Transforming Healthcare with Artificial Intelligence
Artificial Intelligence (AI) can be broadly described as the creation of computer systems that perform tasks traditionally requiring human intelligence, such as learning from experience, recognising patterns, and making informed decisions. AI uses various techniques, including machine learning and deep learning.
“Machine learning”(ML), a key component of AI, enables computers to learn from data and make predictions or decisions without being explicitly programmed for each task. The system “learns” by adjusting its parameters to minimise prediction errors and continuously refines its approach as more data becomes available or as requirements evolve. This technology is widely applied, from recommendation systems like those used by Netflix and Amazon to image recognition and autonomous vehicles.
Deep learning (DL) is an advanced form of machine learning, which employs complex neural networks with multiple layers to detect and understand patterns in vast amounts of data automatically. While machine learning can manage a broad range of tasks with simpler models, deep learning excels at recognising patterns in images and speech by processing data through several layers of abstraction.
With the help of these “abilities,” AI has significantly advanced the efficiency, accuracy, and personalisation of healthcare, with the ultimate goal of improving patient outcomes and reducing costs. What sets AI apart from traditional healthcare technologies is its ability to process larger and more diverse datasets, delivering precise and actionable insights to end-users. Let’s delve into the various applications of AI in medicine.
Medical Diagnostics
AI is transforming medical diagnostics by enabling faster, more accurate, and often earlier detection of diseases. This is because AI algorithms excel in analysing large amounts of complex medical data such as imaging scans, blood tests, and genetic information with a level of precision that often surpasses human capabilities. For instance, in radiology, AI systems can identify abnormalities in X-rays, MRIs, and CT scans, detecting conditions like cancer, fractures, and neurological disorders with remarkable accuracy. It can also quickly identify negative radiological exams, which comes in handy in hospitals with high workloads and limited human resources.
AI plays a crucial role in pathology by analysing tissue samples to detect disease markers that might be overlooked by the human eye. In cardiology, deep learning algorithms are being used to diagnose heart attacks with the same accuracy as cardiologists. Similarly, AI networks trained with clinical images provide valuable assistance in dermatology by accurately classifying skin lesions.
Studies have shown that AI can match or even surpass human experts in diagnostic performance, excelling in both accuracy and speed. For example, in polycystic kidney disease (PKD), the size of the kidneys (total kidney volume) is now known to play a role in how rapidly kidney function is going to decline in the future. Normally, assessing this data would involve analysing dozens of kidney images, which could take about 45 minutes per patient. However, AI can automate this process and generate results in a matter of seconds.
Natural language processing (NLP) is another specialised area of AI that focuses on the interaction between computers and humans through natural language, including the ability to understand, interpret, and generate human language. This technology enhances medical documentation, offering a more efficient and accurate recording of patient information.
Clinical Decision and Personalised Medicine
AI is becoming increasingly essential in clinical decision-making as it can analyse large amounts of extensive medical data such as patient records, research studies, and clinical guidelines, therefore increasing the accuracy and speed of medical care by helping to reduce human error and improve decision-making.
AI allows clinicians to personalise treatments by considering each patient’s unique medical history, genetic profile, and lifestyle data, enhancing the effectiveness of care. It also helps in predicting how patients might respond to different medications or therapies, minimising the risk of adverse reactions and optimising treatment outcomes. Additionally, AI can assist in identifying genetic markers associated with specific diseases, enabling earlier detection and targeted interventions. It can reveal patterns and correlations within patient data that may not be immediately obvious, leading to more informed decisions, more targeted therapies and better management of conditions. These systems offer evidence-based recommendations for treatment plans, predict patient outcomes, and even propose alternative therapies.
AI algorithms also assist in triaging patients based on urgency, prioritising high-risk cases, reducing waiting times, and improving patient flow. Symptom assessment tools can help rule out other causes of illness, reducing unnecessary visits to the emergency department and ensuring that patients receive appropriate care and treatment plans.
AI may be especially useful in the recruitment process for clinical trials as it accelerates identifying suitable candidates based on specific criteria, thereby reducing time and costs. It predicts patient responses to treatments, helping to design more targeted trials and optimise protocols. AI’s ability to continuously analyse incoming data facilitates adaptive trial designs, allowing for real-time adjustments and increasing the overall success of trials.
Monitoring
AI-powered devices, such as wearable sensors and smart watches, are revolutionising patient care by enabling continuous, real-time health monitoring. These devices track vital signs such as heart rate, blood pressure, glucose levels, and oxygen saturation, generating valuable data that can be analysed instantly. With the ability to detect subtle changes in a patient’s condition, such systems can identify patterns and predict potential health issues before they become critical.
The use of AI also facilitates remote patient care, allowing healthcare providers to oversee patients from a distance. This contributes to improved patient outcomes, reduced hospital readmissions, and enhanced overall efficiency within healthcare systems by ensuring timely and tailored care.
Patient Engagement
Healthcare is fast transforming into a more interactive, personalised, and accessible process with the use of AI-powered tools, such as chatbots and virtual health assistants. These tools offer immediate responses to health-related queries, guiding symptoms, medication management, and lifestyle changes, empowering patients in their healthcare.
By analysing patient data, AI enables personalised communication, delivering tailored health advice, medication reminders, and follow-up care instructions. This approach improves adherence to treatment plans and encourages healthier behaviours. AI-driven platforms also facilitate remote consultations (telemedicine), allowing patients to connect with healthcare providers from home –which is useful in several scenarios. Additionally, AI-powered chatbots and virtual assistants provide preliminary medical advice, schedule appointments, and handle routine tasks like sending reminders and managing communications, further streamlining the patient experience.
Robotic Surgery
Integrated with AI, surgical robots rely on data captured through sensors and images to operate. This allows surgeons to have advanced intraoperative metrics such as force and tactile measurements, enhanced detection of positive surgical margins and even allows for the complete automation of certain steps in surgical procedures. These benefits may be broadly classified into three categories: surgical enhancement, surgical assessment/feedback, and robotic autonomy. All categories focus on creating safe environments by data-informed surgical decision-making, as well as enhancing surgical education.
Surgical field enhancement through machine learning provides surgeons with a much clearer field of vision by de-noising, de-blurring, and color-correcting the real-time camera images during an operation, creating a better working environment for the surgeons and more precise operational procedures. AI can also be taught to recognize native tissues, automatically segmenting loose connective tissue fibres to identify safe dissection planes.
Most surgical operations are physically and mentally exhausting. By automating certain surgical tasks such as suturing, some of this workload can be taken off the surgeons, making operations faster and safer. Some machine learning models have already been developed and put into practice and new ones are being developed every day, such as fully automated laparoscopic bowel anastomoses.
AI also shows promise for the generation and delivery of highly specialised surgical feedback for training purposes. By highly efficient gesture recognition, AI can not only enhance autonomous robotic surgery systems but also provide surgeon skills assessment in surgical training.
Drug Discovery and Development
Discovering new drugs is a very slow and expensive process. Preclinical stages can take up to six years, costing up to billions of dollars. AI systems and tools accelerate almost all stages of developing new drugs, which can potentially reshape the whole economics of the industry.
The first AI-designed drug molecule to enter clinical human trials was announced in 2020. In 2021 when an AI system called AlphaFold predicted the protein structures for 330,000 proteins, including all 20,000 proteins in the human genome. The system now includes over 200 million proteins, covering nearly all cataloged proteins known to science. By March 2022, the authorities announced that biotech companies using an AI-first approach had more than 150 small-molecule drugs in discovery and more than 15 already in clinical trials.
AI is used for target identification by training on large datasets to understand the biological mechanisms of diseases and to identify new proteins and/or genes to target certain diseases. By using high-fidelity computer simulations on the molecular level, AI reduces the need to test candidate drugs physically, thus eliminating the time-consuming, high costs of traditional chemistry methods. It can also design drug molecules entirely from scratch, compared to traditional drug development, which involves studying and identifying existing libraries and building novel ones from known molecules. In short, the idea of “fully automated end-to-end drug discovery” seems to be a matter of when rather than if.
Predictive Analysis
The key application of predictive analysis is disease prevention. The datasets that AI can be trained on may include electronic health records, genomics, wearable devices, and environmental factors. By analysing such large datasets AI can predict disease outbreaks, patient outcomes, adverse drug reactions (and drug-drug reactions) as well as progression of chronic diseases. However, to interpret prediction outcomes, healthcare professionals should be able to explain how an AI algorithm reaches a prediction. The concept of explainable artificial intelligence (XAI) has drawn significant attention recently, which involves an internal explanation model to communicate internal decisions, behaviours and actions to the interacting humans. Physicians can communicate with AI systems by answering questions and analysing cases, illustrations, visualisation of data properties, etc.
AI can also help promote information on disease prevention online, reaching large numbers of people quickly, and even analyse text on social media to predict outbreaks. Last but not least, it may be useful in tracking patient deterioration, re-admissions, mortality, documentation improvement, and chronic care management.
Healthcare Administration
Healthcare management and administration details are usually overlooked by visiting patients unless they have negative experiences; cancellations, re-scheduling, waiting for hours for certain tests, etc. It is difficult, if not impossible to provide excellent service all the time because of high demand. What if computer systems were run by AI, making the whole administrative system more efficient? AI can be used to simplify administrative tasks such as scheduling and billing as well as managing electronic health record keeping. This is, broadly speaking, called streamlining administrative tasks and it means optimisation of business processes within an organisation, which could be much more efficient if driven by AI. This would also open up a lot more time for physicians to focus on medical issues instead of having to deal with administrative tasks.
Concerns and Epilogue
There are concerns about AI reducing job opportunities in healthcare. However, even though machines may be able to translate human behaviour, they cannot replicate certain critical human traits like critical thinking, communication skills, emotional intelligence, and creativity. Another concern regarding AI applications in medicine is data privacy because AI models require working with massive amounts of data requiring large-scale data sharing across institutions. Electronic health records, if not used with consent, could be aggregated, shared, and sold.
Besides, developed machine learning models are not transparent, which may cause problems with the reliability and reproducibility of surgical tasks, meaning trust issues may arise among surgeons as well as patients. AI systems must be carefully trained to avoid biases and ensure that their outputs are fair and accurate. AI chatbots, for instance, can sometimes produce misleading medical advice. This may easily lead to questions regarding whether the medical system is being exploited by recommending drugs or tests just to generate profit for the involved parties. To avoid such ethical risks and problems, there is a definite and critical need for effective regulation and oversight. Regulators, data scientists, medical researchers, and clinicians should prioritise equity and ethics, aiming for better health for everyone.
While AI has the potential to save time for healthcare professionals, it is not designed to replace them. Instead, AI is intended to assist and support doctors by improving efficiency, refining processes, and enabling more personalised care.
Human expertise remains crucial to providing clinical context, ensuring accurate data for AI analysis, and translating AI findings into meaningful insights for patients. The relationship between AI and healthcare professionals is similar to that of pilots and automated flight systems -while technology enhances performance, human oversight and intervention are still essential. People are still necessary to make adjustments and take over in cases of emergency.
REFERENCES
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