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Artificial intelligence in the medical field is a vital tool addressing the rapid pace of change that this industry is faced with.
Rithika Venkatesh, Senior Manager of Innovation & Technology at IPI, says “AI-based technologies are particularly beneficial to the medical diagnosis stage of the patient’s journey, especially to detect diseases such as cancer more accurately and in early stages. AI systems can serve as a valuable clinical decision support tool, helping to reduce diagnostic errors by providing additional insights and flagging potential discrepancies in the interpretation of medical data. Integrating the analysis of big data using AI and using predictive analytics can support clinical decision-making thereby improving care and hospital workflow requiring specialist input.”
Indeed, AI has become an invaluable tool in the medical offering solutions by efficiently sifting through vast amounts of data, extracting crucial insights, creating predictive models, enhancing disease diagnosis and treatment, optimising care delivery, and streamlining various tasks and workflows.
Read on as we discover how AI’s integration in medical has painted a remarkable picture of health.
Ways in Which AI Is Revolutionising the Medical Field
- Enabling Higher-quality Patient Care
AI-driven clinical decision support (CDS) tools play a crucial role in developing accurate and actionable diagnostic or treatment recommendations, addressing issues of overtreatment or undertreatment that contribute significantly to healthcare costs. Additionally, CDS contributes to reducing clinician burnout, enhancing the overall clinician experience, and boosting productivity.
1.1 Facilitating Early Diagnosis by Improving Medical Imaging
Medical imaging is a leading application of AI in healthcare whereby large volumes of medical imaging data are analysed by AI to provide CDS to medical professionals such as radiologists. Digital innovation has fuelled the development of sophisticated medical imaging analysis tools that cater to various specialities and imaging modalities, namely MRI, X-ray, ultrasound, CT scan etc., transforming the field of healthcare.
AI algorithms are trained on datasets that include sick and healthy cases, comprising medical images, omics data, and clinical data characterising these images. After gathering sufficient data, these tools can then:
- Draw attention towards specific areas of the image
- Detect abnormalities with unmatched accuracy and precision
- Aid in various disease-monitoring cases
And since AI can process CT scans, X-ray images, and MRIs in just a fraction of the time, physicians and/or specialists can easily spot patterns and retrieve relevant information from volumes of data faster than ever before. The capabilities of such AI algorithms include:
- Identifying brain tumours through meticulous examination of MRI scans
- Identifying breast cancer through AI-supported mammogram screening
- Diagnosing cerebrovascular disease by analysing CT images, facilitating prompt triage and treatment
- Recognising early-stage Alzheimer's disease and dementia by scrutinising brain scans and detecting changes in structure and volume
- Detecting early-stage diabetic retinopathy through the analysis of retinal images.
1.2 Less Invasive Surgeries
AI-powered robotic systems, controlled by surgeons, can perform delicate and precise movements during surgery. These systems enhance the surgeon's dexterity, enabling them to operate with smaller incisions, which typically results in less trauma for the patient. The ‘da Vinci Surgical System’ is a famous example of a surgical robotic device which has been widely used for various minimally invasive surgeries. It is employed in surgeries such as prostatectomies, hysterectomies, and colorectal procedures. Other examples include the ‘Mazor X Surgical System’ designed for spine surgeries and ‘Stryker: Mako’ designed for knee replacement surgeries
1.3 Provide Insights into Precision Medicine
AI’s quickness at handling data analysis workloads can be incredibly beneficial in uncovering biomarkers, hidden associations, and disease-associated risks that would otherwise escape the notice of practitioners. Hence, AI could soon become integral to precision medicine where it can analyse multidimensional data of a patient’s records and find minute patterns that could point to the presence of a disease. Moreover, personalised medicine empowered by AI has the potential to remove uncertainty from both diagnosis and treatment strategies. This approach shifts the paradigm away from relying on statistical averages and optimistic expectations for favourable outcomes to a more precise and explicit identification of abnormalities within an individual patient. This identification is based on factors such as their DNA, medical history, and their family's medical background.
One illustration of this technology is found in DeepMind Health's ‘Streams’ — an AI-powered healthcare app designed for the early detection of acute kidney injury through the analysis of patient data. By delivering timely alerts to healthcare professionals, the app facilitates personalised interventions and contributes to improved patient outcomes. Additionally, NantHealth's ‘GPS Cancer’ test can conduct comprehensive genomic profiling, utilising AI to analyse the genomic and proteomic composition of a patient's cancer. It assists oncologists by identifying potential targeted therapies and suggesting clinical trial options, all based on the distinct characteristics of the patient's tumour.
1.4 Enhancing Patient Engagement
Conversational AI, encompassing chatbots and virtual assistants, significantly enhances patient engagement in healthcare. These technologies facilitate streamlined appointment scheduling, medication management, and post-discharge follow-ups. By providing instant access to health information, answering queries, and serving as virtual health coaches, conversational AI tools empower patients to make informed decisions about their well-being. Additionally, they contribute to remote patient monitoring, emotional support, and efficient handling of frequently asked questions. This streamlined and personalised communication not only improves patient adherence and outcomes but also ensures a more collaborative and informed healthcare experience.
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Revolutionising Clinical Research and Discovery
2.1 Drug discovery
Creating medicinal drugs is a complex process that takes years of clinical trials, research, and approvals. AI can help speed up this process since it can understand information more deeply, whether it be health records, molecular data, or genomic data. For instance, DrugBAN, an AI model developed by researchers at AstraZeneca and the University of Sheffield, can predict whether a drug will engage with its partner protein molecules as desired. Employing this model could significantly cut down the drug discovery process to just a few months instead of years.
2.2 Clinical Trial Recruitment
One of the foremost challenges in conducting clinical trials is the timely and efficient recruitment and enrolment of eligible patients who meet the selection criteria. This bottleneck often prolongs the trial duration and can impact the overall success of the study. The integration of AI technology into the recruitment process holds significant promise in addressing this challenge. AI can analyse vast datasets, electronic health records, and other relevant information to swiftly identify and screen potential participants, ensuring a more streamlined and targeted approach to recruitment.
One example of AI technology addressing the recruitment challenge in clinical trials is IBM’s ‘Watson for Clinical Trial Matching’.
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Optimising the Healthcare Workforce
Hospital optimisation through AI solutions is gaining global momentum as healthcare institutions leverage technology for enhanced efficiency. From predictive analytics optimising patient flow to surgical scheduling algorithms ensuring optimal resource utilisation, AI is transforming hospital operations, leading to improved patient care, and streamlined healthcare delivery.
One notable example is the implementation of AI-powered predictive analytics at Singapore General Hospital. This technology analyses historical patient data to forecast patient admissions, allowing for better resource allocation and improved bed management. Additionally, Tan Tock Seng Hospital in Singapore has embraced AI for optimising surgery scheduling.
In conclusion, Artificial intelligence (AI) stands at the brink of reshaping the landscape of medicine, offering the potential to enhance the experiences of clinicians and patients alike. Nevertheless, it is crucial to emphasise that AI cannot replace physicians; rather, it serves as a valuable tool to augment and support their expertise in healthcare. Additionally, these technologies are poise of ethical considerations, which the field is working toward addressing by establishing robust frameworks, fostering transparency, ensuring informed consent, and safeguarding patient privacy.
To learn more about the latest developments in AI for the medical field, contact us at [email protected] and get started on technology scouting for the available solutions we have curated here at IPI.
