NeuRevolution: AI’s Healing Touch in Medicine
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In an era marked by unprecedented advancements in technology, artificial intelligence (AI) has emerged as a transformative force across different aspects of life. Specifically, the significance of AI in medicine lies in its capacity to process vast amounts of medical data with remarkable speed and precision, leading to more accurate diagnoses, personalized treatment plans, and enhanced patient care.
A new AI development involves a microchip implant in the brain that allows the paralyzed to regain movement and feeling. This method is a part of the double neural bypass approach, which forms a bridge of communication between one’s body and brain. The bypass sets a foundation to facilitate a therapeutic approach known as “thought-driven therapy.” Thought-driven therapy is based on the idea that an individual’s thoughts can be harnessed to trigger specific responses within the body. For instance, when an individual thinks about squeezing their hand, the brain sends signals to the microchips. In this case, the AI component of the system deciphers these commands and translates them into signals that can be understood by a microchip implant. This implant then generates appropriate response signals that can influence bodily functions.
This technology was first applied when Keith Thomas, a 41-year-old wealth manager, became a quadriplegic in a 2020 diving accident. He injured his C4 and C5 vertebrae, the primary members of the mid-cervical spine, losing all ability to move and sense his surroundings from the chest down. In March 2023, Thomas participated in a first-of-its-kind clinical trial led by researcher and bioengineer Chad Bouton at Northwell Health’s Feinstein Institutes for Medical Research, where they implanted microchips in Thomas’s brain.
Through this approach, they were able to use AI to rebuild connections throughout Thomas’s body, allowing him to gradually regain control of it. The goal was to place five fragile microchips in the regions of Thomas’s brain responsible for moving his hand and controlling his fingertips. The procedure consisted of a 15-hour open-brain mapping surgery at North Shore University Hospital. In order to confirm that the microchips were placed in the right spots, neurosurgeon Dr. Ashesh Mehta awakened Thomas mid-surgery and stimulated the appropriate areas of the brain.
Then came the thought-driven therapy. Based on Thomas’s intended movements, the implanted microchips transmitted electrical signals from Thomas’s brain to an amplifier on his skull. The amplifier then passed the signals to a computer through an HDMI cable. This allowed the AI algorithm to decode the messages and send signals to the microchips in Thomas’s brain, simulating touch and movement in his muscles. Immediately, Thomas felt his fingers for the first time in years.
Moving along a similar path of AI-related medical breakthroughs, Elon Musk's Neuralink project is another remarkable brain-computer interface. This initiative enables paralyzed individuals to control devices using their brain activity alone. Musk describes the technology as a “Fitbit in your skull with tiny wires that go to your brain,” a testament to the flawless integration of technology into human biology. Through the aid of surgical robots, a chip is carefully inserted, allowing the brain to control the connected devices.
Another medical application of AI is its role in predicting the outcomes of cancer research initiatives. Ankh LLM (large pre-trained language model) and CancerGPT analyze vast amounts of data to forecast potential outcomes and optimize treatment strategies. They predict the impact of different drug and protein combinations on tissues found in cancer patients. Even with minimal to no samples, both models perform with significant accuracy. This capability holds the promise of accelerating medical breakthroughs and streamlining the path to more effective therapies.
As we continue to transition into a world where technology pushes the boundaries of human capability, a mixture of awe and skepticism results. While the developments hold tremendous promise, there remain uncertainties due to concerns about data security and the potential for inaccuracies. For instance, training AI systems requires data from a wide variety of sources, including health, pharmacy, insurance, and lab records. Many patients may be concerned that the collection of such data is a privacy violation, as it must go through human AI developers as well. In addition, since AI systems learn from the data they receive, they tend to treat patient cases with less available data less effectively, leading to beliefs of bias and inequality. Therefore, in embracing the possibilities of advancing technology, it is important to maintain a fine balance between harnessing a potential for progress and addressing apprehensions.