trials: Clinical trials often rely on comparison groups of patients assigned to placebo that are statistically similar to the group receiving treatment. There is evidence to support that in some cases the placebo group can be substituted by a group of matched digital twins for in-silico simulation of clinical trials especially where ethical considerations are critical.

Challenges and Considerations

While generative AI offers significant advantages, there are challenges and considerations to address:

• Data Quality, Privacy and Security: Ensuring that patient data used in AI models is protected and compliant with local and international regulations (e.g., HIPAA).

• Bias and Fairness: Addressing potential biases in AI models to ensure equitable healthcare outcomes across diverse populations.

• Integration with Existing Systems: Minimizing disruptions and costs while seamlessly integrating AI tools into current healthcare infrastructures and workflows.

• Ethical Implications: Navigating the ethical considerations surrounding AI decision-making and maintaining human oversight.