Revolutionizing Healthcare: How AI is Making Practitioner Diagnoses and Treatment More Effective
In my post Transforming Healthcare with AI: A Perspective on the Latest Developments in Medical AI Tools I spoke about the ways that artificial intelligence (AI) is being used. In this post, I delve into the effectiveness of artificial intelligence tools in the diagnosis and treatment of patients. While some clinical studies of AI tools show mixed or negligible results, the following research highlights the success of other solutions.
AI tools increase diagnostic accuracy and lower radiologist workload
A study in Sweden evaluated the effectiveness of the mammography AI tool Transpara in evaluating 39,996 image sets. The rate of cancer detection was 20% higher using the tool versus double-readings by radiologists alone. Moreover, the AI tool reduced the screen reading workload by 36,886, a 44.3% drop.
This has significant implications. By reducing the radiologist reading workload by almost half, the tool can liberate practitioners to devote more time to patients with detected cancers. A reduced workload would also help mitigate a shortage of qualified radiologists. And fewer readings would result in fewer false positives.
Other AI tools have shown promising results as well. For example, the AI solution provider, Enlitic, has created “a deep learning algorithm that can increase the accuracy of a radiologist’s interpretation by 50-70% and at a speed 50,000 times faster.” The significant increase in interpretation speed has impressive consequences for provider’s radiologist teams, considering that their average salary is $341,066.
Overall, faster time-to-treatment is good for patients, but especially so for stroke victims. This was shown in a UK study that evaluated the performance of the stroke-triage AI tool called AUTOStroke. The solution quickly automates the diagnosis of a stroke “in 30 seconds, compared to a 30-minute scan to manual-reporting timeframe…” This helps physicians deliver essential care within the critical 90-minute time window to treat stroke victims. Speedier treatment correlates with reducing the amount of brain damage a stroke patient suffers.
AI tools helping to create customized treatment plans
AI tools are showing promise in aiding practitioners in the treatment of their patients. For example, uMETHOD has rolled out an AI service to physicians to create a treatment plan for their Alzheimer’s patients. The AI completes an exchange of relevant biometric information between Quest Diagnostics and the practitioner’s electronic health record system. One study showed that “80% of patients showed improved memory function scores or held steady...”
In conclusion, the effectiveness of AI tools in the diagnosis and treatment of patients has been significant. As AI technologies continue to evolve, we can expect to witness further enhancements in patient outcomes. However, it is important to note that AI tools are not a replacement for human expertise in healthcare, but rather an aid to practitioners. Therefore, adequate training and collaboration between human healthcare providers and AI developers is needed to ensure that patients receive the best possible care.