Over the past few years, the healthcare sector has been increasingly digitising, which benefits both the patients and the medics. Artificial intelligence has had a great impact in this sector, from diagnosing different problems more accurately to monitoring patients remotely and improving the clinical workflow.
The 10 Biggest Trends Revolutionizing Healthcare In 2024 (Forbes, 2023)
AI’s Impact on Diagnostics
Thanks to the data-oriented approach, medical systems have begun to obtain value from AI. In 2023, scientists in the Netherlands have discovered a way to use AI to diagnose brain tumours on the operating table. This solution provides surgeons with information regarding the subtype of the tumours and how aggressively they need to operate them (Mueller, B., 2023). Another way that AI has been utilised was in helping doctors diagnose breast cancer more accurately on mammograms. To do that, researchers trained computers to identify cancer using mammograms from approximately 76,000 women in Britain and 15,000 in the United States whose diagnoses were already known. On scans from the United States, the system’s application led to a 9.4% reduction in false negatives, where a mammogram is misinterpreted as normal. Furthermore, the results showed a 5.75% decrease in false positives, where the scan is interpreted as abnormal but there is no cancer (Grady, D., 2020).
Remote Monitoring: Bringing doctors and patients closer
Monitoring of patients can be quite hard at times and it can aslo demand a lot of time, space, and money. However, the digitisation of hospitals has eased this responsibility and brought the patients closer to doctors. This initiative has made a positive change in the hospital stays and aggravated symptoms. The tools that are frequently used are wearable devices such as wristbands or skin patches that feature software components that incorporate data analytics functionalities so that healthcare providers can monitor patient data from anywhere (Martin, S., 2023). This initiative is efficient from a financial and logistical standpoint since it expands the hospital’s reach while freeing up beds for those who need in-person care.
How AI is Easing the Paperwork Burden
In addition to extensive hospital hours and heartbreaking situations, excessive paperwork is another load that contributes to doctors’ burnout. Digital paperwork takes a lot of their time, as it involves typing lengthy notes into electronic medical records, required for treatment, billing, and administrative purposes. However, one of the latest AI tools has begun to summarise, organise, and tag the conversations between doctors and patients. Abridge is a company that was founded in 2018 and it offers an alternative to the overload of documents by using artificial intelligence to record and generate summaries of patient visits. Apart from giving doctors more time on their hands, the patients’ experience has improved thanks to this software. Studies reveal that 80% of patients forget what they have discussed in the consultation, so the AI-generated summary of the visit can be used as a reminder to take medications and follow procedures recommended by the doctor (Lohr, S., 2023).
The future of healthcare
By embracing these state of the art solutions, hospitals are becoming more efficient and are providing higher-quality, personalised services to their patients. AI is currently enabling medical professionals to provide unmatched, personalised care while optimising their workflows. In the future, I believe it will play an even bigger role in preventing the development of some diseases before they pose a threat to people’s lives and recommending the right treatments at the right time. There are endless possibilities, and we are only starting to see what artificial intelligence is capable of achieving in medicine.
References
Grady, D. (2020, January 1). A.I. Is Learning to Read Mammograms. The New York Times. https://www.nytimes.com/2020/01/01/health/breast-cancer-mammogram-artificial-intelligence.html
Lohr, S. (2023, June 26). A.I. May Someday Work Medical Miracles. For Now, It Helps Do Paperwork. The New York Times. https://www.nytimes.com/2023/06/26/technology/ai-health-care-documentation.html
Marr, B. (2023, October 3). The 10 Biggest Trends Revolutionizing Healthcare In 2024. Forbes. https://www.forbes.com/sites/bernardmarr/2023/10/03/the-10-biggest-trends-revolutionizing-healthcare-in-2024/
Martin, S. (2023, March 16). Three Ways Digitalization Is Powering The Future Of Healthcare. Forbes. https://www.forbes.com/councils/forbestechcouncil/2023/03/16/three-ways-digitalization-is-powering-the-future-of-healthcare/
Mueller, B. (2023, October 11). New A.I. Tool Diagnoses Brain Tumors on the Operating Table. The New York Times. https://www.nytimes.com/2023/10/11/health/ai-tumor-diagnosis-brain-cancer.html
This post is very interesting and covers two topics of high importance in the present: AI and Healthcare. I find it even more captivating since for my bachelor thesis I had to develop an AI algorithm that detects cancer based on blood samples taken from patients, and thus there is a correlation between what I did and the topic of your blog post.
AI has indeed improved and provided many new opportunities for the medical sector, which you emphasise with strong facts and clear numbers. Indeed, training an AI with a lot of patient data to predict a diagnosis lifts a big burden from the doctors’ shoulders, but it is still not perfect. A possible issue at the moment with AI predictions in health-related diagnostics is that the AI comes up with most of its outcomes from a mathematical or data analysis point of view, which does not necessarily make sense from a medical point of view. At least, this was one of the biggest issues for me during my thesis research, and I had to seek medical expert input to validate the obtained results. Nonetheless, I do believe that the future of healthcare involves the integration of AI into the healthcare systems and this will improve it a lot, but in the present, the AI techniques to interact with medical data and make predictions are not perfect, and they still need validation from a human expert before concluding the patients’ diagnostics.