Healthcare
Enhancing Patient Outcomes: The Power of AI-Driven Personalisation
June 12th, 2007 was a red-letter day for Adam! Adam ended one era and started another one. We will happily excuse any confusion with the biblical figure! The Adam here is a robot that ended humans’ monopoly on scientific discovery. It successfully discovered a function of a yeast gene using AI (Artificial Intelligence). Eve, the more advanced version of Adam, was not to be left behind. Eve discovered that triclosan could potentially treat drug-resistant malarial parasites by screening thousands of compounds. BenchSci, a Canadian start-up provides a ML (Machine Learning) tool to search for antibodies. A survey conducted by them found that over 40% of the scientists who participated in the survey were unfamiliar with the application of AI in drug discovery. It will be a good exercise to increase our awareness of the key factors that have played a major role in shaping personalised medicine.
Personalised medicine is not dependent on a singular domain in science. It is rather an outcome for the healthcare industry that has emerged over a period. Scientists and personnel from a wide variety of subjects ranging from drug discovery, pharma, molecular biology, computational biology, genetics, clinical trials, computer science, statistical modelling etc must integrate the learnings from each of their areas to fuel the concept of ‘Personalised Medicine’ and make it a possibility. There are 4 emerging complementary themes in biomedical science - the rapid growth in AI, big data research, data-intensive biomedical assays, and personalised medicine. Research associated with these themes is often intensive and independent of each other.
The information, insights and strategies emerging from these studies must be integrated if personalised healthcare interventions are to be accomplished. AI has impacted diagnostics & prognosis of diseases, big data research has automated large-scale clinical data analysis directly impacting the stages of clinical trials of medicine, and high throughput-data-intensive assays like DNA sequencing and proteomics have revealed inter-individual variations at play in disease processes. These have culminated in the need for personalised medicine.
Let us also look at this from the lens of the customer of the healthcare industry – the patient. Earlier, the patient was only the end-user or consumer of the healthcare industry with limited or no ability to influence the working of the industry. In recent times, the patient experience and journey have influenced the way the industry functions. AI can be used to gain insights into patient data, behaviour, biases, preferences, demographics, etc., to tailor it to individual needs.
Personalised treatment plans – The one-size-fits-all concept has evolved to highly personalised treatment plans to suit the differences in genetic makeup, medical history, and lifestyle – factors which play a major role in how patients respond to treatments. Machine learning algorithms can identify patients who would respond well to a treatment. This will lead to targeted treatments, lowering costs as well.
Patient Engagements – Humanised chatbots and virtual assistants are the first-line contacts for patients to provide the latest and real-time information. AI can maintain data privacy while providing the patient with access to information on disease conditions, providers, treatment options, medicine pricing, insurance options, scheduling hospital visits and more.
Preventive Healthcare – The use of preventive healthcare solutions can reduce unnecessary admission, testing, procedures & treatment which will result in cost savings for both patients and service providers. AI has given rise to both preventive and predictive analytics. Patients with NSCLC (non-small cell lung cancer) have a 5-year life expectancy post-diagnosis. An Israeli company Medial EarlySign used AI to detect cancer earlier than existing methods could and as a result directly improved diagnosis. Babylon Health, a healthcare company, used AI to develop a tool to predict a patient's likelihood of developing heart disease.
Scientist Steve Oliver of the University of Cambridge, who is one of the developers of Adam, is very positive about the use of robots with AI-infused ability to test, screen and discover more compounds, genes and functions.
In conclusion, integrating AI into healthcare is a remarkable stride towards a future where patient care is not just about treating illnesses but about understanding individuals on a deeply personal level. AI's capacity to sift through and make sense of humongous amounts of data and draw conclusions from it in real time enables healthcare providers to offer treatments tailored to the unique requirements of each patient. This shift towards personalised patient care promises more effective treatments, better outcomes, and ultimately, a healthier and more resilient society. As AI continues to evolve and be embraced across the healthcare spectrum, we are poised to witness a transformation in patient care that saves lives and enhances countless individuals' quality of life.
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