Episode 160: Artificial Intelligence in Primary Care.
Future Dr. Manophinives explains the present and future of AI in diagnosing and treating diseases.
Written by Rosalynn Manophinives, MS-IV, American University of the Caribbean. Editing by Hector Arreaza, MD.
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Today, we embark on an intriguing journey at the crossroads of technology and healthcare: The Future of Healthcare in Artificial Intelligence (AI) and Machine Learning (ML). Let’s start by establishing the groundwork for AI and ML. Artificial Intelligence involves machines mirroring cognitive functions like learning and problem-solving, while machine learning empowers machines to learn from data and refine their capabilities over time. In healthcare, these technologies aim to elevate diagnostic precision and treatment effectiveness which are pivotal aspects in primary care medicine.
Accurate diagnosis is the cornerstone of effective patient care in all forms of medicine because an accurate diagnosis guides treatment decisions and influences patient outcomes. This is why the integration of AI and ML holds immense promise in this field.
Section 1: AI in Diagnostic Assistance (4 mins)
Let’s explore how AI utilizes algorithms to analyze extensive datasets, enhancing diagnostic accuracy significantly.
AI serves as a revolutionary force in analyzing a large amount of data, particularly in medical imaging. Imagine AI algorithms as super brains, employing machine learning to decipher intricate details from X-rays, MRIs, and CT scans. Notably, studies have demonstrated their precision matching and even surpassing that of human experts. For instance, research published in the Journal of the American Medical Association revealed AI algorithms outperforming radiologists in detecting conditions like breast cancer.
AI's skills extend beyond images. It digs into genetic information, medical history, and treatment outcomes, acting as a detective to spot patterns, predict responses, and customize interventions. Studies support this, showcasing AI models outperforming dermatologists in diagnosing skin cancer from images.
Will AI replace doctors?
The beauty of AI is that it does not replace doctors but acts as a super investigator in your healthcare corner, expediting diagnoses, and refining treatments. So, AI isn’t merely accelerating processes; it’s enhancing healthcare outcomes, making diagnoses quicker, and treatments more precise, and minimizing errors. The future appears very promising with AI leading the way to more precise and tailored healthcare.
Section 2: Case Studies in Diagnosis (4 mins):
Help in research: Let’s delve into real-life examples of AI in action, further amplifying diagnostic accuracy. In a research study, Rajkomar and collaborators crafted an AI algorithm predicting patient deterioration within hours, leveraging electronic health record data. This tool allowed for proactive care, identifying potential issues before they escalated. Taking it up a notch, Aliper and collaborators compared AI to human researchers, resulting in AI outsmarting human brains in designing drugs targeting age-related diseases. These experiments underscore AI's potential in diagnostics, from catching issues early to designing groundbreaking drugs.
AI here enhances doctors' capabilities and acts as an additional set of eyes, boosting their superpowers, spotting nuances, and proposing game-changing solutions in medicine.
Section 3: AI in Risk Prediction (4 mins):
Let’s shift our focus to AI's role in predicting risks and prognosis, particularly in conditions like COPD.
AI employs sophisticated algorithms to analyze patient data comprehensively, including demographics, hospital visits, diagnoses, prescribed medications, and lab results. In COPD, AI not only predicts mortality but also anticipates hospital readmissions for respiratory issues or flare-ups. By scrutinizing various markers, AI resembles Sherlock Holmes, unraveling clues within data.
And AI doesn’t stop there, AI integrates risk predictions into medical practices, which fosters personalized care tailored to individual risk factors. A study led by Choi and their team analyzed retrospective patient data and they were able to identify individuals at risk of undiagnosed COPD, emphasizing the significance of catching potential issues early, finding those who might slip through the cracks otherwise, which is huge!
Section 4: AI in Treatment Planning (4 mins):
Let’s now explore how AI is revolutionizing treatment planning within medicine.
AI, equipped with machine learning algorithms, tailors treatments by analyzing patient-specific data and medical history. In cancer, for example, AI analyzes biopsy images and quantifies biomarkers, facilitating personalized treatments. Beyond cancer, AI extends its reach to cell therapies, predicting their effectiveness through genomic information and drug responses.
And here's the techie part: AI employs various smart algorithms like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to provide personalized treatment recommendations. It’s like having personalized treatment recommendations by experts that fit you like a glove, catering to individual needs.
Section 5: Fuzzy Cognitive Maps and Reduction of Medical Errors (4 mins):
Lastly, let me tell you about the impact of AI-driven treatment planning, specifically in reducing medical errors. Imagine this—medical decisions? They're tough. Sifting through tons of data, inaccessible medical records, physicians' lack of experience, and loads of conflicting info, makes the decision often not crystal clear. This is where a high percentage of medical errors occur, which is where Fuzzy Cognitive Maps (FCMs) come in. FCMs are like a super-smart tool that mimics human reasoning, tackling the messiness of medical data with grace.
FCMs are all about modeling complex systems, by combining fuzzy logic and neural networks, just like our brain does—connecting the dots between concepts and their cause-and-effect relationships. From patient records to test results, they make sense of it all.
And FCM is not just theory—FCMs are the real deal and they're not the newbies in town; they've been around for a while, evolving from their early days. They've proven their worth in various medical areas too – in radiotherapy planning, diagnosing language impairments, and even in grading tumors!
So, in a nutshell, FCMs are useful tools for medical decision support by taking on the complexities of diagnosing and treatment planning.
Closing:
In conclusion, the integration of Artificial Intelligence and Machine Learning in healthcare is a thrilling frontier, offering invaluable tools to enhance diagnostic accuracy and patient outcomes. As we evolve, responsible use of these advances is paramount, ensuring they optimize rather than replace the indispensable human touch in healthcare.
Thank you for joining me in exploring the future of healthcare in AI and Machine Learning. I trust this discussion has sparked curiosity and appreciation for the transformative potential of technology in healthcare.
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Conclusion: Now we conclude episode number 160, “Artificial Intelligence in Primary Care.” This is a new and somewhat unknown field of medicine that is rapidly evolving these days. Future Dr. Manophinives explained that AI and ML can be a useful tool in the diagnosis of diseases by, for example, interpreting images accurately. AI also can help develop plans of care by interpreting large amounts of complex data and predicting trends, possible complications, and the effectiveness of multiple treatments. Keep your eyes and mind wide open to learn more about this advancing technology that will continue to support our efforts to bring health and well-being to our communities.
This week we thank Hector Arreaza and Rosalynn Manophinives. Audio editing by Adrianne Silva.
Even without trying, every night you go to bed a little wiser. Thanks for listening to Rio Bravo qWeek Podcast. We want to hear from you, send us an email at RioBravoqWeek@clinicasierravista.org, or visit our website riobravofmrp.org/qweek. See you next week!
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References:
- Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;375(13):1216-1219. doi:10.1056/NEJMp1606181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070532/.
- Rajkomar, Alvin, et al. "Scalable and accurate deep learning with electronic health records." npj Digital Medicine, 08 May 2018. https://www.nature.com/articles/s41746-018-0029-1
- Choi, Ellen, et al. "Retrospective analysis of real-world data to identify patients at risk for undiagnosed chronic obstructive pulmonary disease." PLoS ONE, 2020.
- Choi, Ellen, et al. "Machine Learning in Primary Care: Predicting Hospitalizations and Critical Events." AMIA Annual Symposium Proceedings, 2018.
- Beam AL, Kohane IS. Translating Artificial Intelligence Into Clinical Care. JAMA. 2016;316(22):2368-2369. doi:10.1001/jama.2016.17217. https://pubmed.ncbi.nlm.nih.gov/27898974/
- Johnson, Kipp W., et al. "Automated Fuzzy Cognitive Maps Generation for Supporting Clinical Decisions in Primary Care." IEEE Transactions on Fuzzy Systems, 2020.
- Royalty-free music used for this episode: Gushito, “Gista Mista”, downloaded on November 16th, 2023, from https://www.videvo.net/
