The trust gap in Healthcare AI

Exec Summary:

The trust gap in healthcare AI is a significant challenge that stems from concerns about its accuracy, transparency, and potential biases. Despite the promise of AI to revolutionise healthcare, there are several factors contributing to this skepticism: ?

1. Lack of Transparency and Explainability:

• Black Box Problem: Many AI algorithms are complex and opaque, making it difficult to understand how they arrive at their decisions. This can lead to mistrust, as patients and healthcare providers may not fully comprehend the reasoning behind AI-driven recommendations. ?
• Bias and Fairness: AI models can inherit biases present in the data they are trained on. This can lead to discriminatory outcomes, particularly for marginalised populations. ?

2. Data Privacy and Security Concerns:

• Patient Data: Healthcare data is highly sensitive and requires stringent protection. Concerns about data breaches and misuse can erode trust in AI systems that handle this information. ?

3. Potential for Errors and Misdiagnoses:

• Limitations: AI is not infallible and can make mistakes. While it can augment human capabilities, it is not a substitute for expert judgment. ?

4. Ethical Considerations:

• Autonomy: Over reliance on AI could diminish the role of human judgment and patient autonomy in healthcare decisions. ?
• Accountability: Determining who is responsible for the outcomes of AI-driven decisions can be complex, raising ethical questions. ?

5. Regulatory and Governance Challenges:

• Lack of Standards: The rapid development of AI in healthcare has outpaced the establishment of clear regulatory frameworks. This can create uncertainty and hinder trust-building. ?

Addressing the Trust Gap: To bridge this trust gap, it is essential to:

• Increase Transparency: Develop methods to explain AI's decision-making processes. ?
• Ensure Fairness and Bias Mitigation: Implement techniques to identify and address biases in AI models. ?
• Strengthen Data Privacy and Security: Implement robust data protection measures.
• Foster Collaboration: Encourage collaboration between AI developers, healthcare providers, and patients to address concerns and build trust. ?
• Develop Ethical Guidelines: Establish clear ethical guidelines for the use of AI in healthcare.
• Invest in Research and Development: Support research to improve AI's accuracy, reliability, and explainability.

By addressing these challenges and fostering trust, healthcare AI can realise its full potential in improving patient outcomes and enhancing the quality of care.

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Oxford University research aims to reduce bias in AI health prediction models

Researchers from Oxford University’s Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) , University College London and the Centre for Ethnic Health Research , supported by Health Data Research UK , have for the first time studied the full detail of ethnicity data in the NHS. They outline the importance of using representative data in healthcare provision and have compiled this information into a research-ready database.

The new study, published in Nature Scientific Data , is the first part of a three-phase project that aims to reduce bias in AI health prediction models which are trained on real-world patient data. The project, which addresses ethnicity disparities that were highlighted during the pandemic, is part of the UK Government’s COVID-19 Data and Connectivity National Core Study led by Health Data Research UK.

The researchers used de-identified data on ethnicity and other characteristics from general practice and hospital health records, accessed safely within NHS England’s Secure Data Environment (SDE) service, via the British Heart Foundation Data Science Centre ’s CVD-COVID-UK/COVID-IMPACT Consortium. This is the first time that patient ethnicity data has been studied at this depth and breadth for the whole population of England. The researchers were able to combine records to analyse patient self-identified ethnicity recorded through over 489 potential codes.

Researchers analysed how more than 61 million people in England identified their ethnicity in over 250 different groups. They also looked at the characteristics of those with no record of their ethnicity, and how conflicts in patient ethnicity data can arise. The data, now available for other researchers to use, shows that 1/10 patients lack ethnicity records, and around 12% of patients had conflicting ethnicity codes in their patient records.

Sara Khalid , Associate Professor of Health Informatics and Biomedical Data Science at NDORMS , explained: ‘Health inequity was highlighted during the COVID19 pandemic, where individuals from ethnically diverse backgrounds were disproportionately affected, but the issue is long-standing and multi-faceted.

‘Because AI-based healthcare technology depends on the data that is fed into it, a lack of representative data can lead to biased models that ultimately produce incorrect health assessments. Better data from real-world settings, such as the data we have collected, can lead to better technology and ultimately better health for all.’

Source: https://www.ox.ac.uk/news/2024-02-22-removing-bias-healthcare-ai-tools#:~:text='Because%20AI%2Dbased%20healthcare%20technology,biased%20models%20that%20ultimately%20produce

Kaiser Permanente's Approach to Building Trust in Healthcare AI

Kaiser Permanente, a large integrated healthcare system, has been at the forefront of adopting AI in healthcare. They have taken several strategic steps to build trust and ensure the ethical and effective use of AI:

1. Transparency and Explainability:

• Explainable AI Models: Kaiser Permanente has invested in developing AI models that can provide clear explanations for their decisions. This helps clinicians understand the reasoning behind AI-driven recommendations and builds trust in the technology.
• Patient Education: The organisation has implemented programs to educate patients about the use of AI in their care, emphasising the benefits and addressing potential concerns.

2. Ethical Guidelines:

• AI Principles: Kaiser Permanente has developed a set of ethical principles for the use of AI in healthcare. These principles guide the development and deployment of AI technologies, ensuring that they are used responsibly and ethically.

3. Data Privacy and Security:

• Robust Measures: Kaiser Permanente has implemented robust data privacy and security measures to protect patient data. This includes encryption, access controls, and regular audits.
• Patient Consent: The organisation obtains informed consent from patients before using their data for AI-powered applications.

4. Collaboration with Clinicians:

• Co-Development: Kaiser Permanente has worked closely with clinicians to develop AI solutions that meet their specific needs and address their concerns. This collaboration has helped build trust among healthcare providers.
• Continuous Feedback: The organisation has established mechanisms for clinicians to provide feedback on AI-powered tools, allowing for ongoing improvement and refinement.

5. Human-Centred AI:

• Augmenting Human Capabilities: Kaiser Permanente views AI as a tool to augment human capabilities, rather than replacing them. This approach helps ensure that AI is used in a way that benefits patients and clinicians.

6. Regulatory Compliance:

• Adherence to Standards: Kaiser Permanente adheres to relevant regulatory standards, such as HIPAA and GDPR, to ensure that its use of AI complies with legal requirements.

By focusing on these areas, Kaiser Permanente has demonstrated a commitment to building trust in healthcare AI. This approach has helped the organisation leverage the potential of AI to improve patient outcomes and enhance the quality of care.

Source: https://about.kaiserpermanente.org/news/fostering-responsible-ai-in-health-care

Technology Companies Addressing the Trust Gap in Healthcare AI

Several technology companies have taken significant steps to address the trust gap in healthcare AI. Here are some notable examples:

1. Google Health:

• Explainable AI: Google has invested heavily in developing explainable AI techniques for its healthcare products. For instance, its AI-powered diagnostic tool for diabetic retinopathy provides visual explanations for its diagnoses, helping clinicians understand the model's reasoning.
• Data Privacy: Google has a strong commitment to data privacy and has implemented robust security measures to protect patient data.

2. IBM Watson Health:

• Transparency and Explainability: IBM Watson Health has focused on developing AI systems that can provide clear explanations for their recommendations. This includes the ability to trace back the reasoning behind a diagnosis to specific pieces of patient data.
• Ethical Guidelines: IBM has developed ethical guidelines for the use of AI in healthcare, emphasizing transparency, fairness, and accountability.

3. GE Healthcare:

• Collaboration with Clinicians: GE Healthcare has collaborated closely with clinicians to develop AI solutions that meet their specific needs and address their concerns. This has helped build trust among healthcare providers.
• Continuous Improvement: GE Healthcare has implemented a process for continuous improvement of its AI systems, ensuring that they are updated and refined based on feedback and new data.

4. NVIDIA:

• Explainable AI: NVIDIA has developed tools and frameworks that can help developers create more explainable AI models. This includes the ability to visualise the internal workings of neural networks.
• Healthcare Partnerships: NVIDIA has partnered with various healthcare organizations to develop AI-powered solutions for medical imaging, drug discovery, and personalized medicine.

5. Microsoft:

• Ethical AI: Microsoft has developed a set of AI principles that guide its development and deployment of AI technologies. These principles emphasise fairness, transparency, and accountability.
• Healthcare Partnerships: Microsoft has partnered with healthcare organizations to develop AI-powered solutions for a variety of applications, including disease diagnosis and treatment planning.

These are just a few examples of technology companies that are actively working to address the trust gap in healthcare AI. As the field of AI continues to evolve, we can expect to see more innovative approaches to building trust and ensuring the ethical and responsible use of AI in healthcare.

Nelson Advisors work with Healthcare Technology Founders, Owners and Investors to assess whether they should 'Build, Buy, Partner or Sell' to maximise shareholder value.

Healthcare Technology Mergers, Acquisitions, Growth & Strategy > www.nelsonadvisors.co.uk

Nelson Advisors HealthTech M&A Newsletter > Subscribe Today! https://lnkd.in/e5hTp_xb

Buy Side, Sell Side, Go To Market, Partnership Strategies > Email [email protected]

Nelson Advisors Healthcare Technology Thought Leadership > Visit https://www.healthcare.digital

#HealthTech #DigitalHealth #HealthIT #NelsonAdvisors #Mergers #Acquisitions #Growth #Strategy #GoToMarket #Partnerships #NHS #Europe #VentureCapital #PrivateEquity