AI in Healthcare

AI: New Healthcare Age or Hi-Tech Risk?

In the UK, AI is making significant strides in revolutionising healthcare practices, improving patient care and driving advancements in medical research. So just what can we look forward to and what are the downsides of AI in healthcare?

AI algorithms can process massive amounts of data, including medical records, clinical trials and research papers, providing valuable insights that were previously unattainable. This data-driven approach has the potential to revolutionise medical research, accelerate the development of therapies and ultimately improve population health outcomes.

From diagnosis and treatment to administrative tasks and data analysis, AI is set to play a crucial and growing role in shaping healthcare but it must always be about the power of computing being harnessed – technology can’t run the show.

Remote patient monitoring has already become a reality in UK healthcare. AI-powered wearable devices and sensors can continuously collect patient data, such as heart rate, blood pressure, and glucose levels and transmit it to healthcare providers in real time. AI algorithms analyse this data, alerting healthcare professionals to any concerning trends or abnormalities.

Clinicians must remain in control

Crucially it’s the clinicians who remain in control but the technology enables proactive interventions, reduces hospital re-admissions, and empowers patients to actively participate in their own healthcare management.

At the forefront, of course, will always be the requirement to meet robust regulatory frameworks drawn up by the government and NHS, to preserve patient privacy and trust.

AI systems rely on vast amounts of sensitive patient data, including medical records and personal information. The improper handling or unauthorised access to this data can lead to privacy breaches and security threats.

Security measures to protect patient data, including encryption, access controls, and regular security audits must be implemented.

Silicon Practice is ISO 27001 accredited and NHS IG Level 2 compliant. Further details of our security policies and measures can be found here.

Here in the UK, it is predicted that AI technology will raise global GDP by seven per cent in the next decade and, with usage in the healthcare industry only recorded at 11.5% by the UK government there is certainly potential for growth.

What does this mean for jobs? Excitingly the same paper, looking ahead as far as 2040, sees spending AI labour ranging from expenditure at £185.2bn by 2040 at an annual growth rate of 7.2% to £456bn at an annual growth rate of 12.1%.

Recognising that security keeping pace with developments in technology will be essential the UK government is to provide £100 million (US$124m) in initial funding for a Foundation Model Taskforce.

Its aim will be to support the development of secure and reliable AI models that can be used in healthcare, education and elsewhere.

Using data gathered through AI does run the risk of bias and discrimination.

This happens because AI algorithms can unintentionally perpetuate biases, leading to discriminatory outcomes and healthcare disparities. This can occur due to biased training data or biased decision-making within the algorithm.

Diverse and representative datasets should be used for training AI algorithms to keep biases to a minimum. Regular auditing and monitoring of AI systems are essential to identify and mitigate any biases that may arise. Transparent documentation of the algorithm’s decision-making process can enable scrutiny and accountability.

One of the fears about AI is the lack of the human touch and it is likely that over-reliance on AI systems without sufficient human oversight will lead to errors or missed diagnoses.

Combining expertise with AI capabilities

It is crucial that AI should support healthcare professionals rather than replace them, a collaborative approach that combines the expertise of clinicians with AI capabilities is key to this as is adhering to data protection regulations, such as the General Data Protection Regulation (GDPR) in the UK.

Additionally, healthcare organisations should ensure strict data anonymisation and implement strong consent processes for data collection and usage.

Regular training and education of healthcare professionals on AI systems can help them understand the limitations and potential pitfalls of AI technology. Establishing clear guidelines and protocols for the use of AI in healthcare can ensure appropriate human oversight and decision-making.

As decisions are taken based on input from AI this raises legal and ethical questions, including liability for AI-generated errors, accountability for decision-making, and ensuring fairness in resource allocation.

To tackle this healthcare organisations and policymakers should develop clear legal and ethical frameworks. Establishing regulatory bodies and ethical committees can help shape AI policies and ensure compliance with ethical standards.

Collaboration between policymakers, healthcare professionals, AI developers and patient advocacy groups will be essential to strike the right balance.

Alongside security and ethics comes the acceptance and trust that healthcare professionals will need to have from patients if they are not to opt out of information gathering, which is likely to lessen the accuracy of the data being collected.

Open communication with patients about the benefits and limitations of AI in healthcare is vital. Transparency in the use of AI algorithms, including explaining how decisions are made, can help build patient trust. Patient engagement and involvement in AI development and decision-making processes can enhance acceptance and ensure that AI is aligned with patient preferences and values.

By addressing these risks and implementing appropriate mitigation strategies, AI in healthcare can be harnessed responsibly, ensuring patient safety, equity and improved outcomes.

As AI continues to evolve, its impact is poised to be transformative, leading to improved patient outcomes, enhanced research, and a more efficient and effective healthcare system. But this will only happen by recognising and addressing the risks of using Artificial Intelligence and implementing appropriate mitigation strategies.

Silicon Practice fits into this ethos by designing sites which can be set up to make sure that patients get help depending on need or urgency, rather than how long they are prepared to wait in a phone queue, and by streamlining workloads for busy staff.

For more information about how our digital services could benefit your healthcare practice, contact us.

blog by Bruno Clements