AI for Personalized Medicine: Tailoring Treatments Based on Individual Needs

Imagine a world where medicine isn't a one-size-fits-all approach. Thanks to Artificial Intelligence (AI), this future is closer than ever. AI is revolutionising healthcare, and personalised medicine is at the forefront.

This blog will explore how AI is transforming how doctors treat patients by creating customised plans based on individual needs.

In recent years, the field of medicine has witnessed a paradigm shift towards personalised healthcare, aiming to tailor treatments to individual patients based on their unique genetic makeup, lifestyle, and other factors.

Personalised medicine holds the promise of more effective and targeted treatments, minimising side effects and optimise therapeutic outcomes. One of the key enablers of this transformative approach is artificial intelligence (AI), which plays a pivotal role in tailoring drug discovery processes for individuals.

Modern biomedical science is guided, if not dominated, by many interrelated themes. Four of the most prominent and important of these themes are 

 1. Personalised medicine, or the belief that health interventions need to be tailored to the nuanced and often unique genetic, biochemical, physiological, exposure and behavioural features individuals possess; 

2. The exploitation of emerging data-intensive assays, such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices; 

3. ‘Big data’ research paradigms in which massive amounts of data, say of the type generated from emerging data-intensive biomedical assays, are aggregated from different sources, harmonised, and made available for analysis in order to identify patterns that would normally not be identified if the different data points were analysed independently; 

4. Artificial Intelligence (AI; which we consider here to include algorithms based machine learning, deep learning, neural network constructs and a wide variety of related techniques, which can be used to find relevant patterns in massive data sets.

Understanding Personalized Medicine

Personalised medicine is a departure from the traditional one-size-fits-all approach to healthcare. It recognizes that individuals differ in their genetic composition, environmental exposures, and lifestyle choices, influencing how they respond to medications. The advent of genomics has been instrumental in unravelling the genetic basis of diseases, providing insights into the variability in drug response among different individuals.

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AI in Genomic Data Analysis

The human genome is an intricate code that holds the key to understanding various aspects of health and disease. AI algorithms excel at processing vast amounts of genomic data, identifying patterns, and drawing meaningful conclusions. By analysing genetic information, AI helps identify genetic variations that may influence an individual's susceptibility to certain diseases or determine their response to specific drugs.

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Drug Target Identification and Validation

AI-powered tools are revolutionising the drug discovery process by accelerating the identification and validation of potential drug targets. Traditional methods are time-consuming and costly, often resulting in high failure rates. AI algorithms can sift through massive datasets, predicting potential drug targets and assessing their viability with greater efficiency. This not only expedites the drug discovery pipeline but also increases the likelihood of developing drugs tailored to specific patient populations.

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Precision Medicine and Biomarker Discovery

Biomarkers are indicators that provide information about the physiological state of an individual. AI is instrumental in discovering and validating biomarkers that can guide treatment decisions. By analysing diverse data sources, including genomic, proteomic, and clinical data, AI helps identify biomarkers associated with disease progression, prognosis, and treatment response. This information allows for the development of targeted therapies that can be customised to an individual's unique profile.

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Clinical Trial Optimization

AI streamlines the design and execution of clinical trials, ensuring that participants are more likely to benefit from the interventions being tested. By identifying suitable patient populations based on their genetic and clinical characteristics, AI contributes to more effective and efficient trials. This not only accelerates the drug development timeline but also reduces costs associated with failed trials.

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Challenges and Ethical Considerations

While the integration of AI in personalised medicine holds immense promise, it also presents challenges and ethical considerations. Issues such as data privacy, bias in algorithms, and the potential for exacerbating healthcare disparities need to be addressed. Striking a balance between innovation and ethical standards is crucial to realising the full potential of AI in tailoring drug discovery for individuals.

Personalised medicine, fueled by advancements in genomics and powered by AI, is reshaping the landscape of healthcare. The ability to tailor drug discovery to individual characteristics holds the promise of more effective treatments with fewer side effects. As AI continues to evolve, its role in personalising medicine is likely to expand, ushering in a new era of healthcare that is truly patient-centred and data-driven. While challenges persist, the potential benefits make personalised medicine a compelling avenue for improving the precision and efficacy of medical treatments.

Doctors have requested whole-genome sequencing for their patients in an attempt to uncover genetic explanations for some ailments that cannot be diagnosed using conventional methods, and other healthcare experts have begun to analyze genomic data5. In December 2013, the US Food and Drug Administration (FDA) approved the first high-throughput (next-generation) genomic sequencer, Illumina’s MiSeqDx. This marketing authorization is an important milestone in the process of exploiting genetic data in a healthcare setting since it allows for the development and use of several novel genome-based tests. The FDA and the National Institute of Standards and Technology collaborated to create the whole human genome DNA and the best sequence interpretation of such genomes in order to generate genomic reference materials for performance evaluation. They may also arrange genetic tests at the request of patients if there is supporting evidence for the patient’s case. Physicians rely on HIM experts to gather knowledge regarding existing genetic tests, as well as their limitations and implications. This assists both patients and professionals in determining the best current treatment alternatives.

HIM experts must also keep patient data up to date because new investigations may disclose new facts. They must be trained to do this task. Powerful information systems are specialised for the administration of large-scale biological data. For example, openBIS (open-source Biology Information System) is a distributed information system that may be used to manage DNA sequences obtained by next-generation sequencing methods.

Image analysis, medicine discovery, and diagnostics are just a few of the applications of AI in healthcare. Data-intensive biomedical technology in study has also shown that people vary substantially in terms of disease processes and response to treatment on genetic, biochemical, physiological, exposure, and behavioural levels. This suggests that it is necessary to adjust, or ‘personalise,’ medications so that they work better for the specific needs of each patient. Through a symbiotic relationship with the use of data-intensive assays, AI can play a crucial role in developing personalised medicine and discovering relevant intervention targets to evaluate their effectiveness. The discovery of blood types and its effect on blood transfusions in the early twentieth century is considered the earliest example of personalised medicine. Personalised medicine improved transfusion safety by matching blood donors and recipients.

However, the requirement for vast amounts of high-quality data, potential bias in data analysis, and ethical concerns about privacy and security are among the problems and limitations of employing AI in personalised medicine. Personal genetic data analysis for healthcare reasons is complex due to the availability of large genomic datasets and the requirement for highly qualified genomic data analysts, databases, algorithms, software programmes, and computer resources. Most clinicians do not have access to these resources. Even if one can easily access excellent genomic data analysis programs and powerful workstations, it is still challenging for most healthcare professionals to select the best program and the correct parameters for that particular data set because they typically do not have the required training in this field.

When attempting to employ personal genomic information in customised medicine, the accuracy of the data obtained from raw datasets is the most pressing challenge. To detect and reduce bias in data and models, IBM has developed an online toolbox (AI Fairness 360) to assist researchers in examining bias among datasets and models, as well as methods to mitigate bias in classifiers. Model performance and therapeutic efficacy may be influenced by socio-environmental factors and workflows where the AI model will be implemented.

Data security and privacy are critical for verifying AI models in the clinical setting and considering an iteration loop before generally adopting them. Promising results were reported by Baowaly and colleagues, but more work needs to be done in AI to guarantee data privacy and security. Other challenges include high-throughput technologies that generate a large amount of genomic data, which can be difficult to manage and analyse. Additionally, the quality of the data can be poor, which can lead to inaccurate results. Personal genomic data is sensitive information that needs to be protected.

There are policies and laws related to the management of personal genomic information. Technical applications such as Interpretome and GenePING aim to protect personal genomic information. There are also legal, social, and ethical issues related to personal genomic information. The Genetic Information Nondiscrimination Act (GINA) is the law specifically created to protect individuals from discrimination based on their genetic test results. GINA extended the medical privacy and confidentiality rules to the disclosure of genetic information before the modifications of HIPAA (Health Insurance Portability and Accountability Act) and the HITECH (Health Information Technology for Economic and Clinical Health Act). GINA represents a landmark in the field of personal genomics because it removes one important concern when patients consider taking genetic tests. It is also beneficial for biomedical research because it removes similar concerns for participants of genetic research.

Physicians rely on HIM professionals to collect information about available genetic tests and their limitations or consequences creating issues relevant to HIM professionals. These professionals are also responsible for updating patient data regularly because new investigations may reveal new information. They need to be trained to perform this job.

To overcome these challenges, potential solutions include increasing collaboration and data sharing among healthcare providers, implementing rigorous quality control measures, and developing ethical guidelines for AI in healthcare. Scientists now have a greater knowledge of the association between genetic changes in patients’ genomes and their risk of getting specific diseases, as well as their possible responses to various treatment regimens, thanks to considerable advances in research. On the other side, these thorough study findings also make it difficult to assess individual genomic data for medical needs. Given that genomics research will very certainly one day allow more information to be extracted from a human genome, the threat to the individual’s progeny may be even more serious. Hence, before the widespread use of genomic data in clinical practice, a more robust and sophisticated security framework should be put in place to protect personal genomic data. It is the duty of all healthcare professionals to inform the public about the advantages of customised therapy and the risks associated with genetic testing.

To figure out how to incorporate genetic data into medical record systems for clinical decision support, software engineers must collaborate closely with healthcare providers. To assist doctors in implementing genomics into their practices, it could be advantageous to create a national information infrastructure with specified standards in place. Physicians must increase their understanding of genomics in order to properly order genetic tests and genomic analyses, comprehend the outcomes of such tests and analyses, and customise the course of treatment for each patient.

One suggestion for managing the overwhelming volume of genomic data is to apply a compression algorithm to reduce the sizes of these sequences. This can significantly reduce the size of stored data. Another approach is to keep only one reference genome and record all the differences between other human genomes and this reference genome. This approach can also significantly reduce the size of stored data. Some information systems are specifically dedicated to the management of large-scale biological information. For instance, openBIS is a distributed information system that can be used for managing DNA sequences generated by next-generation sequencing technologies. 

One essential component of HIM training is to educate HIM students on how to manage, protect, and apply genomic data in clinical settings. HIM professionals may also need to build knowledge-based decision-making systems for genomics-based personalised medicine practices so that clinicians can extract the most needed information from complex genomic data. The Office of the National Coordinator for Health Information Technology (ONC) is currently working on integrating genomic information into health information technology systems in order to take advantage of the full potential of genomic information in healthcare.

In conclusion, the future of AI in personalised medicine is promising, with the potential to revolutionise the way healthcare is delivered. Future research should prioritise the development of more accurate and efficient AI algorithms, the improvement of data quality and access, and the resolution of ethical and privacy concerns. AI has the ability to greatly enhance patient outcomes and overall healthcare quality if these problems and constraints are overcome. In this discipline, the IBM Watson system is a pioneer. 

The system, which contains both ML (Machine Learning) and NLP (Natural Language Processing) modules, has shown encouraging results in oncology. In a cancer study, for example, 99% of Watson’s therapy suggestions agree with medical conclusions. In addition, Watson worked with Quest Diagnostics to provide the AI Genetic Diagnostic Analysis. Furthermore, the system began to have an impact on actual clinical practices. Watson, for example, successfully detected a rare secondary leukaemia caused by myelodysplastic syndromes in Japan by studying genetic data. One prototype for connecting an AI system with front-end data input and back-end clinical actions is the cloud-based CC-Cruiser. More specifically, when patients arrive, their demographic information and clinical data (pictures, EP results, genetic results, blood pressure, medical notes, and so on) are collected into the AI system with their permission. The AI algorithm then uses the patients’ data to provide healthcare recommendations. These recommendations are delivered to clinicians to help them make clinical decisions. Feedback on the ideas (whether correct or incorrect) will also be recorded and put back into the AI system so that it can continue to improve accuracy.

Conclusion

In conclusion, the future of AI in personalised medicine is promising, with the potential to revolutionise the way healthcare is delivered. Future research should prioritise the development of more accurate and efficient AI algorithms, the improvement of data quality and access, and the resolution of ethical and privacy concerns. AI has the ability to greatly enhance patient outcomes and overall healthcare quality if these problems and constraints are overcome. In this discipline, the IBM Watson system is a pioneer. The system, which contains both ML (Machine Learning) and NLP (Natural Language Processing) modules, has shown encouraging results in oncology. 

In a cancer study, for example, 99% of Watson’s therapy suggestions agree with medical conclusions. In addition, Watson worked with Quest Diagnostics to provide the AI Genetic Diagnostic Analysis. Furthermore, the system began to have an impact on actual clinical practices. Watson, for example, successfully detected a rare secondary leukaemia caused by myelodysplastic syndromes in Japan by studying genetic data. One prototype for connecting an AI system with front-end data input and back-end clinical actions is the cloud-based CC-Cruiser. More specifically, when patients arrive, their demographic information and clinical data (pictures, EP results, genetic results, blood pressure, medical notes, and so on) are collected into the AI system with their permission. 

The AI algorithm then uses the patients’ data to provide healthcare recommendations. These recommendations are delivered to clinicians to help them make clinical decisions. Feedback on the ideas (whether correct or incorrect) will also be recorded and put back into the AI system so that it can continue to improve accuracy9.

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