๐๐จ๐ซ๐ค๐ฌ๐ก๐จ๐ฉ ๐๐๐๐๐ฉ: ๐๐, ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ง๐ ๐๐๐๐ฉ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐ข๐ง ๐๐๐๐ฅ๐ญ๐ก๐๐๐ซ๐ ๐๐ง๐ ๐๐๐๐ข๐๐๐ฅ ๐๐๐ฏ๐ข๐๐๐ฌ.
Recently, Dr Samira Gholizadehย ran a workshop at UCT MedTech on ๐๐, ๐๐ข๐ค๐ฉ๐ช๐ฏ๐ฆ ๐๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐๐ฆ๐ฆ๐ฑ ๐๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ช๐ฏ ๐ฉ๐ฆ๐ข๐ญ๐ต๐ฉ๐ค๐ข๐ณ๐ฆ ๐ข๐ฏ๐ฅ ๐ฎ๐ฆ๐ฅ๐ช๐ค๐ข๐ญ ๐ฅ๐ฆ๐ท๐ช๐ค๐ฆ๐ด.
The workshop covers how AI, Machine Learning and Deep Learning are being applied in healthcare and medical devices. It walks through the core technologies, the algorithms behind them, and where each one works best, from detecting disease in medical images to monitoring patients in real time.
The central argument is that AI is most useful when matched to the right clinical task, and that it works alongside clinicians rather than in place of them. If your data is
#biased, incomplete, or wrong, you get systems making multi-step decisions based on potentially flawed data. This is where
#data_integrityย becomes critical.
๐๐๐๐ฃ๐๐จ ๐๐ง๐ค๐ข ๐ฉ๐๐ ๐ง๐ค๐ค๐ข:
โข ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ๐ trained on millions of patient records to predict outcomes, flag deteriorating patients, and surface patterns invisible to the human eye.
โข ๐๐ฒ๐ฒ๐ฝ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐บ๐ผ๐ฑ๐ฒ๐น๐ย that analyse medical images, X-rays, MRIs, and pathology slides, with accuracy rivalling board-certified specialists.
โข ๐ก๐ฎ๐๐๐ฟ๐ฎ๐น ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด ๐ง๐ผ๐ผ๐น๐ that extract structured clinical insights from unstructured physician notes, discharge summaries, and patient-reported data.
โข ๐ฅ๐ผ๐ฏ๐ผ๐๐ถ๐ฐ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐ ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป AI-driven automation that handles scheduling, billing, prior authorisations, and documentation โ dramatically reducing administrative burden.
The algorithm you choose determines the outcome.
Thank you, Dr Gholizadeh, for the insightful session.
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