Health + Tech | Technology customising treatment options using patient specific data
Imagine a world where a doctor could prescribe medication more precisely and tailor it specifically to a patient based on their unique circumstances and health history. In today’s world, where technology reigns and is increasingly being integrated into healthcare, it is possible for this type of precision medicine to be employed to patient care.
The ability to analyse patient data using algorithms to find intricate patterns, identify correlations, and predict outcomes with remarkable accuracy is the opportunity technology has given the sector to vastly improve patient outcomes.
Personalised medicine represents a paradigm shift in healthcare, moving away from the traditional one-size-fits-all approach towards treatments tailored to individual characteristics, including genetic makeup, lifestyle factors, and environmental influences. This approach acknowledges the inherent variability among patients and aims to optimise therapeutic outcomes while minimising adverse effects.
At the heart of personalised medicine lies the ability to extract meaningful insights from complex datasets. In this instance, artificial intelligence (AI) algorithms excel, as they are capable of processing vast volumes of structured and unstructured data, including electronic health records (EHRs), genetic profiles, medical imaging, wearable device data, and even social determinants of health. Through advanced machine learning techniques, AI can uncover hidden patterns, identify risk factors, and predict disease trajectories. Improvements in the use of technology in healthcare has allowed for this to be more seamless.
As sciencedirect.com notes, “Historically, a critical boost in the application of AI in healthcare has been enabled by the digitisation of patient data, including the adoption of electronic health records (EHRs), imaging and digital pathology.”
One of the key applications of AI in personalised medicine is predictive modelling, wherein algorithms analyse patient-specific data to forecast the most effective treatment options. By integrating clinical data with genomic information, biomarker data, and treatment outcomes, algorithms can identify optimal treatment strategies tailored to an individual’s unique characteristics. For example, in oncology, AI models can predict tumour response to different chemotherapy regimens based on genetic mutations and molecular profiles, guiding clinicians in selecting the most appropriate therapy for each patient.
In an article titled ‘Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care’, which appeared on sciencedirect.com in 2022, and was authored by Corti, et al, it was observed that “In cancer care, diagnostic accuracy, staging precision, and time to diagnosis are key determinants of clinical decision-making and treatment outcomes. In this regard, AI’s contribution to the digital pathology and imaging fields has been remarkable in recent years, with performance comparable to that of board-certified experts, and an additional advantage of automation and scalability.”
AI is also revolutionising the drug discovery and development process, enabling the identification of novel therapeutic targets and the design of more efficacious and safer medications. By analysing vast repositories of biomedical literature, genomic databases, and clinical trial data, AI algorithms can uncover hidden relationships between biological pathways, drug targets, and disease mechanisms. This enables researchers to develop targeted therapies that address the underlying molecular drivers of disease, thereby maximising treatment efficacy while minimising off-target effects.
In addition to guiding treatment selection, AI can play a pivotal role in real-time treatment optimisation, continuously adapting therapeutic interventions based on dynamic changes in patient status. Through the integration of wearable devices and remote monitoring technologies, AI algorithms can track patient vital signs, medication adherence, and disease progression in real-time. This enables healthcare providers to intervene proactively, adjusting treatment regimens to optimise outcomes and prevent adverse events.
Despite its immense potential, we have seen in recent times that there has been an overall pushback where AI is concerned and even before then, the widespread adoption of AI in personalised medicine has been slow. With the increased recognition of the importance of integrating health and technology approaches, I believe AI will in the future feature more prominently in treatment and care, specific to individuals as well as at the population level.
It is becoming more apparent that AI holds tremendous promise for customising treatment options based on patient-specific data, ushering in a new era of personalised medicine. Realising the full potential of AI requires continued investment in research, infrastructure, and regulatory frameworks.
Doug Halsall is the chairman and CEO of Advanced Integrated Systems. Email feedback to doug.halsall@gmail.com and editorial@gleanerjm.com