Dr Emelina Ellis
Chief Clinical Officer
AI in healthcare promises to improve and enhance patient care, streamline operations and drive innovation in medical research at unprecedented rates. AI is helping to reshape every aspect of healthcare delivery from disease diagnosis to personalised treatment plans.
The global healthcare AI market is expected to reach $188 billion by 2030 with an increasing CAGR of 37% (2022-2030) Statista.
AI Powered Algorithms…
AI helps analyse medical images (like X Rays, MRIs and CT scans) with unwavering accuracy helping clinicians in early disease detection and prevention. Natural Language Processing (NLP) algorithms are also being used within the industry to extract valuable insights from large volumes of clinical notes and existing research literature and studies to facilitate data driven decision-making across the board. Using AI in this way can reduce medical errors and improve patient safety into the future.
AI can help healthcare providers predict the type of health conditions that a patient is likely to develop in the future. Some studies have found that predictive AI tools could reduce hospital admissions by half. Having the ability to predict certain things not only helps the improve patient and health outcomes but assists with optimising resources, enhancing operational efficiencies and reduces healthcare costs for medical and insurance providers.
Optimising resources with AI helps to free up clinicians’ time by automating routine tasks like data entry or admin. This means that clinicians can focus on patient care and complex decision-making, maximising their time and assisting with speedier treatment and recovery.
More than half (53%) of NHS staff think AI will have a significant impact on clinical service delivery, most immediately through automated appointment booking and improving the accuracy of diagnosis. In addition, NHS staff now agree that AI has the potential to cut wait times (63%), improve patient outcomes (65%) and cut the cost of patient care (56%).
However, AI depends on digital literacy. Digital literacy is crucial in the modern world to communicate, find employment or receive comprehensive education- yet Eurostat research found that 46% of people in the EU aged 16-74 didn’t have basic overall skills (in 2021). and Two thirds (72%) of the people using digital tech in the NHS every day think it is being held back by a lack of integration with other legacy technologies, Staff have also acknowledged that these new innovations must cater to all patients, regardless of tech literacy.
In a mental health care context, AI can identify (with NLP) text and speech changes in language patterns that may indicate depression, anxiety of other mental health conditions. AI based chatbots and virtual assistants can also help provide real time mental health support and interventions whilst assisting with education. AI can help enhance accessibility to mental health services especially in underserved communities with limited resources.
Some ethical considerations arise when AI is used in healthcare. Due to the nature of relying on data, one of the biggest concerns is patient privacy and data security. Ensuring data protection measures and strict privacy regulations are key to help maintain patient trust and confidentiality and ensure it remains a safe space for all.
Algorithms should be formulated with collaboration between ethicists, policy makers, data scientists and healthcare specialists to help develop governing guidelines and regulations for the responsible use of AI in healthcare.
Research studied the preferred treatment modality by most people with depression and found…
- 26% of participants preferred self-guided digital treatment,
- 20% preferred expert guided digital treatment
- 44.5% preferred in-person psychotherapy
This highlights that although there is clear demand for digital services and digital integration, most respondents chose face to face therapy which shows a preference for human feel or an integrated approach with human and digital services.
Although AI can assist in advancing the understanding and causes of mental illnesses which helps improve overall diagnosis, there is little follow up for those that use services. Biases may be present in the algorithms which presents risk of a lack of diversity and the inability to detect non verbal cues.
These risks may leave people vulnerable in a time of need, although AI is gaining traction and we can rely on it to improve the healthcare landscape, further development is needed for AI to match human led mental health solutions.
For this to happen, the tech must show ability to sense, understand and react to a diverse range of complex human behaviour like demonstrating attention, motivation, creativity and most importantly empathy.
Despite vast advancements, current technology has its limits when it comes to mental health. AI algorithms are unconscious or are specifically created to have a special purpose or support with specific tasks.
Generally speaking, digital reasoning and problem solving are the only factors that have a comparison to a human or biological mental health solution such as a therapist or mental health coach.
In conclusion, to effectively improve health outcomes in a mental health setting, AI should be mixed with humans to complement each other and drive real behavioural change and support.