According to Nature Medicine, researchers look at cutting-edge techniques to evaluate the pros and cons of developing medical remedies. Medicine is one of the world’s oldest professions, with surgical procedures reaching back 31,000 years and medical treatises dating back to 1600 BC.
Medical practice based on research is a relatively new concept. Even though the first systematic review was undertaken in 1904 to assess the efficiency of typhoid immunization and the first randomized controlled trials were published in the 1940s, the phrase “evidence-based medicine” did not become common until 1992. Throughout the subsequent decades, randomized controlled trials, meta-analyses, and systematic reviews aided in the development of the field.
This is the first post in a series on rethinking evidence in modern medicine, focusing on establishing the efficacy and safety of newer treatments and health technologies, such as gene editing and artificial intelligence (AI) health algorithms, which necessitate the development of new analytical techniques.
Vivek Subbiah believes in the first perspective of the series that master protocols (including basket, umbrella, and platform trials) should use cutting-edge technologies such as digitization, biomarkers, digital endpoints, and real-world data due to their low cost and high power. Prospective meta-analyses of summary data and the confidential communication of individual patient data during the research are two instances of this form of data sharing.
Throughout the year, they will explore several concerns that threaten the current level of medical evidence. In the realm of precision medicine, the placebo-controlled study faces various obstacles. Because treatments for uncommon diseases, such as gene therapy, may only benefit a small number of people, conducting a large-scale trial is unfeasible. Long-term follow-up of treated patients can establish real-world safety and effectiveness.
Despite its inherent biases, real-world data can be valuable in several circumstances, including rare illness research, follow-up studies after treatment are licensed, investigation of new possible applications for existing drugs, and even as a replacement for a control group.
Due to a lack of racial and cultural diversity in many training sets, multi-model biomedical AI, machine learning, and deep neural networks are just beginning to permeate healthcare with different outcomes. According to the US Food and Drug Administration, AI should be controlled as a therapeutic tool. These algorithms improve with each iteration by being exposed to more data outside of their training set, but each time this requires new clearance.
David Bates says in World View that authorities must be more tolerant in light of the profusion of cutting-edge medical discoveries. Similar to the approval method for variant-specific vaccinations against COVID-19, a gene-editing technology platform may be proposed for approval with modest alterations to the target sequence requiring such an examination.
Some of today’s medical devices might do more harm than good. There is some evidence that wearing a wearable monitor may reduce weight. Still, there is also worry that widespread usage of these devices and applications (the majority of which are unregulated) may result in misdiagnosis, over testing, or emotional discomfort.
Regulators should consider widening their scope to encompass more uses and devising a method for balancing long-term damage with short-term advantages. More has to be done to educate the general public, patients, legislators, and politicians on the value of medical research. If new medical research does not influence how doctors treat their patients, it is all for naught. To successfully deliver public health information, more individuals must use visual aids, consider social media, and engage with conventional media.
Medical research should concentrate on patient issues. Patient advocates, particularly those from poor and middle-income neighborhoods, should be included as co-investigators throughout the study.
The lack of diversity among trial participants, particularly among women and people of color, contributes to people’s distrust of clinical trial outcomes, particularly those from phase 1 research. Taking care of this is the right and ethical thing to do, and it will also aid in the fight against misinformation and the development of trust.