Background　and　objective:　Deep　learning,　a　novel　approach　and　subset　of　machine　learning,　has　drawn　a　growing　amount　of　attention　from　computer　vision　researchers　in　recent　years.　This　method　has　drawn　a　lot　of　interest　because　of　its　extraordinary　ability　to　interpret　medical　pictures,　especially　when　combined　with　residual　neural　networks,　which　have　helped　to　progress　the　field.　Methods:　In　this　paper,　the　following　research　is　carried　out　on　the　residual　network.　First,　the　research　status　of　ResNet　in　the　medical　field　is　introduced.　The　fundamental　idea　behind　the　residual　neural　network　is　then　explained,　along　with　the　residual　unit,　its　many　structures,　and　the　network　architecture.　Second,　four　aspects　of　the　widespread　use　of　residual　neural　networks　in　medical　image　processing　are　discussed:　lung　tumor,　diagnosis　of　skin　diseases,　diagnosis　of　breast　diseases,　and　diagnosis　of　diseases　of　the　brain.　Finally,　the　main　issues　and　ResNet＇s　future　development　in　the　area　of　processing　medical　images　are　discussed.　Results:　In　the　area　of　medical　graph　processing,　residual　neural　networks　have　made　strides　and　have　had　success　in　the　clinical　auxiliary　diagnosis　of　serious　illnesses　such　as　lung　tumors,　breast　cancer,　skin　conditions,　and　cardiovascular　and　cerebrovascular　diseases.　Conclusion:　We　thoroughly　sorted　out　the　most　recent　developments　in　residual　neural　network　research　and　their　use　in　medical　image　processing,　which　serves　as　a　crucial　point　of　reference　for　this　field　of　study.　It　offers　a　helpful　reference　for　further　promoting　the　application　and　research　of　the　ResNet　model　in　the　field　of　medical　image　processing　by　summarising　the　application　status　and　issues　of　the　ResNet　model　in　the　field　of　medical　image　processing　and　putting　forwards　some　future　development　directions.&　COPY;　2023　Elsevier　B.V.　All　rights　reserved.