Artificial intelligence in healthcare: opportunities and challenges | Navid Toosi Saidy | TEDxQUT
Artificial intelligence in healthcare: opportunities and challenges | Navid Toosi Saidy | TEDxQUT
Artificial intelligence has the ability to revolutionise and personalise targeted healthcare for individual patients. The regulatory frameworks for AI in healthcare are a critical component in managing and maximising accurate healthcare predictions.
Navid holds a PhD in Biomedical Engineering and Medical Device Development. He has previously…
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Nice explanation sir, for how AI helps save the million of lifes around the world…
We don't need AI in our life!😒
Bro no offense, but these comments
seem like they came from IA
Amazing information, Looking forward to the future.
Your dedication to quality content is amazing.
❤❤❤❤❤ Ai is features is very good
jioi
Ai is very good
This video is truly inspiring! Looking forward to your next content! 🌟
It seems final development in medical science and humens.
Amazing infromation dr. Navid
Looking frowad to the futurr.👍
AI in healthcare presents both exciting opportunities and significant challenges. On the opportunity side, AI has the potential to revolutionize diagnostics by analyzing medical images, genetic data, and patient records with incredible precision, enabling earlier detection of diseases such as cancer, heart disease, and neurological disorders. AI can also enhance treatment plans by predicting outcomes and personalizing therapies based on individual patient data, improving both the quality and efficiency of care. Additionally, AI can help streamline administrative tasks, reduce healthcare costs, and enable more accessible care through telemedicine and virtual assistants. However, there are notable challenges to overcome. One of the biggest concerns is data privacy and security, as AI systems require vast amounts of personal health data to function effectively. There’s also the issue of algorithmic bias, where AI models trained on unrepresentative data may produce skewed results, leading to unequal care. The integration of AI into healthcare requires robust regulation to ensure safety, transparency, and ethical use. Furthermore, there is a need to train healthcare professionals to work alongside AI tools effectively, ensuring that technology enhances rather than replaces human expertise. Balancing these opportunities and challenges will be key to realizing the full potential of AI in transforming healthcare for the better.
AI detected the small cancer cell that can cause him cancer after 5 year in future. that the success we can use AI in good and educational way not like deepfake
I'm impressed by how AI is being used in healthcare. It's helping to diagnose diseases and improve patient outcomes.
Amazing information Dr. Navid. Looking forward to the future.
Two years later and what has been said in this Ted Talk is still true on a good trajectory. Many considerations and conversations have come into play.
There are a bunch of very longwinded tl;dr comments here, by channels with no avatar, content or "About" info, written in the same kind of tone. Wouldn't be surprised if it's bots or the same person., or AI.
AI is not having the feelings as it is not having empathy, emotions, therapeutic touch as the human beings….
Sounds great. Now if only people could afford healthcare
doctors are corrupt these days, they would only use your service if you pay them to use it.
what about some rare instances where patients were told they were gonna die soon but didnt bcz of their willpower etc? does AI include – a patients will and hopes to live? their zeal for life…
AI and healthcare i guess we shall start implement something, this company has already invented an AI health scan. tg and x BitDoctorAI
While implementation and use of a new technology can be scary, the potential for greater streamlined success is far more vast than any hesitation or fear of using Ai. With a strong and unbiased foundation, along with flexible and stringent monitoring, we could enter a new era of healthcare.
Actually I want to join for this project especially to increase the health rate of cancer patient with minimize the duration of diagnosis process
The depiction of AI in popular culture has often been one of dystopian futures, where machines rise against humanity. However, as the speaker rightly points out, the reality is far from this portrayal. AI has the potential to revolutionize healthcare, offering personalized care, streamlining hospital operations, and providing accurate decision-making tools. The example of AI's role in cancer diagnosis and treatment is particularly poignant. By consolidating data from various sources, AI can provide accurate predictions about a patient's diagnosis, treatment options, and prognosis. This is a game-changer, especially for patients like Peter, who, without AI's intervention, might have faced a grim prognosis.
However, the journey of integrating AI into healthcare is not without its challenges. One of the most significant hurdles is the existing regulatory framework, which is not designed to accommodate the dynamic nature of AI. Traditional software is static, producing the same output for the same data. In contrast, AI has the intrinsic ability to learn and evolve, making it more adaptable and, ideally, more intelligent over time. Locking the learning potential of AI models, as the current regulatory approach suggests, limits their potential and can even be detrimental to patient care.
Furthermore, the issue of data bias is critical. If AI models are trained predominantly on data from one demographic, their accuracy and reliability can diminish for other demographics. It's essential for AI developers to ensure their models are trained on diverse datasets. However, as the speaker mentions, this isn't always feasible due to the availability of data. Therefore, building a functionality where AI models can acknowledge their limitations and uncertainties is crucial.
In conclusion, the potential of AI in healthcare is immense. However, to harness this potential fully, we need to address the challenges head-on. This involves establishing new regulatory frameworks in collaboration with AI developers, healthcare practitioners, policy advisers, and patients. By doing so, we can ensure that AI serves the entire population equally, leading to a future where healthcare is more personalized, efficient, and effective.
Long overdue. Use AI to predict and medication dosage and meds (adhd e.g.) by past response.
Whatsapp then the patient simply exports the log as a txt and a LLM does analysis and etc just use GPT4 and so on (dont even have to fine-tune but it might help) as long it lerns and can be reused or benefitial for future models
I'm writing a persuasive on the benefits of using AI in healthcare. This was very helpful in looking at the benefits and idea of how helpful using an AI can be in healthcare. It's not entirely offsetting the role of decision making from physicians it's a great tool.
Artificial intelligence has some amazing potential benefits in the health care field, with potential efficiency improvements for hospitals, assisting and guiding physicians in patient treatment regimens, as well as the greatest potential of diagnosing a patient. Dr. Navid Saidy discussed some very important complications to consider related to introducing artificial intelligence into the medical practice. Including regulations for medical devices that typically involves a physical device. Yet in the case of artificial intelligence it is a software that evolves and does not involve a static repetitive outcome. Nevertheless if artificial intelligence purpose is to diagnose, give treatment options and prognosis, it’s output has numerous outcomes which can be hard to quantify and therefore hard to regulate. Even as stated in the video the regulations change to allow for more transparency and real time monitoring, there still are risks. One of the main concerns about artificial intelligence is that the data used to create its program is biased. Since humans are the ones collecting the data, and have interpreted the data with some implicit assumptions that are then incorporated in to the system, this bias is then transferred to the artificial intelligence models and can lead to biased resorts in diagnosis/treatment. This is why it is so vital that when assessing these new technologies that the results are accurate. This leads me to a important topic to consider with the implementation of artificial intelligence; that of beneficence. Beneficence is the act of doing good by benefiting the patient more than doing harm. Artificial intelligence has a great potential capacity to reach a current diagnosis with more efficiency which could greatly benefit patients in time sensitive care. However, in the cases where the wrong diagnosis is stated, with confidence by artificial intelligence this could lead to greater harm to the patient. The capacity for AI to state when the answer is unknown and if more testing is needed, is crucial to the application of this technology. These drawbacks need to be critically supervised as artificial intelligence is incorporated into medicine. It is naïve to say that artificial intelligence won’t be a part of medicine in the future. All the same, we need to be careful and diligent in assessing the technology and outcomes for patients. It is important to remember, that part of healing comes from a healing touch and emotional and spiritual connectivity of humans. As technologies become more and more integrated in our society, we must prioritize and preserve our humanity.
I do believe that the actual stretch to which AI can help the healthcare system may be taken too far.
I can understand the importance of using AI to consolidate data, having large amounts of information ready to go when needing to refer to treatment options and such. I can see how this saves time and resources and saves us from error at times. I can also understand the importance of wanting to be as efficient as possible with many situations in medicine. But how well does AI understand the risks and benefits of each patient? How well does AI truly follow beneficence for each individual patient? AI can’t necessarily understand the emotional or mental toll certain treatments can have on a patient outside of typically stated adverse reactions.
A major problem with this is when the patient does not follow the standard of care, when the patient does not respond the way many others have to treatments, procedures, medicine etc. Dr. Saidy states that AI can even learn from these patients who did not follow the treatment and can help come up with following steps. But this is all still algorithm backed up by some data. Do we know if that data is recent? Do we know if it follows a trend and is generalizable to other places? Do we know who took this data? This can all be questions we as healthcare professionals need to think about.
Think about a medication change – we could easily train a robot to know DDIs and which medications can be mixed with one another but what happens when a patient has an allergic reaction to a new medication and needs to replace it with something else? Further yet what if that medication used to replace the one that caused an allergic reaction would require two medication changes if a new medicine were to not work will all their existing meds? Here is where we may end up spending more money or time than we thought we saved with AI. And we could have solved the allergy and or reactions faster if a human doctor was around to supervise, or think to grab a LFT’s or genetic screening for patients with different metabolizing abilities. When we have to pick up the pieces AI left because of the critical thinking, we are taking two steps back. We have to preserve beneficence, and all the though process and considerations that surrounds what is doing best for the patient.
I will say however, there are great ways to use AI, and there should be more information on specific uses such as using it t for locating the primary site of cancer. I think there is a balance between allowing AI to take over an entire patient vs allowing AI to aid us in information we cannot see or feel with the human site or touch. But when we consider places such as an ER that decisions need to be made quickly, is there a potential for doctors to rely on this information too much since they need to work quickly on their feet? Lastly, Dr. Saidy is aware of data bias and how that could skew the information depending on a patient’s information. I believe if we want to do what is best for the patient however, these tools to ensure bias does not occur are extremely important, and manufacturers should consider perfecting these tools prior to using AI on patients and potentially having the AI misdiagnose. In the case of misdiagnosis in particular, AI could potentially lead to breaking the code of non-maleficence. If a patient was misdiagnosed, chances are their treatment is incorrect for their diagnosis. In which case, we could be causing harm to the patient without knowing it. This is where again, AI needs to be used as a backup tool not the lead tool.