In 2023, in a seemingly understated announcement, the US Food and Drug Administration granted approval for human brain chipping trials, a development with potentially groundbreaking implications. In short, the company spearheading the initiative, Neuralink aim to restore sight and mobility by linking implanted chips to computers.
Perhaps linked to Neuralink’s PR the story of Gert-Jan Oskam emerged around the same time to highlight the impact of personalised medicine. Once paralysed from a cycling accident twelve years prior, thanks to brain and spine implants, he regained mobility.
Similarly, amidst the recent uproar over GPT technology, reports surfaced about AI's role in diagnosing and treating cancers. In a nutshell, personalised medicine companies use image-based AI to detect malignancy patterns far earlier than the human eye, which revolutionises medical diagnostics.
The future of personalised medicine and its research hinges on technological advancements, however, at this point, the extent, and ethical considerations of it remain uncertain. Before delving into this discussion, it's essential to address current medical treatment issues, reassess evaluative and diagnostic methods, look at a definition of what personalised medicine is and highlight the benefits of personalised medicine.
In addition, exploring the evolution of medical and diagnostic practices and recognising the flawed reasoning that still impacts patient care is also crucial. Beyond this, discussing the moral and ethical dimensions of emerging medical technologies will provide insights into navigating the complex landscape of personalised medicine.
So, what is personalised medicine?
Early Personalised Medicine Research and Treatment
Have you heard the wild rumour that Leonardo da Vinci robbed graves to study human anatomy? There’s another to say that he examined the bodies of executed criminals with permission, which is more plausible. What is for sure is that he studied and recorded the male anatomy extensively.
In addition to the availability of male bodies for his studies, cadavers for early anatomical studies were mostly white, which influenced medical research and practice to this day. Even today, bodies with different ethnicities are often underrepresented in medical research unless the study specifically addresses health issues prevalent in those populations.
While these disparities stem from historical biases, the medical community is becoming more aware of these deficiencies and advances in artificial intelligence (AI) are also helping to bridge these gaps.
For students aspiring to follow careers in science, medicine tutors can provide essential mentorship and guidance on the topic of personalised medicine.
The Current Approach to Diagnosis and Treatment
Doctors are undeniably overworked and have to handle huge numbers of patients with diverse complaints. As such time constraints force them to quickly prescribe medication and rush through appointments before moving on to the next appointment. This approach has led to a focus on treating symptoms rather than the root causes of many of today’s so-called lifestyle diseases.
A common example is the management of metabolic syndrome, a condition resulting from poor lifestyle choices, excessive processed food and sugar intake, and lack of physical activity. In the past, doctors prescribed multiple medications to manage such conditions. However, there's a growing shift towards advocating for lifestyle changes and better nutrition as a form of treatment.
Despite this, some patients remain convinced that pharmaceuticals are the only solution. Doctors often struggle to persuade these patients that not every visit requires a prescription. For instance, a friend of mine felt run down with a minor cold. When their doctor advised them to simply rest and recover naturally, they were outraged and demanded medication.
Unfortunately, this culture of over-prescribing has contributed to the rise of antibiotic-resistant superbugs. The blame doesn't lie solely with insistent patients or overzealous doctors; it's a long-standing practice to try various treatments to see what works. Anyone who has watched medical dramas like "House" has seen this trial-and-error method in action.
In this evolving landscape, medicine tutors play a helpful role by explaining the complexities of personalised medicine research. They are also able to make the subject matter accessible while highlighting the benefits of personalised medicine like understanding the root causes of diseases and the potential of non-pharmaceutical interventions.
How Artificial Intelligence Is Shaping Medical Practice
Chemotherapy often exemplifies the approach of overwhelming illnesses with aggressive treatments in the hope of achieving remission. With cancer diagnoses on the rise, multi-pronged aggressive treatments have become the norm—but soon, this may change.
Typically, cancer is detected through imaging techniques such as X-rays, mammograms, or MRIs. If an abnormality is found, a biopsy is recommended, and the tissue is examined in a lab. Traditionally, trained pathologists look for cell abnormalities, and if cancer is confirmed, the doctor delivers the difficult news to the patient.
However, decades of accumulated scans have made it feasible to train AI systems to identify growths and cell abnormalities. So far, results are promising, and a study published in The Lancet reports that AI diagnostic performance is "equivalent to healthcare professionals."
It is well known that early detection of cancer is crucial for successful treatment outcomes because it provides patients with more options. While early-stage malignancies might escape the human eye, AI can reliably identify them and eliminate the need for a second opinion.
Beyond detection, AI is used by personalised medicine companies which can then recommend drug regimens and therapies. Thanks to the Human Genome Project, completed in 13 years starting in 1990, provided a wealth of genetic data that AI models now use. Chemotherapy often exemplifies the approach of overwhelming illnesses with aggressive treatments in the hope of achieving remission. With cancer diagnoses on the rise, multi-pronged aggressive treatments have become the norm—but this may soon change.
Typically, cancer is detected through imaging techniques such as X-rays, mammograms, or MRIs. If an abnormality is found, a biopsy is recommended, and the tissue is examined in a lab. Traditionally, trained pathologists look for cell abnormalities, and if cancer is confirmed, the doctor must then deliver the difficult news to the patient.
However, decades of accumulated scans have made it feasible to train AI systems to identify growths and cell abnormalities. Current results are promising, with a study published in The Lancet reporting that AI diagnostic performance is "equivalent to healthcare professionals."
Early detection of cancer is crucial for successful treatment outcomes, as it provides patients with more options. While early-stage malignancies might escape the human eye, AI can reliably identify them, eliminating the need for a second opinion.
Beyond detection, AI can also personalise drug regimens and therapies. Thanks to the Human Genome Project, completed in 13 years starting in 1990, there is a wealth of genetic data that AI models now use to recommend drugs and therapies. These models also integrate comprehensive information on pharmaceuticals, their compositions, and interactions.
Preliminary AI trials matching genetic information with drug data have shown promising results. Instead of subjecting patients to extensive radiation, AI can help doctors select the most effective therapies tailored to specific cancer types.
AI stands out as one of the past decade's most significant medical breakthroughs for personalised medicine companies. It offers immense potential for enhancing healthcare, cancer treatment, and disease diagnosis. While still in its early stages, it seems that AI will be instrumental in developing personalised treatment plans and transforming the future of medical care.
By all accounts the benefits of personalised medicine are undisputed.
What is Personalised Medicine?
One of healthcare's significant failings has been the one-size-fits-all approach to treating patients. Historically, the modus operandi of drug therapies has been to treat the broadest number of patients without considering individual variability.
Learning about the future of personalised medicine from a biology tutor provides the unique benefit of gaining insights into cutting-edge medical technologies which enables students to understand the potential for customised healthcare solutions.
The medical community has long recognised that these generalised approaches are suboptimal. Hence, there's a strong interest in precision medicine, which considers the genetic origin of disease and prescribes treatments tailored to individual patients.
As already mentioned, sadly standard treatments often address symptoms rather than the root causes of diseases. For instance, conditions like cancer often stem from a genetic predisposition, with environmental factors influencing when and how these conditions manifest. This is one reason why cancer patients may face traumatic relapses and endure repeated chemotherapy.
Precision medicine aims to understand the genetic origins of a patient's illness and develop a personalised treatment plan. This involves selecting drugs and therapies that match the patient's genetic profile, ensuring more targeted and effective results.
So, while the future of personalised medicine is still emerging, the human genome has been mapped, and vast amounts of diagnostic data have been collected. The challenge, however, is that the infrastructure to deliver personalised medicine universally is lacking. Advanced medical facilities capable of providing customised treatments are scarce, especially in less developed regions.
Finally, as promising as the future of personalised medicine research and personalised medicine companies may be, it will take decades for it to become a reality and longer still to make it accessible to all.
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